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The Internet of Vehicles (IoV), a subset of the Internet of Things (IoT) within the transportation sector, enhances driving safety and comfort by utilizing intelligent transportation systems to facilitate communication among vehicles and other entities. In this context, communication extends beyond vehicles and roadside units (RSUs), supporting interactions between vehicles, mobile devices, sensors, and infrastructure. Ensuring the security of vehicular communications is essential in the IoV paradigm. Recently, Limbasiya et al. proposed secure and efficient communication protocols for IoV, claiming their protocols are resilient against various known attacks. However, our paper identifies significant vulnerabilities in Limbasiya et al.’s protocols, including susceptibility to identity guessing, impersonation, password guessing, and man-in-the-middle attacks. Furthermore, these protocols lack confidentiality, unlinkability, and the ability for the IoV server to trace malicious vehicle users when necessary. To address these drawbacks, we introduce improved protocols for secure communications within the IoV framework. Afterwards, we assess the security of our proposals through both informal and formal analyses, employing the RoR model, BAN logic, the ProVerif, Scyther, and Tamarin tools.

Electronic Medical Images (EMI) have grown with the increase in population. EMI are used for medical diagnosis for medical emergencies hence, they should be correct and clear for accurate diagnosis. In general, EMI are of varying sizes and dimensions. The aim is to enhance the security and privacy of EMI with reduced computational time while dealing with a larger and varying-sized data set. Reducing computational time will make the method suitable for real-time applications (RTA). Hence, a data set of 5856 secret X-ray images all varying in dimensions, total sized up to 1.16 GB, are applied with a hybrid of steganography and cryptography. Here, one X-Ray image is taken at a time then hidden into a single cover image using Edge-based steganography and then encrypted using five layers of cryptography. Various performance evaluation tests such as Structural Similarity Index Metrics (SSIM) achieved a value close to 1 which is the preferred value, Peak Signal-to-Noise Ratio (PSNR) is 82.51967 dB which is good, Mean Square Error (MSE) is 5.6E-09 which is close to zero indicating no addition of noise in the retrieved X-ray images, the Correlation (R) is 1. Therefore, the extracted image is the same as the original X-ray images, while remaining tests such as RMSE, Entropy, KLD, BER, SNR, CV, MAPE, PRD, etc. achieved good results. The computational time is measured by the Encryption Time (ET) is 0.37 seconds while the Decryption Time (DT) is 3.9275 sec. Thus, it can be concluded that a hybrid method (HM) could be implemented for RTA.

It is now essential to ensure security because of the enormous volumes of data being exchanged. Particularly, images are very vulnerable to encryption flaws. Therefore, it is imperative to provide robust security mechanisms for image data. Conventional image encryption methods secure the privacy of necessary information by converting it into noise-like, encrypted ciphertext images that completely obscure visual content. However, the main drawback of this method is that it almost eliminates visual usability in the cloud. Thumbnail-preserving encryption (TPE) was created to solve this problem by retaining the image’s thumbnail appearance even after encryption, providing a compromise between privacy and usability. Aspiring researchers should use this review to understand TPE schemes thoroughly. In this survey, the objectives, benefits, and drawbacks of several methods are examined with an emphasis on image privacy and usability. Notable TPE applications and evaluation metrics are also highlighted in the review. These metrics are grouped into categories such as quality, encryption key, security, and resilience to different kinds of attacks. In addition, a characterization of TPE schemes is provided, along with a comparison and summary of each scheme’s contributions. In addition, we list the open research questions and difficulties that must be resolved to guarantee secure TPE for real-time applications.

Recent events dedicated to e-healthcare systems demonstrated that a critical mass of users using e-health services and applications are being created and producing a tremendous output, such as improved patient monitoring, faster diagnosis, and better disease management. However, despite the benefits and availability of e-health systems, widespread adoption in real-life medical or health situations still needs to be improved. Therefore, implementing a four-party authentication system can help address concerns about security and privacy and encourage patients to adopt electronic healthcare technologies. This article presents a security protocol for securely authenticating four participants in the e-healthcare system, namely LSP–eHS. The scheme uses a simple cryptographic hash function, concatenation, and xor operations. The security of the proposed protocol can be confirmed through a widely used BAN (Burrows–Abadi–Needham) logic, and the performance analysis of the proposed protocol can be finished by measuring communication and computation costs.

The revolutionary technologies behind Industry 4.0 have opened a new era for manufacturing: connected and autonomous machines, collaborative robotics, and monitoring techniques are spreading to increase productivity and sustainability. From the workers’ perspective, they bring new safety threats but also opportunities to solve old ones, while concerns about workers’ privacy arise due to the increase of data sensed and transferred from the shop floor. This paper presents the results of a research project addressing the prediction of dangerous conditions through workplace monitoring with privacy guarantees. This work is driven by a realistic approach starting from the fact that it is entirely centered on a real 14-meter production line equipped with an extensive array of top-tier devices, including robotic arms, autonomous mobile robots, a reconfigurable moving belt, a multi-camera system, and a highly efficient data transport and computation infrastructure. This project shows safety and privacy achievements over six representative use cases such as man-on-the-ground, environmental events (e.g., fire incidents), workers’ errors that can lead to potential accidents, compliance of Personal Protective Equipment (PPE), and gatherings restrictions. The benefits of this study extend to stakeholders such as manufacturers and workers offering safety systems that can be deployed in industrial settings while addressing privacy concerns and providing compliance with regulations. The industrial laboratory at the heart of this study represents with realism a dynamic and interconnected Industry 4.0 and 5.0 environment.

The adoption of electronic health (e-health) systems has transformed healthcare delivery by harnessing digital technologies to enhance patient care, optimize operations, and improve health outcomes. This paper provides a comprehensive overview of the current state of e-health systems, tracing their evolution from traditional paper-based records to advanced Electronic Health Record Systems(EHRs) and examining the diverse components and applications that support healthcare providers and patients. A key focus is on the emerging trends in AI-driven cybersecurity for e-health, which are essential for protecting sensitive health data. AI’s capabilities in continuous monitoring, advanced pattern recognition, real-time threat response, predictive analytics, and scalability fundamentally change the security landscape of e-health systems. The paper discusses how AI strengthens data security through techniques like anomaly detection, automated countermeasures, and adaptive learning algorithms, enhancing the efficiency and accuracy of threat detection and response. Furthermore, the paper delves into future directions and research opportunities in AI-driven cybersecurity for e-health. These include the development of advanced threat detection systems that adapt through continuous learning, quantum-resistant encryption to safeguard against future threats, and privacy-preserving AI techniques that protect patient confidentiality while ensuring data remains useful for analysis. The importance of automating regulatory compliance, securing data interoperability via blockchain, and prioritizing ethical AI development are also highlighted as critical research areas. By emphasizing innovative security solutions, collaborative efforts, ongoing research, and ethical practices, the e-health sector can build resilient and secure healthcare infrastructures, ultimately enhancing patient care and health outcomes.

