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Serverless computing has revolutionized cloud services by abstracting infrastructure management, enabling developers to focus on application logic. This paper examines the security and compliance features of three major serverless platforms: AWS Lambda, Azure Functions, and Google Cloud Functions. By evaluating authentication mechanisms, data encryption practices, vulnerability management, and compliance certifications, this paper aims to provide a comparative analysis that informs businesses and developers on the most secure and compliant platform for their needs. [ABSTRACT FROM AUTHOR]

Audio cryptography is the practice of encrypting audio data to prevent illegal access to and listening to it. This paper presents an innovative technique of audio cryptography based on the Python computer language. To ensure secrecy and integrity, the suggested system encrypts and decrypts audio signals using advanced cryptographic techniques. A crucial component of AES, the cryptographic key is dynamically created to improve security. Python's broad library support and ease of use make it an ideal platform for implementing the AES algorithm, which ensures dependable and effective audio data encryption. The system utilizes Python's cryptography library for seamless integration and ease of implementation. Simulation results demonstrate the efficacy of the AES algorithm in securely encrypting and decrypting audio data with reduced noise compared to traditional methods. [ABSTRACT FROM AUTHOR]

Massive amounts of real-time streaming network data are generated quickly because of the exponential growth of applications. Analyzing patterns in generated flow traffic streaming offers benefits in reducing traffic congestion, enhancing network management, and improving the quality of service management. Processing massive volumes of generated traffic poses more challenges when data traffic encryption is raised. Classifying encrypted network traffic in real-time with deep learning networks has received attention because of their excellent performance. The substantial volume of incoming packets, characterized by high speed and wide variety, puts real-time traffic classification within the domain of big data problems. Classifying traffic with high speed and accuracy is a significant challenge in the era of big data. The real-time nature of traffic intensifies deep learning networks, necessitating a considerable number of parameters, layers, and resources for optimal network training. Until now, various datasets have been employed to evaluate the effectiveness of previous methods for classifying encrypted traffic. The primary objective has been to enhance accuracy, precision, and F1-measure. Presently, encrypted traffic classification performance depends on pre-existing datasets. The learning and testing phases are done offline, and more research is needed to investigate the feasibility of these methods in real-world scenarios. This paper examines the possibility of a tradeoff between evaluating the model's effectiveness, execution time, and utilization of processing resources when processing stream-based input data for traffic classification. We aim to explore the feasibility of establishing a tradeoff between these factors and determining optimal parameter settings. This paper used the ISCX VPN-Non VPN 2016 public dataset to evaluate the proposed method. All packets from the dataset were streamed continuously through Apache Kafka to the classification framework. Numerous experiments have been designed to demonstrate the efficacy of the proposed method. The experimental results show that the proposed method outperforms the baseline methods by 11% in the F1-measure when the number of workers is two and by 25% when the number of workers is equal to 32. [ABSTRACT FROM AUTHOR]

All messaging platforms rely on a centralized server which has the vulnerability of getting hacked by outside agencies and posing risk to private data. Authentication between users is an important asset in electronic messaging today, which is one of the most widely used network applications. By replacing the need for reliable mediators, blockchain technology overcomes these threats and allows for a reduction in power-intensive operations. In this work, we propose a blockchain-based solution for secure messaging. Blockchain-based communication can increase communication effectiveness and security, and this paper proposes the creation of a blockchainbased messaging model that will improve the performance and security of blockchain-recorded data, with a smart contract to verify ownership. The system is still fully distributed and allows users to exchange messages securely. This paper has tried to implement a blockchain with a few users and implemented secure end-to-end encryption using the SHA-256 algorithm and multiple crypto techniques so that messages cannot be vulnerable. [ABSTRACT FROM AUTHOR]

