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In the 21st-century digital age, health data privacy remains a crucial concern. This paper evaluates the effectiveness of the Health Insurance Portability and Accountability Act, known as HIPAA. More specifically, it demonstrates a need for a unified federal framework in the U.S. that aligns with General Data Protection Regulation’s protections to address modern-day cybersecurity threats better. This article argues that in an era of increased globalization, the United States should confront the task of reforming its healthcare data protection law to align with current cybersecurity risks. We begin by examining landmark legislation across American states to reveal inconsistencies between state and federal protective rulings. Later, we uncover the reactive nature of HIPAA, in contrast to GDPR’s proactive and citizen-centric approach. Through evaluating past lawsuits related to patient protection noncompliance, this paper depicts significant differences in the purpose, coverage, and execution of data protection laws between the United States and the European Union. It highlights GDPR’s effectiveness in granting individuals greater control over their data. Furthermore, this article proposes the adoption of newfound systems for standardized risk analysis and enhanced security across healthcare providers.As healthcare becomes more accessible to the American public, the amount of data in this system increases. This nationwide surge in data underscores the critical need to assess whether privacy laws established in the 1990s remain sufficient. Therefore, updates to healthcare legislation are essential to establishing stringent patient protections in response to the significant rise in data breach incidents within the healthcare network.

This uses homomorphic encryption in cloud-based platforms to improve electronic health records (EHR) security and accessibility. Protecting sensitive medical data while enabling data processing and analysis is the main goal. The study examines how homomorphic encryption protects EHR data privacy and integrity. Its main purpose is to reduce risks of unauthorized access and data breaches to build trust between healthcare professionals and patients in digital healthcare. The research uses homomorphic encryption to safeguard cloud EHR storage and transmission. Results will highlight the algorithm's influence on data security and computing efficiency, revealing its potential use in healthcare to protect patient privacy and meet regulatory requirements. Results from dataset of patient health metrics show in the 1st instance sample data for 5 instances with ages between 57 to 88, blood pressure (BP) values from 33 to 85, glucose values from 5 to 99, and heart rate values from 24 to 88. In another study of 5 patients, cholesterol levels ranged from 10 to 80 mg/dL, body mass index (BMI) from 10 to 96 kg/m², smoking status from 14 to 79, and medication adherence from 6 to 78%. [ABSTRACT FROM AUTHOR]

In the era of mobile Internet, the widespread use of VPNs increases the demand for data security and privacy but also poses challenges for ISPs in terms of quality of service and traffic monitoring. The research in this paper focuses on how to accurately classify encrypted traffic. Traditional methods usually require manual labeling of features, which suffers from high cost and unstable accuracy. Due to the special characteristics of encrypted traffic, traditional labeling methods cannot be well adapted, so new solutions are urgently needed. In this paper, a generative learning method based on large-scale language models is adopted, which fuses encrypted traffic features into the T5 language model. The fine-tune T5 model conducts transfer learning with a small amount of data and achieve good classification accuracy. Compared with the traditional methods, the model performs better in terms of classification effectiveness. It can effectively classify encrypted traffic even with a small number of samples, and distinguish between VPN and non-VPN traffic. Test results on the ISCX VPN-nonVPN dataset show that the new generative classifier improves the F1 score to 98.5%, which is a 5.5% improvement compared to the previous one. The experiments show that the method is effective and efficient. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT), the scope of personal data sharing has significantly increased, enhancing convenience in daily life and optimizing resource management. However, this also poses challenges related to data privacy breaches and holdership threats. Typically, blockchain technology and cloud storage provide effective solutions. Nevertheless, the centralized storage architecture of traditional cloud servers is susceptible to single points of failure, potentially leading to system outages. To achieve secure data sharing, access control, and verification auditing, we propose a data security sharing scheme based on blockchain technology and attribute-based encryption, applied within the InterPlanetary File System (IPFS). This scheme employs multi-agent systems and attribute-based signcryption algorithms to process data, thereby enhancing privacy protection and verifying data holdership. The encrypted data are then stored in the distributed IPFS, with the returned hash values and access control policies uploaded to smart contracts, facilitating automated fine-grained access control services. Finally, blockchain data auditing is performed to ensure data integrity and accuracy. The results indicate that this scheme is practical and effective compared to existing solutions. [ABSTRACT FROM AUTHOR]

The most important and difficult challenge the digital society has recently faced is ensuring data privacy and security in cloud‐based Internet of Things (IoT) technologies. As a result, many researchers believe that the blockchain's Distributed Ledger Technology (DLT) is a good choice for various clever applications. Nevertheless, it encountered constraints and difficulties with elevated computing expenses, temporal demands, operational intricacy, and diminished security. Therefore, the proposed work aims to develop a Decentralized Identifiable Distributed Ledger Technology‐Blockchain (DIDLT‐BC) framework that is intelligent and effective, requiring the least amount of computing complexity to ensure cloud IoT system safety. In this case, the Rabin algorithm produces the digital signature needed to start the transaction. The public and private keys are then created to verify the transactions. The block is then built using the DIDLT model, which includes the block header information, hash code, timestamp, nonce message, and transaction list. The primary purpose of the Blockchain Consent Algorithm (BCA) is to find solutions for numerous unreliable nodes with varying hash values. The novel contribution of this work is to incorporate the operations of Rabin digital data signature generation, DIDLT‐based blockchain construction, and BCA algorithms for ensuring overall data security in IoT networks. With proper digital signature generation, key generation, blockchain construction and validation operations, secured data storage and retrieval are enabled in the cloud‐IoT systems. By using this integrated DIDLT‐BCA model, the security performance of the proposed system is greatly improved with 98% security, less execution time of up to 150 ms, and reduced mining time of up to 0.98 s. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach: This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings: Interest from libraries in AI technologies is tempered by concerns about the capability of tools and fears over data security and privacy. Exploring the technologies through small-scale experiments and a culture of collaboration and learning can ascertain each technology's suitability for the library's purpose and help allay the other concerns. Originality/value: The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format. [ABSTRACT FROM AUTHOR]