The wide use of social media raises numerous privacy concerns, with limited studies of the Middle East and North Africa (MENA) region. This study presents an in-depth analysis of social media privacy concerns in sixteen MENA countries, a timely and important topic in an under-studied region. A census-representative sample (N = 8140) was collected using an online panel survey from Algeria (n = 551), Bahrain (n = 453), Egypt (n = 522), Iraq (n = 526), Jordan (n = 580), Kuwait (n = 459), Lebanon (n = 485), Libya (n = 486), Morocco (n = 530), Oman (n = 471), Palestine (n = 486), Qatar (n = 489), Saudi Arabia (n = 521), Tunisia (n = 574), United Arab Emirates (n = 479), and Yemen (n = 528). Results show multi-level privacy differences in the Gulf, Levant, and North Africa regions, among countries in a region, and within a country based on gender, age, educational level, and resident status (expat vs. national). Results show the concerns and attitudes towards social media privacy in the MENA region, identifying factors contributing to these perceptions with implications for developing platforms and policies to address the unique privacy concerns of social media users in the MENA region.

Big Data Cybersecurity Analytics (BDCA) is a helpful tool for cybersecurity maintenance that may support the identification of potential threats. Data preparation for traditional BDCA environments contains several steps comprising data movement, transformation, aggregation, and processing. All these steps take place before data becomes accessible to users. Executing such a workflow may take a reasonable time, which increases significantly with the growing amount and variety of available data sources for analytic operations. As the elapsed time between the actual occurrence of cybersecurity events and data availability for analytical queries grows, BDCA’s usefulness decreases. In this work, we deal with near real-time BDCA. We propose DBD-Guardian, a system that runs distributed queries over cybersecurity data sources (e.g., log files) while stored in their original location. DBD-Guardian supports querying heterogeneous unstructured and semi-structured sources by using specialized parsers. Also, as data sources are in their raw format, DBD-Guardian has a component specially designed to deal with sensitive data, providing access to anonymized data. To evaluate our proposals, we prototyped DBD-Guardian and implemented a representative scenario of a small company with several hosts and log files of different types. We also simulated several malicious operations in this scenario and assessed the DBD-Guardian ability to support intrusion identification and enforce privacy protection. We evaluated analytic operations’ response time as well. The results proved our solution efficiently supports analytical operations and threat identification and also demonstrated the solution’s adaptability to distributed and heterogeneous environments.

In most parts of the world, minors’ access to education is guaranteed, resulting in most of their time being spent in the school building. Similarly, school staff spend a relatively long time in the same building. This unique setting requires both groups to stay inside the school walls throughout the working days, which might negatively affect their well-being. Studies show that low indoor air quality might negatively affect well-being. Moreover, school staff and students mention other problems, such as worrisome declining students’ attendance, motivation, social relations, safety, stress, and pressure, among others. Technological solutions, such as IoT-based systems, might be one way to tackle the well-being issue at schools. However, most of the existing solutions focus on the quality of the indoor environment, disregarding the needs and pains of school inhabitants. Further, some research focused on IoT in households, social housing, or universities. Yet, school inhabitants’ requirements may differ, considering higher density of occupancy, cognitive tasks that school inhabitants continuously perform, and the fact that school students are minors who are still in the process of physical and mental development. In the current article, we present an investigation of how IoT-based solutions could extend beyond solely environment-measuring tools and fulfill the needs of school inhabitants. Through an interview study with eleven IoT experts guided by user (school inhabitants) requirements identified in the previous research, we assess functional and non-functional system requirements that such systems should build on. These are discussed against the five-layer IoT architecture model. Moreover, the study identifies barriers and benefits of IoT-based solutions, which are discussed in the context of existing scientific and legal frameworks. The exploratory findings presented in the paper could be used as a guide for decision-makers at schools to ensure that the students and staff’s well-being is maintained, as well as by IoT suppliers to produce holistic or compatible solutions that could be easily integrated into the unique contexts of school.

In blockchain-based systems usually there are two common concerns: one of them is privacy and other is key management. Besides privacy has been widely studied, nor key management has been done so deeply, moreover, the conjunction of both neither has be addressed. In this work we present a threshold blind signature scheme designed on the modified version of the Paillier cryptosystem presented by Yi et al. Our scheme guarantees four main characteristics of interest: Independence meaning that our scheme could be applied to any blockchain-based transaction system, allowing to adapt it to different application scenarios, i.e. electronic voting or founds administration; secure key storage which improves the key management security by recovering the whole signature instead of the private key and avoiding to have one single point of failure. In addition, we also provide anonymity as one of the best-evaluated features on online transactions. Finally, we achieve independence from mixers or trusted third parties, which most of the time are used to reach anonymity with inherent risks. All of these characteristics are achieved without increase computational and communication cost in comparison with related work.

With the rise of the Internet of Things (IoT), social networks, and mobile devices, vast amounts of mobility data are continuously generated. These data encompass diverse location information from various sources, including smart vehicles, sensors, wearables, and social media platforms. By leveraging these data, we explore the semantic enrichment of trajectory components related to moving objects and locations, bringing the so-called multiple-aspects trajectories and relative privacy issues. Privacy risk analysis is crucial for the earlier detection of privacy problems, particularly when dealing with semantically enriched trajectories. In this study, we introduced the TrajectGuard privacy risk assessment framework. TrajectGuard, an extension of PRUDEnce, achieved significant results by formulating and assessing the privacy risk of multiple-aspects trajectories under several proposed attacks. The framework introduced a nuanced risk evaluation using AspectGuard and conducted fair privacy assessments on anonymized datasets using AnonimoGuard. Its adaptability and versatility make TrajectGuard a valuable tool for preserving data privacy with multiple-aspects.

The Internet of Things (IoT) ecosystem presents substantial challenges in terms of privacy and security, rendering it an attractive target for malicious actors. In this context, the literature review highlights the ongoing difficulty in addressing privacy and security through a unified mechanism owing to the heterogeneous nature of IoT devices, their dynamic behavior, and the continual advancement of intelligent hacking tools. Hence, encoding techniques have been considered from the perspective of privacy in Artificial Intelligence (AI) models. To overcome these challenges, this study introduces an integrated single mechanism for privacy and security in the IoT. In order to safeguard sensitive data during AI model training, a novel privacy mechanism called Replacement Encoding (RE) is proposed. This mechanism ensures the camouflage of sensitive information while preserving the integrity and utility of trained models. Additionally, this approach provides automated preprocessing, enhancing the performance of AI models. Message packet features were derived, extracted, and analyzed from the CICIoT2023 dataset (PCAP files) using Wireshark. The proposed replacement encoding scheme is integrated with AI classifiers to detect attacks, achieving an accuracy of 88.94% and 86.61% for the Random Forest (RF) and Deep Neural Network (DNN) models, respectively, utilizing 100 features. These results are compared to accuracies of 90.16% and 94.81% for the same models with up to 15 features using genetic algorithm-based correlation features. Finally, the proposed RF mechanism demonstrates its utility across multiple domains, including privacy preservation, automated data preprocessing, and protection of sensitive user data in Generative Pre-Training Transformer (GPT) applications as well as AI models.