A cryptography algorithm for data transfer encryption provides confidentiality, requires considerable computing power, and is not commonly implemented in embedded systems with limited computing power, such as Programmable Logic Controller (PLC). PLC is the core component for automation and control in industrial automation. For decades, PLC has prioritized speed over security; program execution in PLC must be as efficient as possible. The cryptography algorithm uses a seed, the initialization vector, to encrypt the data with the cryptography key to strengthen the encryption. Pseudo Random Number Generator (PRNG) can be used as the initialization vector. This paper proposes the XORasm PRNG algorithm, the lightweight XORshift-based algorithm with a modified seed from the system's clock. The applied methodology generates and visualizes PRNG, tests the randomness, and implements the PRNG on compact PLC. XORasm is evaluated statistically with runs-test in simulation by comparing the algorithm to one of the simulated compact PLC's PRNG implementations. The findings from this research are that p-values demonstrate that XORasm is statistically and significantly more random than the current implementation, and there is evidence that XORasm's generated data distribution is practically random at a 99.95% confidence level, suitable for implementation in embedded systems as a lightweight PRNG. [ABSTRACT FROM AUTHOR]

Data outsourcing has become more and more popular due to its low cost and flexibility. However, there is a problem that the cloud server used to store data is partially trusted. Searchable encryption is an efficient technology that is devoted to helping people conduct accurate searches without leaking information. Nonetheless, most existing schemes cannot support dynamic updates or meet the privacy requirements of all users. There have been some experiments to solve these issues by implementing a dynamically searchable asymmetric encryption scheme. This paper proposes an efficient searchable encryption scheme based on the Authenticator Bloom Filter (ABF). The solution can support dynamic updates and multiple users and meet forward and backward security. This paper uses an ABF to improve the efficiency of searches and updates while playing a significant role in dynamic updates. This paper designs a new token encryption scheme and file set encryption scheme, which not only helps users reduce time in searches and updates but also supports multi-user modes. Experiments show that the proposed scheme takes less time in searching and updating algorithms, especially when the keyword does not exist. The solution also takes into account the problem of history storage when updating, which reduces the unnecessary consumption of memory and avoids multiple storage states for the same file. [ABSTRACT FROM AUTHOR]

Purpose: The rapid advancement of digital technologies has necessitated robust security measures to protect information systems against escalating cyber threats. The objective is to study the effectiveness of the command line interface (CLI) in IT system security management. Design/methodology/approach: This paper explores the efficacy of the command line interface (CLI) in managing IT system security and examines the potential of integrating biometric data into cryptographic mechanisms. We delve into the CLI's precision and flexibility, which enable the execution of complex security tasks and its seamless integration with advanced security tools. Furthermore, we investigate the incorporation of biometrics, such as fingerprints and facial recognition, into encryption processes, offering enhanced security by binding access to individual biometric identifiers. Findings: Our findings suggest that while CLI remains a vital tool for security specialists, the convergence of CLI with biometric authentication can significantly fortify the security of information systems. Practical implications: The paper addresses the challenges and opportunities presented by this integration, including privacy concerns and the need for secure handling of biometric data. We also discuss the implications of such technologies in the context of the European Union's legal framework on cybersecurity. Originality/value: The article is aimed at those involved in cyber security management. The article presents the possibility of using biometric attestations to support the security of IT systems. [ABSTRACT FROM AUTHOR]

Quantum computing advancements pose security challenges for cryptography. Specifically, Grover's search algorithm affects the reduction in the search complexity of symmetric-key encryption and hash functions. Recent efforts have been made to estimate the complexity of Grover's search and evaluate post-quantum security. In this paper, we propose a depth-optimized quantum circuit implementation for ASCON, including both symmetric-key encryption and hashing algorithms, as a part of the lightweight cryptography standardization by NIST (National Institute of Standards and Technology). As far as we know, this is the first implementation of a quantum circuit for the ASCON AEAD (Authenticated Encryption with Associated Data) scheme, which is a symmetric-key algorithm. Also, our quantum circuit implementation of the ASCON-HASH achieves a reduction of more than 88.9% in the Toffoli depth and more than 80.5% in the full depth compared to the previous work. As per our understanding, the most effective strategy against Grover's search involves minimizing the depth of the quantum circuit for the target cipher. We showcase the optimal Grover's search cost for ASCON and introduce a proposed quantum circuit optimized for depth. Furthermore, we utilize the estimated cost to evaluate post-quantum security strength of ASCON, employing the relevant evaluation criteria and the latest advancements in research. [ABSTRACT FROM AUTHOR]