Optimizing digital twins in the Industrial Internet of Things (IIoT) requires secure and adaptable AI models. The IIoT enables digital twins, virtual replicas of physical assets, to improve real-time decision-making, but challenges remain in trust, data security, and model accuracy. This paper presents a novel framework combining blockchain technology and federated learning (FL) to address these issues. By deploying AI models on edge devices and using FL, data privacy is maintained while enabling collaboration across industrial assets. Blockchain ensures secure data management and transparency, while explainable AI (XAI) enhances interpretability. The framework improves transparency, control, security, privacy, and scalability for self-optimizing digital twins in IIoT. A real-world evaluation demonstrates the framework's effectiveness in enhancing security, explainability, and optimization, offering improved efficiency and reliability for industrial operations. [ABSTRACT FROM AUTHOR]

The use of cloud computing for storing and processing cyber data via the internet has grown widespread in the field of cyber security. Ensuring the secure sharing of cyber data, including texts, images, audio, and video, over the cloud is paramount. However, cloud computing encounters significant challenges concerning cyber data security, authentication, and privacy. The prevailing approaches to data security encounter challenges such as the generation of intricate keys, intensive computation for large keys, and susceptibility to attacks by intruders. Scalability presents its own set of obstacles in maintaining cyber data privacy and ensuring secure communication. A significant privacy concern arises from the frequent changes in membership and data sharing among multiple owners. This paper introduces a novel approach to secure data sharing within data centres by proposing a scheme that employs Private Key Dynamic Visual Cryptography (PK-DVC), Multiple Key Encryption Visual One-Time Pad (MK-VOTP), and visual steganography for encoding and decoding. MK-VOTP is used for data encryption, facilitating data owners' encryption via utilising their identity with supplementary security features. Subsequently, the encrypted data is kept in the cloud, guaranteeing heightened security protocols. Visual steganography is employed for further authentication purposes. Decrypting the original data requires users who fulfil the encrypted properties. This increases security and reduces critical size, allowing several authenticated owners to share data without conflicts. Combining all shares recreates the original picture. The approach described in the paper sounds innovative and addresses several key challenges in ensuring secure data sharing within data centers, particularly in the context of cloud computing. The proposed scheme main components are Private Key Dynamic Visual Cryptography (PK-DVC), Multiple Key Encryption Visual One-Time Pad (MK-VOTP), and Visual Steganography. These three components plays important role in further authenticating the encrypted data or providing additional security measures beyond encryption. By combining these techniques, the proposed scheme aims to provide robust security for sharing cyber data in cloud environments. The use of dynamic keys, multiple encryption layers, and steganography can enhance data security, making it challenging for intruders to access or decipher sensitive information. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence, the Internet of Things (IoT) can deploy various machine learning algorithms for network and application management. In the IoT environment, many sensors and devices generate massive data, but data security and privacy protection have become a serious challenge. Federated learning (FL) can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing. This review aims to deeply explore the combination of FL and the IoT, and analyze the application of federated learning in the IoT from the aspects of security and privacy protection. In this paper, we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security. Next, we focus on exploring and analyzing the advantages of the combination of FL on the Internet, including privacy security, attack detection, efficient communication of the IoT, and enhanced learning quality. We also list various application scenarios of FL on the IoT. Finally, we propose several open research challenges and possible solutions. [ABSTRACT FROM AUTHOR]

As smart grid technology rapidly advances, the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection. Current research emphasizes data security and user privacy concerns within smart grids. However, existing methods struggle with efficiency and security when processing large-scale data. Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge. This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities. The approach optimizes data preprocessing, integrates Long Short-Term Memory (LSTM) networks for handling time-series data, and employs homomorphic encryption to safeguard user privacy. It also explores the application of Boneh Lynn Shacham (BLS) signatures for user authentication. The proposed scheme's efficiency, security, and privacy protection capabilities are validated through rigorous security proofs and experimental analysis. [ABSTRACT FROM AUTHOR]

In recent years, the healthcare sector has encountered significant challenges in authenticating identities for online medical services. A predominant reliance on centralized identity management systems (IDMs) has presented obstacles to the seamless exchange of patient identities among various healthcare institutions, often resulting in data isolation within individual silos. Of paramount concern are the potential privacy breaches associated with centralized IDMs, which may compromise patient confidentiality. In response to these challenges, we propose a novel approach to securely sharing patient details across multiple hospitals utilizing the zero-knowledge access protocol (MediCrypt-ZKAP) within the Hyperledger Fabric blockchain framework. By adopting MediCrypt-ZKAP, hospitals can effectively verify the identities of requesting entities without disclosing sensitive patient information, thereby ensuring the highest levels of confidentiality and privacy protection. The proposed system represents a proactive step towards addressing the critical need for secure and interoperable patient data exchange within the healthcare sector. Through the integration of MediCrypt-ZKAP into existing blockchain infrastructure, our solution aims to enhance data security and privacy while promoting seamless collaboration among healthcare institutions. [ABSTRACT FROM AUTHOR]