The current healthcare infrastructure faces significant challenges, including security vulnerabilities, privacy breaches, data inconsistencies, and overly accessible health records. These issues highlight the urgent need for comprehensive reforms in information management and patient data protection. To address the current issues, the present work proposes RSHealth framework, an e-healthcare ring signature scheme. It is a promising solution for ensuring anti-tampering of transaction fie and identity anonymization of sender healthcare stakeholder during transmission from main healthcare community to a branch healthcare community, maintaining compliance with data privacy regulation. The RSHealth scheme includes five functions: TFSetup(), TFKeygen(), TFRing(), TFVerify(), and TFOpen(). These functions respectively initialize system parameters, generate keys, create ring signatures, verify authenticity, and reveal signer identities by authorized receiver healthcare stakeholders. Extensive experimentation is performed for aforementioned framework to judge the latency and throughput; varying sender healthcare stakeholder and sizes of transaction. The performance analysis shows that increasing sender healthcare stakeholder and transaction sizes leads to increased throughput and decreased latency.

The widespread integration of on-demand services founded on proprietary algorithms into various software applications has ushered into a new era of advanced service capabilities. However, using these services entails disclosing information by the customer, not only during the payment process but also when using the service, where certain personal information must be shared to obtain a more personalized service. This practice potentially exposes users to increased security risks in case of data security breaches. In this paper, we introduce a novel framework aimed at enhancing client privacy and ensuring service integrity within the context of computing services that rely on proprietary algorithms. A blockchain-based approach is proposed to enhance user privacy throughout service provision, encompassing both the payment process and the verification of the provided service. Our proposal leverages properties of distributed ledger networks to improve user privacy during payment transactions and incorporates a verification system using zero-knowledge proofs on blockchain to validate the integrity of the contracted service. Finally, we analyze the privacy, overhead, and performance aspects of the framework, employing custom proprietary algorithms. We illustrate this through examples of Convolutional Neural Networks with multiple layers, undisclosed to the client. This emphasizes the potential benefits of its applicability for both service providers and clients.

This paper proposes a paradigm that combines edge computing and blockchain technology in the healthcare field, providing a groundbreaking method to handle, analyze, and safeguard medical data. The method effectively decreases the time delay, addresses problems related to data transmission capacity, and improves the protection of sensitive patient data by processing it locally. The blockchain network is essential to the system since it offers a secure and unchangeable record for storing processed data. The integration of this novel system guarantees that patient data, once recorded, is unable to be modified in a way that affects previous entries, thereby preserving the integrity of the data. This approach prominently incorporates the innovative Proof of Trust and Expertise (PoTE) consensus process, which utilizes the reliability and specialized knowledge of users in the network. This approach not only guarantees the secure and efficient validation of transactions, but also conforms to the essential demands of managing healthcare data. The proposed model provides efficient environment that is scalable and reliable with average block processing time of 0.25 sec, transactional throughput of 140 transactions per second (tps) and a success rate of 97% while operating over larger number of nodes in the network. The incorporation of edge computing and blockchain in healthcare is positioned to establish a novel benchmark in the management, processing, and security of medical data, consequently augmenting the overall caliber of healthcare services.

Efficient and reliable management of the business operations within the food supply chain is crucial to ensure the quality, safety, traceability, and security of food products. Streamlining different food supply chain management operations by utilizing cutting-edge technologies such as Cloud computing and the Internet of Things (IoT) has gained enough attention in recent years. Most of the systems developed for streamlining food supply chain management operations are centralized and offer limited traceability, reliability, transparency, immutability, provenance, and consumer trust in food products. Furthermore, such systems are prone to single-point failure, as well as data tampering and alteration. Blockchain is a decentralized technology that follows a distributed peer-to-peer (P2P) architecture for storing and processing data. It offers several benefits to participants of the food supply chain in terms of improved data provenance, transparency, traceability, security, and immutability. This article discusses blockchain technology’s potential role and opportunities in transforming food supply chain processes. It identifies and discusses key requirements of food supply chain participants that blockchain technology must meet to efficiently manage the business processes involved in the food supply chain. It explores case studies and research projects in the food sector that are based on blockchain technology, aiming to highlight the applicability of blockchain in the food supply chain. Finally, it identifies and discusses open research issues and challenges in the food industry.

Content-Centric Networking (CCN) aims to meet the demand for content access and distribution by securing content rather than connections. However, content cached in CCN routers is vulnerable to malicious attacks and unauthorized access. Existing authentication and access control protocols often suffer from issues such as single points of failure and inadequate protection against sophisticated attacks. To address these security and integrity concerns, we propose a blockchain-based authentication protocol using soulbound tokens (SBTs). The protocol links the consumer to a secure crypto wallet, serving as a repository for authentication information. The content provider mints and distributes SBTs via blockchain, ensuring proof-of-authenticity and ownership. This blockchain-based approach eliminates single points of failure and provides a decentralized, tamper-proof method for authentication. Smart contracts for the protocol, developed in Solidity and deployed on a zk-EVM enabled blockchain, provide privacy-preserving authentication. Security analysis using AVISPA confirms the protocol’s robustness against various CCN threats, including denial-of-service and unauthorized access. Performance evaluation demonstrates the protocol’s computational efficiency and feasibility for CCN environments, maintaining low latency and resource usage while enhancing security.

Healthcare services and organizations rely on Electronic Health Records (EHRs) to manage, store, and transmit patient data records. Consequently, EHRs play a crucial role in providing high-quality services and maintaining the privacy and security of patients’ sensitive data. However, designing such complex systems with security and privacy concerns is anything but simple. This study aims to propose a Software Reference Architecture (SRA) tailored for Electronic Health Records (EHRs) with security and privacy considerations, intending to enhance the development of these systems. To achieve this goal, we analyze the classification of Reference Architectures (RAs), taking into account the primary security and privacy requirements of EHRs along with well-established architectural design methods. We propose a layered architecture for SRA with privacy and security considerations. Subsequently, we derive the following five architecture views for SRA: the feature diagram, the context diagram, the decomposition view, the layered view, and the deployment view. Each view showcases the SRA software architecture from a different perspective. Moreover, we conducted an evaluation of the proposed SRA through its application in a case study. Specifically, we applied the proposed SRA and derived the application architecture from a study focused on Brazilian EHRs. Our analysis highlights the potential issues arising from the absence of an SRA tailored for EHRs, particularly regarding privacy and security concerns surrounding patient data. Through this case study, we demonstrate the practical applicability of our proposed SRA in enhancing EHR systems.

In the era of 6G, given the emergence of heterogenous devices, diverse network scenarios, along with future deployment of cutting-edge applications that require high computation ability, how to establish trust in such complex network environment has become a demanding task. Current trust establishment process mostly relies on certificates issued by certificate authority (CA) or reputation derived from interaction and behavior history. However, these approaches may not be applicable to 6G scenarios due to several reasons, including but not limited to insufficient adaptability, lack of openness and transparency, and limited scalability. Hence, this article mainly proposes a novel approach, which is based on a claim-attestation-evidence framework and leverages both blockchain and artificial intelligence (AI) to build trust in highly open and dynamic future networks. Meanwhile, we also provide an overview on current trust establishment models, discuss about the advantages of our proposed model and suggest certain parts of our current design for improvements that can be considered by future research.