Purpose: The rise of cryptocurrencies and other digital assets has triggered concerns about regulation and security. Governments and regulatory bodies are challenged to create frameworks that protect consumers, combat money laundering and address risks linked to digital assets. Conventional approaches to confiscation and anti-money laundering are deemed insufficient in this evolving landscape. The absence of a central authority and the use of encryption hinder the identification of asset owners and the tracking of illicit activities. Moreover, the international and cross-border nature of digital assets complicates matters, demanding global coordination. The purpose of this study is to highlight that the effective combat of money laundering, legislative action, innovative investigative techniques and public–private partnerships are crucial. Design/methodology/approach: The focal point of this paper is Australia's approach to law enforcement in the realm of digital assets. It underscores the pivotal role of robust confiscation mechanisms in disrupting criminal networks operating through digital means. The paper firmly asserts that staying ahead of the curve and maintaining an agile stance is paramount. Criminals are quick to embrace emerging technologies, necessitating proactive measures from policymakers and law enforcement agencies. Findings: It is argued that an agile and comprehensive approach is vital in countering money laundering, as criminals adapt to new technologies. Policymakers and law enforcement agencies must remain proactively ahead of these developments to efficiently identify, trace and seize digital assets involved in illicit activities, thereby safeguarding the integrity of the global financial system. Originality/value: This paper provides a distinctive perspective by examining Australia's legal anti-money laundering and counterterrorism financing framework, along with its law enforcement strategies within the realm of the digital asset landscape. While there is a plethora of literature on both asset confiscation and digital assets, there is a noticeable absence of exploration into their interplay, especially within the Australian context. [ABSTRACT FROM AUTHOR]

Natural Language Processing holds immense potential for extracting insights from healthcare data, but it demands stringent privacy protection. Privacy-preserving NLP techniques, notably Fully Homomorphic Encryption (FHE), offer a path to advanced analytics while preserving patient data confidentiality. This paper marks the culmination of our initial phase, focusing on data preparation and encryption. We’ve employed FHE with the CKKS scheme to ensure data remains encrypted. Performance evaluation adapts multiclass classification metrics, addressing the distinct nature of healthcare data. As we conclude this phase, we emphasize that this is only the beginning. Subsequent steps will focus on developing NLP models trained on encrypted data. Our work highlights the intersection of data privacy and advanced analytics in healthcare, ultimately benefiting healthcare providers and patients. [ABSTRACT FROM AUTHOR]

A high-precision, complex, three-dimensional (3D) geological model can directly express the attributes of stratum thickness, geological structure, lithology and spatial form, which can provide a reliable basis for the development and utilization of underground space and planning decisions. However, it is difficult to perform accurate modelling due to the lack of basic data. As such, this paper proposes coupling a machine learning algorithm (K-nearest neighbour (KNN)) with the kriging algorithm to construct the topological relationship between the Delaunay triangle and the Thiessen polygon in order to perform the simulation and prediction of virtual drilling. Based on KNN, support vector machine (SVM) and neural network algorithms as well as the virtual borehole encryption data, data standardization processing and analysis are carried out. Through model verification, algorithm optimization is realized, and the optimal modelling method is explored. The results show that the fine KNN algorithm improved by Bayesian optimization can effectively improve the modelling accuracy through 0.1-m encryption, standardization processing and 5-fold cross-validation. Stratum modelling combined with the fine KNN and kriging algorithms can obtain a more accurate modelling without adding virtual boreholes. The improved levels of upper and lower hybrid modelling with an appropriate number of profile boreholes can also effectively optimize model accuracy. Both modelling accuracy and efficiency can be significantly improved by using Delaunay triangles and Thiessen polygons with virtual boreholes. Stratum modelling can effectively express the geological pinch-out in areas with adequate degrees of stratification, and hybrid modelling performs well in irregular geological bodies such as karsts and lenses. [ABSTRACT FROM AUTHOR]