Motivation: We are witnessing an enormous growth in the amount of molecular profiling (-omics) data. The integration of multi-omics data is challenging. Moreover, human multi-omics data may be privacy-sensitive and can be misused to de-anonymize and (re-)identify individuals. Hence, most biomedical data is kept in secure and protected silos. Therefore, it remains a challenge to re-use these data without infringing the privacy of the individuals from which the data were derived. Federated analysis of Findable, Accessible, Interoperable, and Reusable (FAIR) data is a privacy-preserving solution to make optimal use of these multi-omics data and transform them into actionable knowledge. Results: The Netherlands X-omics Initiative is a National Roadmap Large-Scale Research Infrastructure aiming for efficient integration of data generated within X-omics and external datasets. To facilitate this, we developed the FAIR Data Cube (FDCube), which adopts and applies the FAIR principles and helps researchers to create FAIR data and metadata, to facilitate re-use of their data, and to make their data analysis workflows transparent, and in the meantime ensure data security and privacy. [ABSTRACT FROM AUTHOR]

End-cloud collaborative computing framework ensures the security and privacy of edge device data, enabling collaborative training of global models without direct data exchange. However, in practical scenarios, anomalies in training or edge device data may severely degrade or disable the global model’s performance. Existing frameworks lack effective debugging and anomaly localization, hindering real-time monitoring and precise identification of abnormal edge devices in data heterogeneity scenarios. In this paper, we propose a new method named FedCheck, a debugging framework for end-cloud collaborative federated learning that enables real-time alerts and detects abnormal devices for nonindependent and identically distributed (nonIID) data without disrupting the regular training process. Specifically, we employ a model similarity-based method to quantitatively assess the degree of device anomaly in data heterogeneity scenarios, supporting real-time alerts during the end-cloud collaboration process. Furthermore, a simulation program replays the training process based on recorded telemetry data, facilitating backtracking debugging of any training round and the status of edge devices. Finally, the framework removes abnormal devices and repairs the global model. Experiments on MNIST and Fashion-MNIST datasets demonstrate that FedCheck can effectively detect and locate abnormal devices in data heterogeneity scenarios. Even in large-scale federated learning, it maintains high detection performance and exhibits good scalability. [ABSTRACT FROM AUTHOR]

To secure the privacy, confidentiality, and integrity of Speech Data (SD), the concept of secure Speech Recognition (SR) involves accurately recording and comprehending spoken language while employing diverse security processes. As the Internet of Things (IoT) rapidly evolves, the integration of SR capabilities into IoT devices gains significance. However, ensuring the security and privacy of private SD post-integration remains a critical concern. Despite the potential benefits, implementing the proposed Reptile Search Optimized Hidden Markov Model (RSO-HMM) for SR and integrating it with IoT devices may encounter complexities due to diverse device types. Moreover, the challenge of maintaining data security and privacy for assigned SD in practical IoT settings poses a significant hurdle. Ensuring seamless interoperability and robust security measures is essential. We introduce the Reptile Search Optimized Hidden Markov Model (RSO-HMM) for SR, utilizing retrieved aspects as speech data. Gathering a diverse range of SD from speakers with varying linguistic backgrounds enhances the accuracy of the SR system. Preprocessing involves Z-score normalization for robustness and mitigation of outlier effects. The Perceptual Linear Prediction (PLP) technique facilitates efficient extraction of essential acoustic data from speech sources. Addressing data security, Elliptic Curve Cryptography (ECC) is employed for encryption, particularly suited for resource-constrained scenarios. Our study evaluates the SR system, employing key performance metrics including accuracy, precision, recall, and F1 score. The thorough assessment demonstrates the system's remarkable performance, achieving an impressive accuracy of 96%. The primary objective revolves around appraising the system's capacity and dependability in accurately transcribing speech signals. By proposing a comprehensive approach that combines the RSO-HMM for SR, data preprocessing techniques, and ECC encryption, this study advocates for the wider adoption of SR technology within the IoT ecosystem. By tackling critical data security concerns, this approach paves the way for a safer and more efficient globally interconnected society, encouraging the broader utilization of SR technology in various applications. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is designed to protect privacy of participants by not allowing direct access to the participants' local datasets and training processes. This limitation hinders the server's ability to verify the authenticity of the model updates sent by participants, making FL vulnerable to poisoning attacks. In addition, gradients in FL process can reveal private information about the local dataset of the participants. However, there is a contradiction between improving robustness against poisoning attacks and preserving privacy of participants. Privacy-preserving techniques aim to make their data indistinguishable from each other, which hinders the detection of abnormal data based on similarity. It is challenging to enhance both aspects simultaneously. The growing concern for data security and privacy protection has inspired us to undertake this research and compile this survey. In this survey, we investigate existing privacy-preserving defense strategies against poisoning attacks in FL. First, we introduce two important classifications of poisoning attacks: data poisoning attack and model poisoning attack. Second, we study plaintext-based defense strategies and classify them into two categories: poisoning tolerance and poisoning detection. Third, we investigate how the combination of privacy techniques and traditional detection strategies can be achieved to defend against poisoning attacks while protecting the privacy of the participants. Finally, we also discuss the challenges faced in the area of security and privacy. [ABSTRACT FROM AUTHOR]