Wireless communication is now a cornerstone of modern society, propelled by the widespread adoption of IoT devices and sophisticated wireless technologies. As wireless networks grow in complexity, there is an increasing need for efficient scheduling algorithms that can manage resources and adapt to evolving conditions effectively. Link scheduling is critical as it optimizes the use of wireless spectrum and bandwidth, prioritizing high-priority transmissions and minimizing interference. However, traditional link scheduling algorithms have primarily focused on performance, often overlooking the crucial aspect of privacy. This oversight poses significant risks in scenarios where privacy is paramount. To address this issue, we introduce PriLink, a novel link scheduling algorithm that prioritizes privacy without sacrificing performance. PriLink employs a least privilege model, sharing only essential links to protect critical topology details from potential adversaries. Our comprehensive evaluation shows that PriLink not only matches but occasionally surpasses the performance of established benchmarks like Greedy Maximal Scheduling and Local Greedy Scheduling. Moreover, it offers faster execution times and superior privacy protection. These results highlight PriLink’s effectiveness as a robust solution for efficient, real-time, and privacy-preserving link scheduling in dynamic wireless networks.

Blockchain technology has fascinated and significantly sparked research activities and industries due to its transparency in transactions and data sharing. The striking proliferation of blockchain technology is used to confront the challenges associated with trust, transparency, security, centralization, supply chain traceability, and regulatory compliance. The promising technology has the potential to enable several boons across diverse fields like healthcare, supply chain management, manufacturing, cross-border payments, finance, and energy trading. Telemedicine primarily aims to facilitate the transmission of healthcare data through electronic channels, enabling users to access medical services. It supports healthcare services around the globe, aids in early diagnosis and treatment, and assists with remote care by provisioning effective healthcare that is safe, secure, and reliable. However, there are challenges associated with Personal Health Records (PHR) due to lack of ownership of the data, accuracy, reliability, and data transaction security. The architecture presented in this work enables us to benefit from telemedicine and securely share PHR. The proposed model offers a permissioned network-based Hyperledger Fabric blockchain framework for secure PHR sharing amongst healthcare providers. The Byzantine Fault Tolerance consensus mechanism protects patient privacy, and the IPFS protocol is used to store the data off-chain. Additionally, the smart contract is utilized for providing patients with granular access control over PHR data. Hyperledger Caliper is used as a benchmarking tool to test this technology and determine the average latency rate for viewing and updating the PHR by healthcare providers along with analysis of CPU and memory utilization. As a result, our goals in telemedicine to improve secure sharing and give the patient access control are achieved.

Crowdsourcing has emerged as a pivotal data source for diverse smart city applications, ranging from health and traffic to security and safety. However, the integration of users’ location data in crowdsourced information poses a significant privacy challenge. Current privacy protection approaches of location-based services have become inadequate to face the evolving attackers’ techniques and tools. Moreover, these protection methods ignored the issue of preserving the accuracy and reliability of data. This paper introduces a novel approach, termed Double Cloak Area (DCL-Ar), designed to effectively safeguard users’ location privacy and ensure the reliability of data based on crowdsourcing. DCL-Ar differentiates by offering dual-layer protection for identity. The first layer involves users creating an initial cloak zone, while the second layer utilizes fog nodes to establish an extended cloak zone. Furthermore, the proposed method introduces three distinct scenarios for managing collaboration among fog nodes to select the optimal anonymizer and address the limitations of existing protection methods which are related to saving the reliability and the accuracy of data. DCL-Ar maintains maximum entropy, achieving complete uncertainty about user locations, thereby ensuring a high level of privacy protection. Through simulation and comparative analysis, the efficacy of the proposed approach is demonstrated where it provides a superior privacy level without significant performance. Experimental results demonstrate that DCL-Ar outperforms traditional methods, improving cache hit ratios and response times while reducing server query loads. Specifically, our approach reduces the number of queries sent to the service provider (SP) by up to 50% compared to existing methods and maintains a high cache hit ratio of nearly 100% over time. It further impacts on the traditional cloak-area and other protection approaches.

Consent management has become an important issue with the increased usage of the Internet and also smart devices that collect personal data. Each country enacts its regulations and laws for consent management. These laws ensure that personal data is not collected without the individual’s consent and cannot be processed with a purpose other than the stated purpose. The General Data Protection Regulation (GDPR) has strict rules regarding collecting and processing personal data. This paper proposes a new approach for auditable hybrid consent management systems using blockchain technology and a purpose tree. The suggested approach includes (1) the implementation of a GDPR-compliant consent management system using blockchain and purpose tree; (2) the implementation of an audit mechanism that detects consent violations and corrects consents; and (3) the use of both on-chain and off-chain technologies. The audit mechanism proposed in this paper detects possible violations by performing inspections on every transaction in the system. Besides, it immediately informs the data subject and the competent authorities regarding the relevant violations. As part of this study, a prototype of the architecture is developed as a proof of concept to evaluate the performance of critical components. The obtained experimental results show that the proposed hybrid architecture that use purpose tree effectively supports consent sharing between the parties.

Vehicular fog computing (VFC) is a developing concept that utilizes the ideas of fog computing to facilitate immediate communication and cooperative decision-making among vehicles. However, guaranteeing safe authentication in VFC presents notable difficulties as a result of characteristics such as dynamic network topology, extensive mobility, and limitations on resources. This paper introduces D-BlockAuth, an innovative authentication mechanism for 5G-assisted VFC that utilizes a dual blockchain approach. To reach the most vehicles, this link makes use of all the features of the fifth-generation base station (5G-BS). D-BlockAuth employs two blockchains: a permissioned blockchain to handle long-term identities and a consortium blockchain to enable streamlined and effective authentication at the fog layer. The D-BlockAuth concept incorporates advanced cryptographic techniques such as ring signatures and group signatures, which provide improved privacy and anonymity for vehicles within the network. The study provides a comprehensive description of the architecture of D-BlockAuth, examines its security characteristics, and assesses its performance using simulations. The results indicate that D-BlockAuth successfully performs both efficient and safe authentication in VFC, while also maintaining user privacy.

The growth of data publishing, sharing, and mining mechanisms in various fields of industry and science has led to an increase in the flow of data, making it an important asset that needs to be protected and managed effectively. To this end, different mechanisms have been used across different domains, including Privacy Enhancing Technologies like Synthetic Data Generation, which aim to protect user-sensitive data and prevent misuse among different domains. Then, Synthetic data has been used not only to augment datasets and balance classes but also in applications of data analysis paradigms that aim to provide useful insights in terms of utility while preserving the privacy of sensitive data. Still, there is a gap in the conceptual and state-of-the-art understanding of the level of privacy synthetic data generators can provide and how they affect various industries and fields. This systematic review attempts to address how privacy has been assessed and measured in the framework of synthetic data generation, and getting to know which metrics have been used to evaluate those mechanisms. We provide an overview with a total of 105 recent studies in this field after a screening process and identify future open research directions. The main findings include a high prevalence of differential privacy as a privacy-preserving technique and privacy budget cost as a trade-off metric, with a high percentage of GAN-based model implementations, and mainly healthcare applications. Our systematic review covers multiple privacy domains and can be understood as a general framework for privacy measurement applied in Synthetic Data Generation.