In an era characterized by unparalleled levels of data exchange and communication, ensuring the security of network transmissions emerges as a paramount concern. This paper undertakes a comprehensive exploration aimed at bolstering network security through the integration of Chaotic Maps with the robust ChaCha20 encryption algorithm. This pioneering approach seeks to introduce an additional stratum of intricacy and unpredictability to data encryption processes, thereby augmenting the confidentiality and integrity of information in transit. The investigation delves deeply into the theoretical underpinnings, practical implementation intricacies, thorough security analysis, and diverse real-world applications of this enhanced encryption framework. By offering a multifaceted examination, this study provides invaluable insights and guidance not only for network professionals but also for researchers operating within this dynamic field. Through its rigorous examination and innovative proposals, this paper contributes significantly to the ongoing discourse surrounding network security enhancement strategies. [ABSTRACT FROM AUTHOR]

In recent years, the internet and smart devices have developed rapidly. Many people no longer rely on newspapers, magazines, or television to receive news. They can see the latest news using computers or mobile phones. According to a study by the Taiwan Internet Information Center, nearly 90% of Taiwanese people have used the internet. Many online streaming services have emerged, and people can easily watch movies and TV programs through computers or mobile phones. Hence, some websites use digital copyright management mechanisms to protect videos from being directly downloaded. However, 30% of websites use AES-128 encryption to protect their content. If the key access mechanism is not well protected, the encryption methodology may be useless. Therefore, this paper proposes a cross-platform digital copyright management mechanism for adaptive streaming. With this mechanism, users do not need to download additional applications, as the mechanism implements Web-Assembly language through the browser. [ABSTRACT FROM AUTHOR]

In this paper, we propose a three-dimensional (3D) optical encryption technique for simultaneous two-primary data using double random-phase encryption (DRPE). In conventional DRPE, the primary data can be encrypted through two different random phase masks optically. Thus, its speed is the same as the speed of light. However, in this method, each primary dataset can be decrypted by the individual key data. For simultaneous two primary dataset such as stereo images or multi-view images, a new encryption technique is required. Therefore, in this paper, we encrypt the simultaneous two different primary datasets by DRPE. In our method, the first and second primary data are regarded as the amplitude and phase with single key data for encryption. To verify the feasibility of our method, we implement the simulation and measure the performance metrics such as thw peak signal to noise ratio (PSNR) and the peak sidelobe ratio (PSR). As a result, PSNR values of two-dimensional decryption results for the first ("LENA" text) and second (lena image) primary data by our proposed method with the correct and incorrect key data are 311.0139, 41.9609, 12.0166, and 7.4626, respectively, since the first primary data are lossless, and the second primary data are lossy. For 3D reconstruction, PSR values of the first and second primary data are 914.2644 and 774.1400, respectively. [ABSTRACT FROM AUTHOR]

The paper presents a traceability framework founded upon a methodological approach specifically designed for the integration of the IOTA-based distributed ledger within the mining industry. This framework constitutes an initial stride towards the certification and labelling of sustainable material production. The efficacy of this methodology is subject to real-world evaluation within the framework of the European Commission funded project DIG_IT. Within the architectural framework, the integration of decentralized identifiers (DIDs) and the IOTA network are instrumental in effecting the encryption of data records, with associated hashes securely anchored on the explorer. Recorded environmental parameters, encompassing metrics such as pH level, turbidity, electrical conductivity, and emissions, serve as tangible evidence affirming their adherence to prevailing regulatory standards. The overarching system architecture encompasses a sophisticated Industrial Internet of Things platform (IIoTp), facilitating the seamless connection of data from a diverse array of sensors. End users, including governmental entities, mining managers, and the general public, stand to derive substantial benefits from tailored dashboards designed to facilitate the validation of data for emission compliance. [ABSTRACT FROM AUTHOR]

With the advent of 6G Narrowband IoT (NB-IoT) technology, IoT security faces inevitable challenges due to the application requirements of Massive Machine-Type Communications (mMTCs). In response, a 6G base station (gNB) and User Equipment (UE) necessitate increased capacities to handle a larger number of connections while maintaining reasonable performance during operations. To address this developmental trend and overcome associated technological hurdles, this paper proposes a hardware-accelerated and software co-designed mechanism to support streaming data transmissions and secure zero-trust inter-endpoint communications. The proposed implementations aim to offload processing efforts from micro-processors and enhance global system operation performance by hardware and software co-design in endpoint communications. Experimental results demonstrate that the proposed secure mechanism based on the use of non-repeating keys and implemented in FPGA, can save 85.61%, 99.71%, and 95.68% of the micro-processor's processing time in key block generations, non-repeating checks, and data block transfers, respectively. [ABSTRACT FROM AUTHOR]