Frequent user data breaches and misuse incidents highlight the flaws in current identity management systems. This study proposes a blockchain-based, peer-supervised self-sovereign identity (SSI) generation and privacy protection technology. Our approach creates unique digital identities on the blockchain, enabling secure cross-domain recognition and data sharing and satisfying the essential users' requirements for SSI. Compared to existing SSI solutions, our approach has the practical advantages of less implementation cost, ease of users' understanding and agreement, and better possibility of being soon adopted by current society and legal systems. The key innovative technical features include (1) using a zero-knowledge proof technology to ensure data remain "usable but invisible", mitigating data breach risks; (2) introducing a peer review mechanism among service providers to prevent excessive data requests and misuse; and (3) implementing a comprehensive multi-party supervision system to audit all involved parties and prevent misconduct. [ABSTRACT FROM AUTHOR]

The integration of blockchain and federated learning (FL) has emerged as a promising solution to address data privacy and security challenges in Intelligent Unmanned Ports (IUPs). However, existing blockchain federated learning (BFL) frameworks encounter significant limitations, including high latency, inefficient data processing, and limited scalability, particularly in scenarios with sparse and distributed data. This paper introduces a novel multi-chain federated learning (MFL) framework to overcome these challenges. The proposed MFL architecture interconnects multiple BFL chains to facilitate the secure and efficient aggregation of data across distributed devices. The framework enhances privacy and efficiency by transmitting aggregated model updates rather than raw data. A low-frequency consensus mechanism is employed to improve performance, leveraging game theory for representative selection to optimize model aggregation while reducing inter-chain communication overhead. The experimental results demonstrate that the MFL framework significantly outperforms traditional BFL in terms of accuracy, latency, and system efficiency, particularly under the conditions of high data sparsity and network latency. These findings highlight the potential of MFL to provide a scalable and secure solution for decentralized learning in IUP environments, with broader applicability to other distributed systems such as the Industrial Internet of Things (IIoT). [ABSTRACT FROM AUTHOR]

Developing a secured and accurate disease diagnosis framework in the healthcare cloud systems are still remains one of the crucial problems in recent times. Due to the rapid growth of information and technology, it is highly essential to protect the patient health information against the unauthenticated users for ensuring the privacy and security. For this purpose, the different types of security approaches are developed in the conventional works, which are mainly focused on increasing the privacy of medical data stored in the cloud systems. However, it lacks with the major issues of increased computational overhead, communication cost, lack of security, complex mathematical modeling, and increased time consumption. Therefore, the proposed work objects to implement an intelligent and advanced privacy preserving framework, named as, lightweight signing signature based secured jellyfish data aggregation (LS3JDA) for ensuring the privacy of medical data in the healthcare cloud systems. The main contribution of this research work is to develop a new and lightweight privacy preservation model by incorporating the functions of both AI and signing signature algorithms for assuring data security in cloud systems. Moreover, it simplified the process of entire privacy preservation system with low computational burden and high data security. It also objects to accurately predict the type of disease based on the patients' medical history by using an advanced random forest (RF) machine learning methodology. The novel contributions of this work are, a message signing signature generation algorithm is used to strengthen the security of patients' medical data, and a jelly fish optimization (JFO) methodology is used to improve the process of data aggregation. The primary advantages of the proposed system are reduced processing time, low computational burden, and simple to deploy. For validating the results of the proposed model, several parameters include level of security, time, throughput, latency, signature cost, and communication overhead are assessed during evaluation. Moreover, the results are contrasted with some of the recent privacy preservation models for assuring the superiority of the proposed framework. Here, the overall processing time is reduced up to 1.5 ms, and communication overhead is reduced up to 100 bytes with the use of optimization integrated data aggregation model. [ABSTRACT FROM AUTHOR]

In this study, a comprehensive digital twin monitoring system for ship equipment was designed and implemented, including the system architecture, key technologies, and applications. Through data-driven models and operational monitoring system analysis, our PSO-SVM-based time series prediction method demonstrated excellent predictive capabilities for catamaran equipment, achieving efficient fault warnings using a threshold method. The digital twin model and virtual scenarios constructed here provide a visualisation and simulation platform for equipment status monitoring, enhanced fault diagnosis and support for maintenance decisions. The system integrates real-time monitoring, fault warning, and data analysis, and testing results show good stability and accuracy. In addition, the system optimises the user experience through multi-round feedback testing, and ensures data security and privacy protection through multi-layer encryption, identity verification, and role-based access control. A case study indicates that the proposed system effectively monitors equipment status and provides fault warnings, and has broad application prospects and practical value. Future work will focus on optimising the functionality and improving the applicability and security of the system. [ABSTRACT FROM AUTHOR]

Airports generate vast amounts of data across various systems that are crucial for operational efficiency, safety and enhanced passenger experience. As airports increasingly rely on this data in order to adapt to evolving passenger experience demands, business environments and regulatory requirements, data governance becomes essential for managing, safeguarding and leveraging data effectively. A robust data governance framework provides the structure for ensuring data quality, security and compliance while enabling airports to harness data for artificial intelligence (AI) applications such as predictive maintenance and passenger flow management. By starting with a clear scope, objectives and policies, airports can build a data governance framework that addresses both current needs and future challenges. This paper explores the role of data governance in making airports AI-ready, outlining best practices for implementing a governance programme. It highlights the importance of tools such as data lineage tracking and the need for strong data security measures to comply with regulations such as General Data Protection Regulation (GDPR). The paper also emphasises the need for a culture of accountability, outlining key roles such as data stewards and chief data officers (CDOs) to ensure consistent, robust data management. As AI adoption grows, airports must focus on maintaining data integrity, fostering transparency and ensuring regulatory compliance to unlock the full potential of their data assets while safeguarding privacy and building stakeholder trust. [ABSTRACT FROM AUTHOR]