Cloud-based deployments face increasing threats from various types of attacks, necessitating robust anomaly detection frameworks to safeguard against potential security breaches. Existing solutions, such as RSSI, GTM, and APG, though effective to a certain extent, exhibit limitations in terms of precision, accuracy, and scalability. To address these shortcomings, this paper proposes a novel anomaly detection framework that integrates multimodal feature analysis, deep learning models, and QoS-aware sidechains to enhance the prediction accuracy of cloud attacks and optimize blockchain-based cloud installations. By maximizing feature variance across different sample types and leveraging advanced deep learning techniques, the proposed approach significantly outperforms conventional methods in terms of precision, accuracy, recall, and AUC performance. Furthermore, the framework demonstrates superior efficiency in block mining delay, energy consumption, and throughput, making it highly suitable for real-time cloud attack prediction scenarios. The proposed methodology represents a significant advancement in anomaly detection and cloud security, offering a comprehensive solution for addressing challenges in blockchain-based cloud deployments. Thus, the proposed anomaly detection framework employs both Deep Learning and Blockchain technologies. Using Recurrent Neural Networks (RNN) with Convolutional Neural Networks (CNN), the system examines system logs and identifies unusual behavior patterns associated with different attacks. Using Blockchain technology, the framework ensures the transparency and integrity of system logs, and Deep Learning models provide precise and timely anomaly detection. The decision to combine Deep Learning and Blockchain technology is justified by the merits of each technique. The distributed, immutable ledger provided by blockchain technology makes it impossible to tamper with system logs and ensures the accuracy of anomaly detection. While, deep learning models, have exceptional pattern recognition abilities and can adapt to changing attack methods, resulting in high precision, accuracy, recall, and AUC metrics. Analyses of experimental data demonstrate that the proposed framework is effective. The framework achieves impressive performance metrics, such as low delays, 98.5% precision, 99.4% accuracy, 98.3% recall, and 99.2% Area Under the Curve (AUC).

The cultural shift of work from on-premises to remote home offices allows hackers to access corporate data by compromising devices attached to home-based broadband routers. Currently, home devices are growing at an exponential rate, e.g., in the form of smartphones, Internet of Things (IoT) devices, and corporate laptops. Presently, there is a lack of cost-effective, practical, and lightweight Unified Threat Management (UTM) systems designed to protect home users from the wide range of existing cyber threats. This applied research article introduces a sophisticated Smart UTM System (SUTMS) that is designed to tackle the contemporary security issues encountered in home networks while running on a Raspberry Pi single-board computer. The proposed integrated SUTMS design consists of flow detection, intrusion detection, and firewall core engines, as well as optional routing and log collection engines. The flow detection engine in SUTMS discovers anomalies and detects the active protocols, which are a basis for signature optimization in the SUTMS intrusion detection system (IDS). By ingesting Indicator of Compromise (IoC) feeds, the SUTMS firewall engine provides dynamic anti-bot protection. Evaluations indicate that SUTMS with IDS signature optimization can provide 99% accuracy with approx. 55% memory utilization reduction compared to traditional signature-based IDS.

Intrusion Detection Systems (IDS) tackle the challenging task of detecting network attacks as fast as possible. As this is getting more complex in modern enterprise networks, Artificial Intelligence (AI) and Machine Learning (ML) have gained substantial popularity in research. However, their adoption into real-world IDS solutions remains poor. Academic research often overlooks the interconnection of users and technical aspects. This leads to less explainable AI/ML models that hinder trust among AI/ML non-experts. Additionally, research often neglects secondary concerns such as usability and privacy. If IDS approaches conflict with current regulations or if administrators cannot deal with attacks more effectively, enterprises will not adopt the IDS in practice. To identify those problems systematically, our literature survey takes a user-centric approach; we examine IDS research from the perspective of stakeholders by applying the concept of personas. Further, we investigate multiple factors limiting the adoption of AI/ML in security and suggest technical, non-technical, and user-related considerations to enhance the adoption in practice. Our key contributions are threefold. (i) We derive personas from realistic enterprise scenarios, (ii) we provide a set of relevant hypotheses in the form of a review template, and (iii), based on our reviews, we derive design guidelines for practical implementations. To the best of our knowledge, this is the first paper that analyzes practical adoption barriers of AI/ML-based intrusion detection solutions concerning appropriateness of data, reproducibility, explainability, practicability, usability, and privacy. Our guidelines may help researchers to holistically evaluate their AI/ML-based IDS approaches to increase practical adoption.

The increasing adoption of intelligent Electric Vehicles $(EVs)$ in the realm of transportation has raised significant concerns pertaining to security aspects within the Vehicle-to-Grid $(V2G)$ communication system. This includes issues related to authentication, integrity, confidentiality, privacy, and the effective tracking of EVs. While numerous researchers have proposed solutions to address these security challenges, the existing schemes often exhibit a considerable demand for computational and communication resources. This renders them impractical for resource-limited $V2G$ setups operating within the Internet-of-Things $(IoT)$ framework. To address these limitations, this paper introduces an energy-efficient mutual and batch authentication scheme tailored specifically for $V2G$ communication systems within the IoT paradigm. Through a meticulous security and performance analysis, our proposed scheme demonstrates its proficiency in providing essential security features, including robust authentication and privacy safeguards, while significantly minimizing both computational and communication complexity. The outcomes of our analysis affirm that the proposed approach is well-suited for the unique constraints of IoT-based $V2G$ communication systems, offering a balanced and resource-efficient solution to enhance overall security and performance.

Nowadays, passwords play an important role in ensuring practical security. Password strength meters (PSMs) are typical and well-known tools developed to help users estimate password strength. Among existing PSMs, Markov-based PSMs offer high accuracy and reliability. However, these PSMs are often compared with other types of PSMs, and no study has compared their accuracy in detail. Two types of Markov models introduced in previous studies are the Simple Markov Model (SMM) and the Layered Markov Model (LMM). When calculating the probability of a password, these models consider two factors: character position and password length. However, these two models do not cover all possible cases of these two factors. In this paper, we introduce the application of two new Markov models, which can be seen as “hybrid models” of SMM and LMM, into PSMs, called Simple Markov Model with password length consideration (SMMl) and unique Layered Markov Model (uLMM), to cover all the missing cases. Then, we present two specific scenarios and compare the effectiveness of four types of Markov models in evaluating passwords based on these two scenarios. The experimental results indicate that the SMMl model, one of the two models proposed in this paper, yields the best effectiveness. This result also suggests that the number of samples of sub-string sequences obtained during the training process affects the effectiveness of password evaluation. Therefore, we conduct a detailed analysis related to this issue. These results will support us in developing new PSMs in the future.

In many special cases, the data collected from wireless sensor networks are stored in encrypted form to provide the required privacy. Sorting is an essential operation on any stored data for orderly presentation and fast searching. In the case of cloud-stored data, sorting of the data can be delegated to the cloud server, employing suitable homomorphic encryption that supports sorting. This paper presents a new homomorphic sorting algorithm based on the Hardy-Littlewood-Polya rearrangement inequality. The associated homomorphic encryption scheme is accomplished using integer matrix keys generated based on Hermite Normal Form transformation. This work uses the homomorphic sort support encryption algorithm to securely sort the wireless sensor data stored in the cloud. Subsequently, the computation of the corresponding descriptive statistical values is securely outsourced to the Cloud Server. Our scheme saves the homomorphic sort execution time by about 30% compared to its nearest competing method.