Innovations in wearable technology and artificial intelligence have enabled consumer devices to process and transmit data about human mental states (cognitive, affective, and conative) through what this paper refers to as "cognitive biometrics." Devices such as brain-computer interfaces, extended reality headsets, and fitness wearables offer significant benefits in health, wellness, and entertainment through the collection and processing and cognitive biometric data. However, they also pose unique risks to mental privacy due to their ability to infer sensitive information about individuals. This paper challenges the current approach to protecting individuals through legal protections for "neural data" and advocates for a more expansive legal and industry framework, as recently reflected in the draft UNESCO Recommendation on the Ethics of Neurotechnology, to holistically address both neural and cognitive biometric data. Incorporating this broader and more inclusive approach into legislation and product design can facilitate responsible innovation while safeguarding individuals' mental privacy. Biometric data relating to human cognitive, affective, and conative states ("cognitive biometrics") are not adequately protected under existing privacy laws. The authors argue for an expansive approach that would treat neural and cognitive biometric data holistically as categories of "sensitive data" and provide them with analogous privacy protections. [ABSTRACT FROM AUTHOR]

This article addresses the pervasive issue of digital piracy in the movie industry. Despite substantial efforts to combat piracy, its impact remains significant, resulting in substantial financial losses for companies. Digital rights management (DRM) has been introduced as a countermeasure to counter this challenge. While the security of video applications, including commercial and military videos, has gained prominence in recent research, this paper focuses on enhancing the encryption algorithm and presenting encryption results. Specifically, it explores the AES 128 encryption algorithm's vulnerabilities in the MPEG-DASH media file and proposes a randomized algorithm for video file encryption. To bolster security, the randomized approach leverages three robust algorithms: AES 256, Two-Fish, and Serpent. The randomized algorithm demonstrated significantly higher resistance to brute-force attacks than AES 128 CBC, as exemplified by a test scenario with a key length of 32 and 100 media chunks. By utilizing this combination of unbroken algorithms, the paper offers an efficient solution for offline-protected media storage, ultimately strengthening the security and privacy of the MPEG-DASH DRM system and mitigating the risk of media file piracy. The randomized algorithm consistently outperformed AES 128 CBC in terms of execution time across multiple media files, with the randomized algorithm completing the encryption process in 34,610 milliseconds for Media File 1, compared to AES 128 CBC, which required 59,910 milliseconds, showcasing a notable enhancement in speed. [ABSTRACT FROM AUTHOR]

Protecting sensitive patient data, such as electrocardiogram (ECG) signals, during RF wireless transmission is essential due to the increasing demand for secure telemedicine communications. This paper presents an innovative chaotic-based encryption system designed to enhance the security and integrity of telemedicine data transmission. The proposed system utilizes a multi-scroll chaotic system for ECG signal encryption based on master–slave synchronization. The ECG signal is encrypted by a master system and securely transmitted to a remote location, where it is decrypted by a slave system using an extended state observer. Synchronization between the master and slave is achieved through the Lyapunov criteria, which ensures system stability. The system also supports Orthogonal Frequency Division Multiplexing (OFDM) and adaptive n-quadrature amplitude modulation (n-QAM) schemes to optimize signal discretization. Experimental validations with a custom transceiver scheme confirmed the system's effectiveness in preventing channel overlap during 2.5 GHz transmissions. Additionally, a commercial RF Power Amplifier (RF-PA) for LTE applications and a development board were integrated to monitor transmission quality. The proposed encryption system ensures robust and efficient RF transmission of ECG data, addressing critical challenges in the wireless communication of sensitive medical information. This approach demonstrates the potential for broader applications in modern telemedicine environments, providing a reliable and efficient solution for the secure transmission of healthcare data. [ABSTRACT FROM AUTHOR]