The rapid development in neurotechnology has significantly advanced our ability to understand and manipulate brain functions. However, these advancements have raised critical concerns regarding the security and privacy of brain data. This paper aims to explore the multifaceted issues surrounding the protection of brain data, focusing on neurosecurity. We begin by reviewing the current technological landscape, focusing on methods used to secure brain data, including encryption, authentication protocols, and anonymisation techniques. Drawing parallels with established computer security practices, we highlight both the strengths and limitations of these approaches when applied to neural data. Next, we delve into the ethical dilemmas posed by brain data security. Issues such as mental privacy and informed consent are analysed. The implications of unauthorised access to brain data and the misuse of such data in various contexts, including criminal justice, employment, and military applications, are discussed in detail. Furthermore, we examine the philosophical perspectives on brain data security, particularly concerning personal identity, autonomy, and freedom of thought. We explore how the manipulation and protection of brain data intersect with longstanding debates in ethics and philosophy, proposing frameworks for addressing these challenges. By combining a technological review with an ethical and philosophical analysis, this paper aims to provide a comprehensive understanding of neurosecurity and brain data security. We conclude with recommendations for future research and policy development to ensure the ethical and responsible use of brain data, emphasising the need for robust governance frameworks that protect individual rights while fostering technological innovation. Keywords: Brain Data Security, NeuroSecurity. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC), is a technology that extends the power of cloud computing to the edge of the end device, with the primary goal of providing faster response times and an optimized quality of service. Given that edge devices are often used by smaller organizations with less computing power, data on the edge are more susceptible to data corruption, which is closely related to uneven resource allocation and uneven security protection. It is therefore particularly important to check the integrity of the MEC to ensure that it is intact, which in turn serves the symmetry of data security protection and facilitates the realization and optimization of symmetry in resource allocation. An integrity verification protocol MEC-P is proposed. MEC-P allows a third-party auditor (TPA) to check data integrity on the edge without violating users' data privacy and query pattern privacy. We rigorously demonstrate the security and privacy guarantees of the protocol. In addition, this protocol carefully considers the scenarios of single edge node and multiple edge nodes, as well as the complexity of dynamic modifications. Both theoretical analysis and experimental results demonstrate that the proposed protocol is effective. [ABSTRACT FROM AUTHOR]

The European Union's General Data Protection Regulation (GDPR) of 2016 is widely recognised as the benchmark global standard for data-privacy law. In recent years, countries across Southeast Asia have enacted or updated data-privacy laws with provisions that align with the GDPR. This paper explores the balance of internal and external forces driving these regulatory changes. It argues that a nuanced understanding of diffusion in the policymaking process allows us to see beyond the 'Brussels Effect' and how the increasing digitalistion of Southeast Asian societies has created increasing local demand for regulatory change. While an analytical focus on scripting in the drafting of GDPR-like laws focuses attention on the extraterritorial reach of the EU's regulatory power, an analytical focus on problematisation points to different pathways of domestic learning regarding data privacy, especially in response to rising data-security threats. [ABSTRACT FROM AUTHOR]

Protecting cloud-based personal health records (PHRs) is critical toensuring privacy and data security for individuals in an era of rapidly growing digital healthcare systems. Traditional encryption methods face challenges in balancing computational efficiency with robust data protection. Proxy-based certificateless cryptography offers a promising solution by eliminating the need for certificate management while enabling seamless secure communication. In this work, a Proxy-Encryption Based Rabin Certificateless Signcryption (PERSC) scheme is proposed to enhance the security and efficiency of cloud-stored PHRs. The proposed method integrates proxy encryption with Rabinbased certificateless cryptography, leveraging its quadratic residue properties to secure key exchanges. A hybrid signcryption mechanism ensures data integrity and confidentiality while providing resistance against various attacks, including man-in-the-middle and key exposure attacks. This approach minimizes computational overhead, particularly on resource-constrained devices. Experimental results demonstrate that PERSC reduces encryption time by 38% compared to traditional certificateless cryptographic methods and achieves a 98.5% success rate in preventing unauthorized access. Additionally, PERSC's decryption latency is reduced by 30%, making it suiTable.for real-time applications in cloud environments. The proposed framework ensures a balance between performance and security while providing a scalable solution for managing sensitive PHR data in the cloud. These findings underscore the feasibility of adopting advanced cryptographic techniques for cloud-based healthcare systems to meet increasing demands for secure and efficient data sharing. [ABSTRACT FROM AUTHOR]

E-Government is used to provide various services to citizens via an online portal and is currently available in many countries. Current e-government technology is supported by an extensive, centrally controlled database and a collection of applications linked to it through web interfaces. However, e-government depends too much on centralization. E-government services store sensitive data about citizens, making them particularly vulnerable to cyberattacks, data breaches, and access control. Therefore, alternative techniques should be developed to protect sensitive data and ensure secure storage in e-government platforms. This study proposes a safe and distributed electronic system for e-government based on blockchain technology to protect sensitive data from breaches. This system uses advanced encryption methods, including Lightweight Encryption Device (LED) and Elliptic-Curve Cryptography (ECC), to protect transmitted data. The proposed system employs a two-layer encryption approach to secure user data. The first layer utilizes the LED algorithm with a randomly generated key, and the second employs the ECC algorithm with a public key obtained from the blockchain server to enhance user data security and privacy. The proposed system allows data to be disseminated across many networks, retrieves and synchronizes data in case of unauthorized changes, and restores them to their original form. Experimental results showed that the proposed system takes an average of 0.05 seconds to complete the login process for five successful login attempts, confirming the effectiveness of the proposed approach in the execution of login procedures. The effectiveness of this system in resisting different attack types was verified through formal and informal security analyses and simulations based on the Scyther tool. [ABSTRACT FROM AUTHOR]