The development of smart sensors and appliances can provide a lot of services. Nevertheless, the act of aggregating data containing sensitive information related to privacy in a single location poses significant issues. Such information can be misused by a malicious attacker. Also, some previous studies attempted to apply privacy mechanisms, but they decreased data utility. In this paper, we propose privacy protection mechanisms to preserve privacy-sensitive sensor data generated in a smart home. We leverage Rényi differential privacy (RDP) to preserve privacy. However, the preliminary result showed that using only RDP still significantly decreases the utility of data. Thus, a novel scheme called feature merging anonymization (FMA) is proposed to preserve privacy while maintaining data utility by merging feature dataframes of the same activities from other homes. Also, the expected trade-off is defined so that data utility should be greater than the privacy preserved. To evaluate the proposed techniques, we define privacy preservation and data utility as inverse accuracy of person identification (PI) and accuracy of activity recognition (AR), respectively. We trained the AR and PI models for two cases with and without FMA, using 2 smart-home open datasets i.e. the HIS and Toyota dataset. As a result, we could lower the accuracy of PI in the HIS and Toyota dataset to 73.85% and 41.18% with FMA respectively compared to 100% without FMA, while maintaining the accuracy of AR at 94.62% and 87.3% with FMA compared to 98.58% and 89.28% without FMA in the HIS and Toyota dataset, respectively. Another experiment was conducted to explore the feasibility of implementing FMA in a local server by partially merging frames of the original activity with frames of other activities at different merging ratios. The results show that the local server can still satisfy the expected trade-off at some ratios.

The Internet of Things (IoT) emerged as a pervasive technology, facilitating the seamless interaction of devices, individuals, and services, enabling data exchange and task execution across various domains. While the impact of IoT is undeniably transformative, its extensive proliferation raised significant concerns surrounding security, privacy, and trust, which stand as critical barriers to the widespread adoption and advancement of IoT technology. This review article explores IoT security, privacy, and trust research using a 3-layer IoT architecture. After introducing the fundamental tenets of IoT security, privacy, and trust, it proceeds to examine the prevalent security requirements within IoT architectures and their associated challenges. Then, the survey investigates the recent trends in research dedicated to addressing security, privacy, and trust issues within IoT systems. Furthermore, this article reviews the latest advancements and methodologies designed to secure IoT systems against security breaches and protect the privacy of sensitive data. Finally, the survey outlines unresolved challenges within the IoT security landscape and potential solutions. By offering this consolidated insight, this article offers a bridge between foundational and advanced IoT security topics, providing researchers with an in-depth understanding of current IoT security, privacy, and trust challenges, as well as cutting-edge solutions tailored to address the security and trust-related obstacles faced by IoT applications. In addition, it provides the IoT community with the knowledge necessary to navigate the complex terrain of security, privacy, and trust in IoT systems.

Cloud computing is a fast-growing industry that offers various online services, including software, computing resources, and databases. Its payment model is usage-based, whereas consistency is based on resource-sharing. Cloud storage is popular among individuals and businesses because it reduces cost, increases productivity, boosts performance, and improves security. However, cloud computing comes with security risks as data are stored with third-party providers, and Internet access limits visibility and control. Effective data security and data protection are key issues compared with traditional on-premise computing. There are several methods for ensuring data security in the cloud, of which cryptography is the most important. Cryptography offers a range of security features including authentication, confidentiality, integrity, and availability. However, a thorough examination of the different cryptography methods in a single study is lacking. This study comprehensively examined different cryptography methods, including deoxyribose nucleic acid (DNA), elliptic curve, homomorphic, hybrid, lightweight, and novel methods. The analysis addresses their methodology, algorithms, results, applications, and limitations and provides valuable suggestions for data security in the cloud. This paper proposes the use of elliptic curve cryptography (ECC) to ensure safe communication and lightweight cryptography for Internet of Things (IoT) devices with limited resources. This emphasizes the benefit of combining asymmetric security with symmetric efficiency in hybrid cryptography.

The infrastructureless architecture for Internet-of-Drones (IoD) environment regulates drones in airspace for performing tactical tasks. Synergy is crucial for IoD participants; otherwise, complex task completion is impossible because IoD is a newer area that has gained more attention for domestic and commercial usage. However, wireless communication in the IoD environment is prone to numerous threats; security and privacy are among the top challenges. If all the participating entities of IoD become securely authenticated, then information broadcasting will never be confronted by a strong adversary. Therefore, in this research article, we have designed a robust and lightweight security mechanism based on a fuzzy extractor and the MD5 (Message Digest 5) method to authenticate all IoD participants and ensure secure communication. The security analysis of the proposed biometric-based authentication mechanism has been formally verified through a simulation toolkit ProVerif and Random-Oracle Model (ROM) and informally through propositions. The performance metrics of the proposed protocol have been measured by considering communication and computation costs. The result obtained from the security and performance analysis sections shows that the secrecy, reachability, and confidentiality of the session secret key are, of course, ensuring secure information communication for the IoD environment. When comparing it with prior works, the result demonstrated that it can be strongly recommended for practical implementation in the IoD environment.

Along with the creation of medical profiles of patients, Electronic Health Records have several secondary missions, such as health economy and research. The recent, increasing adoption of a common standard, i.e., the Fast Healthcare Interoperability Resources (FHIR), makes it easier to exchange medical data among the several parties involved, for example, in an epidemiological research activity. However, this exchange process is hindered by regulatory frameworks due to privacy issues related to the presence of personal information, which allows patients to be identified directly (or indirectly) from their medical data. When properly used, de-identification techniques can provide crucial support in overcoming these problems. FHIR-DIET aims to bring flexibility and concreteness to the implementation of de-identification of health data, supporting many customised data-processing behaviours that can be easily configured and tailored to match specific use case requirements. Our solution enables faster and easier cooperation between legal and IT professionals to establish and implement de-identification rules. The performance evaluation demonstrates the viability of processing hundreds of FHIR patient information data per second using standard hardware. We believe FHIR-DIET can be a valuable tool to satisfy the current regulation requirements and help to create added-value for the secondary use of healthcare data.

Internet of Medical Things (IoMT) are a kind of Internet of Things (IoT) systems which are used in the healthcare domain. Nowadays, there are an abundance of wearable smart devices, either commercial or clinical, which can be used to collect vital signs and transmit the collected data to remote servers for further analysis. Remote patient monitoring, smart diagnostics, and autonomous control of chronic diseases are examples of different healthcare services that can be provided by these systems at a lower cost and higher efficiency compared to traditional healthcare settings. However, as data related to patients’ health status and treatment history, transmitted in these systems, are highly confidential and private, security and privacy concerns in their widespread adoption may arise. Deap Learning (DL) algorithms, with their ability in extracting knowledge from big data generated in these systems, can be leveraged to design smart security mechanisms. In this survey study, the recent literature on the DL-assisted security and privacy provisioning frameworks in IoMT systems are categorized and summarized with respect to their main contributions. Finally, some possible future directions are introduced to assist interested researchers to continue research in this domain.