An AG-group is an algebraic structure that satisfies the left invertive law and includes its left identity and inverse elements. This framework is naturally suited for representing and manipulating various real-life applications in civil engineering, information engineering, artificial intelligence, and more. In this paper, we present the structural application of AG-groups in data encryption and in the rotational transformation of water molecules. Additionally, numerous authors have explored fuzzy set theory over AG-group systems, yielding many valuable results. Spherical fuzzy set theory, an extension of fuzzy set theory, allows for the representation of uncertainty in decision-making, where there may be multiple possible choices or uncertainty about the degree of membership of an element in a set. However, no prior work has combined spherical fuzzy set theory with AG-groups, which motivates our study. In this paper, we apply spherical fuzzy set theory to AG-groups and introduce the concept of spherical fuzzy AG-subgroupoids. We provide the characterization of spherical fuzzy AG-subgroupoids over AG-groupoids and examine their related properties. As an application of spherical fuzzy sets over AG-groups, we develop the concept of normal spherical fuzzy groups over AG-groups and investigate their various properties. [ABSTRACT FROM AUTHOR]

Privacy-enhancing techniques and protocols for data aggregation and analytics in wireless networks require the development of novel methods for efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active attackers. Previous approaches to secure privacy-preserving computation of distributed queries incur significant communication overhead and cannot be applied to big data. This paper proposes two solutions to the problem of efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active outsider attackers for a new large class of distributed statistical or numerical queries. This class contains many useful statistics and is larger than other classes considered in the literature previously. We propose two protocols for the Protection of data from Active Attackers (PAA) in the case of distributed privacy-preserving computation: PAA applying Shamir's Secret Sharing (PAA-SSS) and PAA applying homomorphic encryption (PAA-HE). The PAA-HE protocol combines the use of ElGamal and Paillier encryption schemes in one system. Theoretical analysis and experimental results show that both protocols outperform alternative approaches. PAA-HE provides stronger protection and is more efficient than PAA-SSS. [ABSTRACT FROM AUTHOR]

The Advanced Metering Infrastructure (AMI), as a crucial subsystem in the smart grid, is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers. However, with the advancement of information and communication technology, new security and privacy challenges have emerged for AMI. To address these challenges and enhance the security and privacy of user data in the smart grid, a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance (HPPM-AMICFA) is proposed in this paper. The proposed model integrates cloud and fog computing with hierarchical threshold encryption, offering a flexible and efficient privacy protection solution that significantly enhances data security in the smart grid. The methodology involves setting user protection levels by processing missing data and utilizing fuzzy comprehensive analysis to evaluate user importance, thereby assigning appropriate protection levels. Furthermore, a hierarchical threshold encryption algorithm is developed to provide differentiated protection strategies for fog nodes based on user IDs, ensuring secure aggregation and encryption of user data. Experimental results demonstrate that HPPM-AMICFA effectively resists various attack strategies while minimizing time costs, thereby safeguarding user data in the smart grid. [ABSTRACT FROM AUTHOR]

In ciphertext-policy attribute-based encryption, there might be different levels of overlapping in the access policies of different data objects outsourced by the same data owner. This paper proposes a soft set decision-making method and cluster percolation method-based policy clustering by using policy similarity for CP-ABE, aiming to merge the duplicated access policy pieces to reduce repeated computations during the encryption process of corresponding data objects. Firstly, the access policies are clustered using either the soft set decision-making or the cluster percolation method. Secondly, the access policies within the same cluster are integrated for further encryption of corresponding data objects as a whole, thereby preventing redundant computations during the encryption process and thus reducing computational overhead. Theoretical analysis and experimental results demonstrate the feasibility and effectiveness of the proposed approach in this paper. [ABSTRACT FROM AUTHOR]