Privacy protection is a fundamental guarantee for secure data flows and the basic requirement for data security. A reasonable privacy protection system acts as a catalyst for unlocking the financial value of data. The current legislative framework for personal data protection in China, adhering to the principle of proportionality, establishes critical measures such as informed consent for data collection and processing, data classification and grading management, and remedies for data leakage and other risks. In addition, in judicial practice, typical disputes regarding personal information protection and privacy rights have been promoted to clarify the scope for collecting users' personal information and biometric data. Although further improvements are needed in legislative, judicial, and technical approaches, China's commitment and practice in personal data protection are noteworthy. The existing legislation, law enforcement, and technical practices play an increasingly vital role in realizing the financial value of data and are essential for international cooperation on privacy protection. Furthermore, it is crucial to actively explore cooperation mechanisms for cross-border data flows under the principle of data sovereignty, participate in developing international rules for cross-border data flows, and formulate different management norms for cross-border data flows across different industries. [ABSTRACT FROM AUTHOR]

In today's healthcare industry, safeguarding patient data is critical due to the increasing digitization of medical records, which makes them vulnerable to cyber threats. Telehealth services, while providing immense benefits in terms of accessibility and efficiency, introduce complex challenges in maintaining data privacy and security. This paper proposes a privacy-preserving framework for secure data sharing within telehealth services, employing blockchain technology and advanced cryptographic techniques. The framework ensures that all patient health data are encrypted using homomorphic encryption before storage on the blockchain, guaranteeing confidentiality and protecting data from unauthorized access. Secure multi-party computation (SMPC) is integrated for encrypted data computations, maintaining data confidentiality even during operations. Smart contracts enforce access control, ensuring that patient preferences and regulatory requirements such as the HIPAA and the GDPR are met. Furthermore, the framework includes auditing and verifying data integrity mechanisms, making it resilient against cyber threats such as impersonation, replay, and Man-In-The-Middle attacks. The analysis demonstrates the framework's superior performance in addressing these challenges compared to that of existing systems. Future work suggests integrating AI-driven threat detection and quantum-resistant cryptographic techniques to enhance security further and adapt to the evolving telehealth landscape. [ABSTRACT FROM AUTHOR]

Quantum-secure cryptography is a dynamic field due to its crucial role in various domains. This field aligns with the ongoing efforts in data security. Post-quantum encryption (PQE) aims to counter the threats posed by future quantum computers, highlighting the need for further improvement. Based on the learning with error (LWE) system, this paper introduces a novel asymmetric encryption technique that encrypts entire messages of n bits rather than just 1 bit. This technique offers several advantages including an additive homomorphic cryptosystem. The robustness of the proposed lightweight public key encryption method, which is based on a new version of LWE, ensures that private keys remain secure and that original data cannot be recovered by an attacker from the ciphertext. By improving encryption and decryption execution time—which achieve speeds of 0.0427 ms and 0.0320 ms, respectively—and decreasing ciphertext size to 708 bits for 128-bit security, the obtained results are very promising. [ABSTRACT FROM AUTHOR]

Abstract The development of Web 3.0 technology may signify the dawn of a new digital era. Its concepts of co‐management, co‐construction, and sharing address the need for private data sharing among medical institutions. However, the sharing of private data has been challenging due to the lack of effective monitoring methods and authorization mechanisms. Additionally, controlling the scope of data sharing, providing incentives, and ensuring legal compliance have presented difficulties. To this end, a medical privacy data security sharing model based on key technologies of Web 3.0 has been proposed and implemented. It stores the source data in Inter Planetary File System by constructing an index of private data keywords, generates trapdoors using query keywords, and achieves retrieval of ciphertext data. Finally, data users apply to multiple parties for joint secure computing to obtain the use of private data. The experimental results indicate that when the size of the private data is less than 5 MB, with 3000 ciphertext indexes and three search keywords, both encryption and decryption times are around 50 ms, and the retrieval time is approximately 1.6 s. This performance is adequate for typical medical privacy sharing and computing scenarios.

The rapid development of deep learning technology has led to the emergence of diffusion models as a promising generative model with diverse applications. These include image generation, audio and video synthesis, molecular design, and text generation. The distinctive generation mechanism and exceptional generation quality of diffusion models have made them a valuable tool in these diverse fields. However, with the extensive deployment of diffusion models in the domain of image generation, concerns pertaining to data privacy, data security, and artistic ethics have emerged with increasing prominence. Given the accelerated pace of development in the field of diffusion models, the majority of extant surveys are deficient in two respects: firstly, they fail to encompass the latest advances in diffusion-based image synthesis; and secondly, they seldom consider the potential social implications of diffusion models. In order to address these issues, this paper presents a comprehensive survey of the most recent applications of diffusion models in the field of image generation. Furthermore, it provides an in-depth analysis of the potential social impacts that may result from their use. Firstly, this paper presents a systematic survey of the background principles and theoretical foundations of diffusion models. Subsequently, this paper provides a detailed examination of the most recent applications of diffusion models across a range of image generation subfields, including style transfer, image completion, image editing, super-resolution, and beyond. Finally, we present a comprehensive examination of these social issues, addressing data privacy concerns, such as the potential for data leakage and the implementation of protective measures during model training. We also analyse the risk of malicious exploitation of the model and the defensive strategies employed to mitigate such risks. Additionally, we examine the implications of the authenticity and originality of generated images on artistic creativity and copyright protection. [ABSTRACT FROM AUTHOR]