The state-of-the-art face recognition systems are typically trained on a single computer, utilizing extensive image datasets collected from various users. Nevertheless, these datasets often contain sensitive personal information that users may hesitate to disclose. To address potential privacy concerns, we explore the application of federated learning, both with and without secure aggregators, in the context of both supervised and unsupervised face recognition systems. Federated learning facilitates the training of a shared model without necessitating the sharing of individual private data, achieving this by training models on decentralized edge devices housing the data. In our proposed system, each edge device independently trains its own model, which is subsequently transmitted either to a secure aggregator or directly to the central server. To introduce diverse data without the need for data transmission, we employ generative adversarial networks to generate imposter data at the edge. Following this, the secure aggregator or central server combines these individual models to construct a global model, which is then relayed back to the edge devices. Experimental findings based on the CelebA datasets reveal that employing federated learning in both supervised and unsupervised face recognition systems offers dual benefits. Firstly, it safeguards privacy since the original data remains on the edge devices. Secondly, the experimental results demonstrate that the aggregated model yields nearly identical performance compared to the individual models, particularly when the federated model does not utilize a secure aggregator. Hence, our results shed light on the practical challenges associated with privacy-preserving face image training, particularly in terms of the balance between privacy and accuracy.

Blockchain networks provide a reliable and secure mode of communication due to their decentralized and distributed nature. The emergence of amalgamated blockchain-based internet-of-things (IoT) systems has generated a huge amount of data to be online. Though blockchains show potential for ensuring transparency, traceability, and immutable records, the privacy of online data in blockchains becomes a question. The privacy of blockchain transactions is at stake as various privacy breaching methods are used by attackers such as linking the transactions, deanonymization, etc. However, the benefits of blockchain make the technology a dominator in the present and future technological paradigms. The other side of the coin deals with privacy information retrieval (PIR), which is necessary to retrieve private information from servers without much revealing. However, the conjunction of blockchain privacy and PIR is very critical and an important aspect of blockchain solutions. In this present survey, we pioneer in analyzing the privacy factors of existing blockchain solutions. We discuss the privacy parameters and important privacy enhancement techniques for blockchains comprehensively. We show the applicability of privacy in various domains including e-commerce, supply chain, healthcare, and IoT. We also discuss PIR-related issues and solutions in the existing literature. We highlight open research problems and discuss the benefits of collaborating with PIR and blockchain systems to improve privacy in blockchains. Our survey is beneficial for academia and industries to be aware of the present status of privacy solutions in blockchains and to address the identified loopholes to make the systems better.

The Internet of Things (IoT) infrastructure enables smart devices to learn, think, speak and perform. The facilities of the IoT devices can be enhanced to support an intelligent application through technologies like fog computing, smart networks, federated learning or explainable artificial intelligence infrastructures. In all these cases networking of IoT devices becomes inevitable. Whereever there exists a network, a threat to the network infrastructure is also possible. The proposed work classifies various attacks on the hosts with the support of proven machine learning (ML) algorithms. This work performs the comparative analysis of all these classification parameters of the machine learning algorithms with the use of fuzzy-based recommendation systems. This work also lists out various incidents of intrusions on the IoT hosts in appropriate layers of the interface and proposes an efficient algorithm and framework to overcome the occurrences of intrusions on the host side. In particular, we propose an effective security framework to deal with the intrusions that can deteriorate the host-based systems. The ranking of the algorithms is evaluated using fuzzy-based recommendation systems such as TOPSIS, VIKOR, MORA, WASPAS. The ensemble of machine learning algorithms such as Decision Tree, Lite Gradient Boost, Xtra Gradient Boost and Random Forest provide better values of accuracy (around 99%) with higher precision, re-call and F1-scores, thus proving their efficacy for intrusion detection in IoT networks.

In the past decade, the deployment of deep learning (Artificial Intelligence (AI)) methods has become pervasive across a spectrum of real-world applications, often in safety-critical contexts. This comprehensive research article rigorously investigates the ethical dimensions intricately linked to the rapid evolution of AI technologies, with a particular focus on the healthcare domain. Delving deeply, it explores a multitude of facets including transparency, adept data management, human oversight, educational imperatives, and international collaboration within the realm of AI advancement. Central to this article is the proposition of a conscientious AI framework, meticulously crafted to accentuate values of transparency, equity, answerability, and a human-centric orientation. The second contribution of the article is the in-depth and thorough discussion of the limitations inherent to AI systems. It astutely identifies potential biases and the intricate challenges of navigating multifaceted contexts. Lastly, the article unequivocally accentuates the pressing need for globally standardized AI ethics principles and frameworks. Simultaneously, it aptly illustrates the adaptability of the ethical framework proposed herein, positioned skillfully to surmount emergent challenges.

Internet of Things (IoT) plays an essential contribution in connecting devices and enabling seamless data exchange, leading to increased efficiency and convenience. However, security concerns in IoT systems are significant, as compromised devices can lead to data breaches and privacy violations. Blockchain technology can enhance IoT security by providing decentralized consensus, immutability, and transparent transaction records, ensuring secure and trustworthy communication and data integrity. This review article gives a succinct but thorough understanding of blockchain technology, covering architecture of blockchain, working principles, types, applications, platforms, and its role in the IoT environment. The study highlights potential benefits of blockchain like enhanced security and privacy, and explores its integration with IoT. Additionally, the study discusses various real-world applications, examines blockchain platforms, and addresses the limitations and challenges associated with blockchain technology. This review serves as a valuable resource for researchers and practitioners seeking a deeper understanding of blockchain’s potential and its implications in the IoT landscape.

The exponential growth of data in the information age poses several threats to the privacy and safety of digital service users. Existing legislation, such as the GDPR in Europe and the CCPA in California, defines frameworks and guidelines to promote personal privacy but leaves freedom in the choice of means to achieve privacy. Data anonymization techniques remove information that can be used to identify individuals from the dataset, either through suppression, generalization, anatomization, permutation, or perturbation. Information suppression remains the most common, safe, and widely applicable anonymization method, though at a high data utility cost. In this paper, we argue that even information suppression may not be sufficient in some cases. We study the case of a dataset that describes the shopping habits of a grocery store’s customers. All identifiers and quasi-identifiers are removed from the dataset by suppression. However, by augmenting the data in a novel multi-step, iterative process, and building a neural network enriched with prior knowledge, we show that most sensitive information can be retrieved with an accuracy of 80%.

In the rapidly evolving realm of the Industrial Internet of Things (IIoT), securing shop floor operations, especially in audit processes, is of critical importance. This paper confronts the challenge of ensuring data integrity and trust in IIoT systems by leveraging the capabilities of blockchain technology. The unique characteristics of blockchain, such as its immutable and decentralized ledger, establish a solid and transparent foundation for verifying shop floor transactions and activities. We introduce a privacy-centric approach, meticulously designed to comply with stringent data privacy regulations. This method allows auditors to authenticate both IIoT data and devices, ensuring confidentiality and adhering to regulatory standards. Our practical implementation strategy, tailored for shop floor environments, not only enhances the security of device and data integrity but also showcases robustness against specific adversarial threats, including network intrusion, data tampering, and unauthorized access. The findings indicate that our approach not only strengthens security protocols but also integrates effortlessly with existing IIoT infrastructures. It presents an efficient, scalable solution that elevates the safety and reliability of IIoT ecosystems, making it a significant step forward in the quest for secure and compliant industrial operations.