The convergence of quantum physics and medicine has the potential to transform healthcare in unprecedented ways. Quantum computers can process vast quantities of data with unmatched velocity, facilitating more precise diagnoses and customized treatment strategies aligned with each individual's distinct genetic profile. Quantum sensors can identify diseases in their initial stages, facilitating preemptive measures prior to the manifestation of symptoms. Quantum encryption technology guarantees the utmost preservation of patient privacy and data security. Additionally, quantum concepts like superposition and entanglement may soon be utilized for innovative medicines that precisely target individual molecules, hence reducing side effects and enhancing efficacy. As researchers investigate the intersection of quantum mechanics and medicine, we anticipate a future in which healthcare is proactive, preventive, and genuinely individualized rather than merely reactive. [ABSTRACT FROM AUTHOR]

Summary: Blockchain (BC) technology has been incorporated into the infrastructure of different kinds of applications that require transparency, reliability, security, and traceability. However, the BC still has privacy issues because of the possibility of privacy leaks when using publicly accessible transaction information, even with the security features offered by BCs. Specifically, certain BCs are implementing security mechanisms to address data privacy to prevent privacy issues, facilitates attack‐resistant digital data sharing and storage platforms. Hence, this proposed review aims to give a comprehensive overview of BC technology, to shed light on security issues related to BC, and to emphasize the privacy requirements for existing applications. Many proposed BC applications in asset distribution, data security, the financial industry, the Internet of Things, the healthcare sector, and AI have been explored in this article. It presents necessary background knowledge about BC and privacy strategies for obtaining these security features as part of the evaluation. This survey is expected to assist readers in acquiring a complete understanding of BC security and privacy in terms of approaches, ideas, attributes, and systems. Subsequently, the review presents the findings of different BC works, illustrating several efforts that tackled privacy and security issues. Further, the review offers a positive strategy for the previously described integration of BC for security applications, emphasizing its possible significant gaps and potential future development to promote BC research in the future. [ABSTRACT FROM AUTHOR]

Effective summarization of unstructured patient data in electronic health records (EHRs) is crucial for accurate diagnosis and efficient patient care, yet clinicians often struggle with information overload and time constraints. This review dives into recent literature and case studies on both the significant impacts and outstanding issues of patient chart review on communications, diagnostics, and management. It also discusses recent efforts to integrate artificial intelligence (AI) into clinical summarization tasks, and its transformative impact on the clinician's potential, including but not limited to reductions of administrative burden and improved patient-centered care. Furthermore, it takes into account the numerous ethical challenges associated with integrating AI into clinical workflow, including biases, data privacy, and cybersecurity. [ABSTRACT FROM AUTHOR]

Objectives: World Health Organization issued Joint Statement on Data Protection and Privacy in the COVID-19 Response stating that collection of vast amounts of personal data may potentially lead to the infringement of fundamental human rights and freedoms. The Organization for Economic Cooperation and Development called on national governments to adhere to the international principles for data security and confidentiality. This paper describes the methods used to assist the Ministry of Health in bringing awareness of the data ownership, confidentiality and security principles to COVID-19 responders. Methods: The Sierra Leone Epidemiological Data (SLED) Team data managers conducted training for groups of COVID-19 responders. Training included presentations on data confidentiality, information disclosure, physical and electronic data security, and cyber-security; and interactive discussion of real-life scenarios. A game of Jeopardy was created to test the participant's knowledge. Results: This paper describes the methods used by the SLED Team to bring awareness of the DOCS principles to more than 2,500 COVID-19 responders. Conclusion: Similar efforts may benefit other countries where the knowledge, resources, and governing rules for protection of personal data are limited. [ABSTRACT FROM AUTHOR]

The era of big data has brought rapid growth and widespread application of data, but the imperfections in the existing data integration system have become obstacles to its high-quality development. The conflict between data security and shared utilization is significant, with traditional data integration methods risking data leakage and privacy breaches. The proposed Privacy-Preserving Record Linkage (PPRL) technology, has effectively resolved this contradiction, enabling efficient and secure data sharing. Currently, many solutions have been developed for PPRL issues, but existing assessments of PPRL methods mainly focus on single indicators. There is a scarcity of comprehensive evaluation and comparison frameworks that consider multiple indicators of PPRL(such as linkage quality, computational efficiency, and security), making it challenging to achieve a comprehensive and objective assessment. Therefore, it has become an urgent issue for us to conduct a multi-indicator comprehensive evaluation of different PPRL methods to explore the optimal approach. This article proposes the use of an modified CRITIC method to comprehensively evaluate PPRL methods, aiming to select the optimal PPRL method in terms of linkage quality, computational efficiency, and security. The research results indicate that the improved CRITIC method based on mathematical statistics can achieve weight allocation more objectively and quantify the allocation process effectively. This approach exhibits exceptional objectivity and broad applicability in assessing various PPRL methods, thereby providing robust scientific support for the optimization of PPRL techniques. [ABSTRACT FROM AUTHOR]

The increasing use of technology has led to the incorporation of software-defined network (SDN) communication in ad hoc mobile environments. Although a lot of study has been done on MANET network security, privacy issues in SDN-enabled MANETs have received less attention. We suggest using PRISDASM (Enhanced Data Security and Privacy for SDN-Enabled MANET), a cutting-edge technology created to improve data integrity and privacy protections, to close this gap. Our suggested approach provides a practical way to handle these problems by integrating online/offline signature approaches for safe data aggregation, Paillier homomorphic encryption, and the Walrus optimization algorithm for cluster head selection. Furthermore, in order to select the best cluster head, our study presents a fitness function that considers trust, mobility, and energy consumption criteria. Our six-phase solution is designed to protect sensitive data, authenticate users and ensure data integrity, and manage aggregation requests over many networks to enable effective communication. Furthermore, our strategy specifically targets lightweight processing to reduce the computational load on mobile nodes and enable seamless integration into different systems. Using ns3 simulation, we have put our suggested system into practice and assessed its effectiveness, showing that it has better security features and communication efficiency than existing methods. Our findings unequivocally show that PRISDASM outperforms current techniques for data integrity and privacy in SDN-enabled MANETs. [ABSTRACT FROM AUTHOR]

The traditional methods for identifying sensitive data in APIs mainly encompass rule-based and machine learning-based approaches. However, these methods suffer from inadequacies in terms of security and robustness, exhibit high false positive rates, and struggle to cope with evolving threat landscapes. This paper proposes a method for detecting sensitive data in APIs based on the Federated Large Language Model (FedAPILLM). This method applies the large language model Qwen2.5 and the LoRA instruction tuning technique within the framework of federated learning (FL) to the field of data security. Under the premise of protecting data privacy, a domain-specific corpus and knowledge base are constructed for pre-training and fine-tuning, resulting in a large language model specifically designed for identifying sensitive data in APIs. This paper conducts comparative experiments involving Llama3 8B, Llama3.1 8B, and Qwen2.5 14B. The results demonstrate that Qwen2.5 14B can achieve similar or better performance levels compared to the Llama3.1 8B model with fewer training iterations. [ABSTRACT FROM AUTHOR]

Federated learning avoids centralizing data in a central server by distributing the model training process across devices, thus protecting privacy to some extent. However, existing research shows that model updates (e.g., gradients or weights) exchanged during federated learning may still indirectly leak sensitive information about the original data. Currently, single-key homomorphic encryption methods applied in federated learning cannot solve the problem of privacy leakage that may be caused by the collusion between the participant and the federated learning server, whereas existing privacy-preserving federated learning schemes based on multi-key homomorphic encryption in semi-honest environments have deficiencies and limitations in terms of security and application conditions. To this end, this paper proposes a privacy-preserving federated learning scheme based on multi-key fully homomorphic encryption to cope with the potential risk of privacy leakage in traditional federated learning. We designed a multi-key fully homomorphic encryption scheme, mMFHE, that encrypts by aggregating public keys and requires all participants to jointly participate in decryption sharing, thus ensuring data security and privacy. The proposed privacy-preserving federated learning scheme encrypts the model updates through multi-key fully homomorphic encryption, ensuring confidentiality under the CRS model and in a semi-honest environment. As a fully homomorphic encryption scheme, mMFHE supports homomorphic addition and homomorphic multiplication for more flexible applications. Our security analysis proves that the scheme can withstand collusive attacks by up to N − 1 users and servers, where N is the total number of users. Performance analysis and experimental results show that our scheme reduces the complexity of the NAND gate, which reduces the computational load and improves the efficiency while ensuring the accuracy of the model. [ABSTRACT FROM AUTHOR]

ABSTRACT The recent global trend is the convergence of information and communications technology (ICT). By applying ICT in various fields such as the humanities, new types of products and services are created, and new values that help people's lives can be created. AI can be selected as a representative technology in such convergence ICT. However, applying AI technology to actual production requires ensuring data security. Federated learning (FL) can achieve secure sharing of data, where all parties participate in model training locally and upload it to the server for aggregation. The data never leaves the parties involved, thus solving the problems of data privacy and data silos. However, FL faces issues such as high communication cost, imbalanced performance distribution among participants, and low privacy protection. To achieve a balance between model accuracy, communication cost, fairness, and privacy, this paper proposes a multi‐objective optimization‐based FL algorithm (M‐FedAvg). The multi‐objective optimization problem of maximising the accuracy of the global model, minimising the communication cost, minimising the variance of the accuracy, and minimising the privacy budget is solved by NSGA‐III. The experimental results show that the algorithm proposed can effectively reduce the communication cost of FL and achieve privacy protection for participants without affecting the accuracy of the global model. [ABSTRACT FROM AUTHOR]

In a world where technology is rapidly advancing, regulation of dark pattern practices has become a topic of increasing importance. Society has become somewhat desensitized to these deceptive online practices that manipulate users into taking actions, which are not in their best interests, such as difficulty unsubscribing from a service, prominence of consent buttons, and countless other advanced tactics to obscure transparency. However, these ongoing practices harm both the individual user, and society in general, by impeding informed decision‐making. This Article addresses the European Union's leading efforts to tackle dark pattern practices, and in particular, addresses the numerous legislative acts which have been enacted to regulate and eliminate them. The acts explored in this Article include the General Data Protection Regulation, the Uniform Commercial Practices Directive, the Data Act, the Digital Markets Act, the Digital Services Act, the Amendment to the Directive on Financial Services Contracts Concluded at a Distance, and the Artificial Intelligence Act. This Article then discusses the interplay between the numerous acts, and the resulting ambiguities and overlap which have led to a level of regulatory redundancy. This Article examines not only the difficulty in interpretation of the various acts, but additionally, explores the issues which arise in implementation from a jurisdictional perspective. Further, this Article suggests potential solutions to address the fragmented legislation, including a hybrid form of harmonization, as well as methods for consolidation and centralization. [ABSTRACT FROM AUTHOR]