The Internet of Things (IoT) has revolutionized various domains, enabling interconnected devices to communicate and exchange data. The integration of Artificial Intelligence (AI) in IoT systems further enhances their capabilities and potential benefits. Unfortunately, in the era of AI, ensuring the privacy and security of the IoT faces novel and specific challenges. IoT security is imperative, necessitating comprehensive strategies, including comprehension of IoT security challenges, implementation of AI methodologies, adoption of resilient security frameworks, and handling of privacy and ethical concerns to construct dependable and secure IoT systems. It is vital to note that the term ‘security’ encompasses a more comprehensive view than cyberattacks alone. Therefore, with an emphasis on securing against cyberattacks, this comprehensive survey also includes physical security threats on the IoT. It investigates the complexities and solutions for IoT systems, placing particular emphasis on AI-based security techniques. The paper undertakes a categorization of the challenges associated with ensuring IoT security, investigates the utilization of AI in IoT security, presents security frameworks and strategies, underscores privacy and ethical considerations, and provides insights derived from practical case studies. Furthermore, the survey sheds light on emerging trends concerning IoT security in the AI era. This survey provides significant contributions to the understanding of establishing dependable and secure IoT systems through an exhaustive examination of the present condition of IoT security and the ramifications of AI on it.

Intrusion detection is essential for safeguarding computer systems and networks against unauthorized access, malicious activities, and security breaches. Its application domains include network security, information security, and cybersecurity across various sectors such as finance, healthcare, government, and industry. Federated learning-based intrusion detection offers improved performance compared to conventional mechanisms by leveraging decentralized data sources, preserving data privacy, and enhancing model generalization through collaboration among multiple organizations. However, challenges faced by existing federated learning-based intrusion detection mechanisms include ensuring data privacy and security, mitigating communication overhead, and enhancing detection accuracy. In order to overcome these issues, this research article proposes a federated learning-based intrusion detection methodology that leverages Enhanced Ghost_BiNet, a novel deep learning model, to enhance the security of information sharing and detection accuracy. Federated learning, a privacy-preserving machine learning technique, is utilized to enable multiple entities to collaboratively train a global intrusion detection model without sharing sensitive data. The proposed system first trains local models using Enhanced Ghost_BiNet, which integrates GhostNet and Bidirectional Gated Recurrent Unit (BiGRU). To optimize the model’s performance, the Chaotic Chebyshev Artificial Humming Bird (CAh) algorithm is employed. Homomorphic encryption is applied to encrypt the local model updates, enhancing data privacy and security. Server-side aggregation of updates and collaborative optimization are introduced to minimize communication rounds during data aggregation. The results demonstrate that the Enhanced Ghost_BiNet outperforms traditional models like GhostNet, BiGRU, RNN, Auto Encoder, and CNN in terms of accuracy, precision, recall, F-Score, and mean square error (MSE). For instance, the Enhanced Ghost_BiNet achieves an accuracy of 99.24% on the KDD CUP 99 dataset, surpassing the other models by a significant margin. The proposed methodology provides a robust and secure approach to intrusion detection, ensuring the confidentiality of sensitive data while improving detection accuracy.

Assessing privacy risks and incorporating privacy measures from the onset requires a comprehensive understanding of potential impacts on data subjects. Privacy Impact Assessments (PIAs) offer a systematic methodology for such purposes, which are closely related to Data Protection Impact Assessments (DPIAs), particularly outlined in Article 35 of the General Data Protection Regulation (GDPR). The core of a PIA is a Privacy Risk Assessment (PRA). PRAs can be integrated as part of full-fledged PIAs or independently developed to support PIA processes. Although these methodologies have been identified as essential enablers of privacy by design, their effectiveness has been criticized because of the lack of evidence of their rigorous and systematic evaluation. Hence, we conducted a Systematic Literature Review (SLR) to identify published PIA and PRA methodologies and assess how and to what extent they have been scientifically validated or evaluated. We found that these methodologies are rarely evaluated for their performance in practice, and most of them have only been validated in limited studies. Most validation evidence is found with PRA methodologies. Of the evaluated methodologies, PIAs were the most evaluated, where case studies were the predominant evaluation method. These evaluated methodologies can be easily transferred to an industrial setting or used by practitioners, as they provide evidence of their use in practice. In addition, the findings in this study can be used to inform researchers of the current state-of-the-art, and practitioners can understand the benefits and current limitations of the methodologies and adopt evidence-based practices.

Malware targeting user privacy has seen a surge in recent times, attributed to evolving global regulations and the boost of electronic commerce and online services. Moreover, recognizing privacy malware that employs obfuscation as evasion mechanism presents a major challenge due to its dynamics, resilience, and polymorphism at runtime, necessitating the application of forensic techniques such as memory dumping analysis in order to reveal suitable patterns and behaviors that enable its subsequent detection and classification. In this paper, we present three obfuscated privacy malware classifiers trained on the CIC-MalMem-2022 dataset. These solutions include a binary classifier to distinguish benign from malicious samples using logistic regression (LR), a multiclass classifier that further categorizes benign, spyware, ransomware, and trojan horse obfuscated privacy malware; and a more detailed multiclass classifier capable of discriminating benign samples from fifteen specific obfuscated privacy malware families. Multiclass classifiers were built using several traditional machine learning algorithms and a novel Deep Neural Network (DNN). We applied the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalances. In particular, our results demonstrate that DNN outperforms traditional machine learning algorithms, yielding statistically significant improvements in key metrics. These achievements reach practical thresholds, suggesting the potential for enhanced malware protection systems. The dataset and all the coding files required for experiments reproducibility are publicly available at https://github.com/dcevallossalas/PrivacyMalwareClassifiers.

Deep learning-based Multi-Task Classification (MTC) is widely used in applications such as facial attributes and healthcare, which require robust privacy guarantees. In this study, we propose a novel Multi-Trigger-Key (MTK) framework to fulfill the privacy-preservation objective of protecting sensitive information throughout the entire workflow of MTC. We provide two real world examples that demonstrates how MTK can be implemented in the context of healthcare and financial tasks. Each secured task in the multi-task dataset is linked to a specially crafted trigger-key, processed by a data distributor, a secret key distributor, an assembler, and a model optimizer/keeper in the MTK system. If a user is authorized to access certain data, the insertion of trigger keys will reveal the accurate information. Furthermore, the learning process is structured to allow the four MTK agents to collaboratively distribute privacy protection. To address the information leakage problem caused by correlations among different classes, MTK training also includes a tuning parameter, which is used to balance the protective efficacy and model performance. Theoretical assurances and experimental results demonstrate that privacy protection is effective without significantly compromising model performance.

Computer vision has become indispensable in various applications, including autonomous driving, medical imaging, security and surveillance, robotics, and pattern recognition. In recent years, the quality of training data has emerged as a critical factor for ensuring effectiveness in real-world scenarios. However, the increasing stringency of privacy regulations in various regions necessitates careful handling of collected images for computer vision. Personal information within images is typically anonymized by applying anonymization patterns to remove it. Empirical findings underscore the significant influence of data quality on the training of deep learning models. Striking the right balance between privacy and recognition performance becomes paramount. Therefore, it is essential to understand how the anonymization of image datasets affects deep learning model performance. In this paper, we thoroughly analyze the effects of different anonymization techniques on the performance of deep learning-based models in computer vision tasks, with a particular emphasis on presenting a model-centric perspective, such as the type of deep learning model and the number of parameters. We aim to provide valuable insights and guidelines for selecting the optimal level of anonymization that strikes a balance between recognition accuracy and privacy protection.