Supply chain management has become increasingly complex, leading to information loss and delayed transmission, exacerbating the bullwhip effect. Furthermore, the need for more reliable information storage, traceability, and accountability has slowed supply chain management. In this context, blockchain technology (BCT) solves the bullwhip effect problem, as it supports distributed networking, information synchronization among nodes, digital encryption, traceable information, and unchangeable block content. This paper proposes using public blockchain to solve the bullwhip effect problem in the supply chain (SC), which has yet to be discussed in published academic research papers. To this end, a framework has been developed to determine the readiness of a supply chain for blockchain adoption. This framework includes a checklist to evaluate the motivation, timing, and appropriate platform for adopting blockchain. The framework has been validated using a case study of personal computer devices, which were affected by the Covid-19 pandemic and resulted in semiconductors supply chain disruption. The study involved polling four executives The study involved polling four executives (of large organizations in Egypt and some Middle Eastern countries) representing different stages in the supply chain, they are retail, distribution, manufacturing, and supply. The results showed that while the executives recognized the benefits of adopting blockchain, they were still determining if it was the right time for adoption and did not see the technology ready for their supply chains. Finally, some public blockchain platforms were introduced to help businesses choose the most suitable platform for adoption. [ABSTRACT FROM AUTHOR]

Due to the wide variety of distant information transmission, data encryption has become an absolute necessity for practically all data transaction applications. Sensitive data gets uploaded to the cloud every day through many methods. AES algorithm is now the best option for many applications that require security services. Because of this, a lot of research has been carried out to improve the performance and efficiency of the algorithm. This paper's main goal is to propose an improved method for encrypting and decrypting data. As key sharing was a significant problem in the symmetric approach, we generate the key using the ECC and will encrypt and decrypt data using the AES using this key. The suggested algorithm, which is simpler than ECC and solely utilized for key generation-not data encryption or decryption-is more secure than AES because it circumvents the key-sharing issue that plagued AES. [ABSTRACT FROM AUTHOR]

Nowadays the cloud computing environment is widely utilized for transmitting and receiving data securely. Inorder to secure the data the encryption method is used but still due to some limitations the security process is diminished. Therefore, this paper proposes a new algorithm to provide better security while transmitting data through the network. At first, the sensitivity of data is determined using a lightweight convolutional neural network (LWCNN) model which is used to categorize the unclassified data into two categories normal sensitive data and highly sensitive data. After determining the level of data sensitivity, the encryption process is performed further. The efficient hash function-based duplication detection approach is employed to maintain confidential information before outsourcing it to a cloud server. Subsequently, the ideal keys are generated for each data based on its sensitivity level using the proposed fuzzy tuna swarm (FTS) algorithm. Finally, the data is encrypted by converting plain text into ciphertext which is only visible to authorized users. The experimental results show that the LWCNN model utilized for data sensitivity classification achieved 94% accuracy and the FTS algorithm proposed for optimal key generation took much less communication time of about 1800 µs than other compared techniques. [ABSTRACT FROM AUTHOR]

This paper delves into the realm of steganography, focusing on the evolution of techniques employed to conceal data within digital images. Beginning with an exploration of the motivations behind information security and the role of cryptography, the paper introduces adaptive steganography as a discreet means of incorporating private data into a cover medium. The discussion then shifts to the three key factors in image steganography - resilience, capacity, and imperceptibility - forming the foundation of effective data hiding. The spatial and frequency domain methods are compared, with specific emphasis on the classical Least Significant Bit insertion and more advanced adaptive steganography techniques. The paper further introduces edge-based steganography, highlighting the advantage of manipulating edge areas for increased imperceptibility. Moving beyond definitions and types, the paper provides a comprehensive analysis of notable research works in the field, elucidating their objectives, methodologies, results, strengths, and limitations. The conclusion reflects on the dynamic landscape of steganography, acknowledging both achievements and areas for improvement. [ABSTRACT FROM AUTHOR]

Over the years, there has been a significant surge in the popularity of instant messaging applications (IMAs). However, the message notification functionality in IMAs exhibits certain limitations. Some IMAs fail to alert users about new messages after their phone restarts unless they unlock the phone. This is a consequence of end-to-end encryption (E2EE) and the app not knowing the message is in the queue until the app decrypts it. This approach using E2EE is used to prevent offline attacks, as the key is unavailable to decrypt the notification messages. In this paper, we introduce a novel design and implementation of a message notification protocol for IMAs based on the Signal protocol. The proposed protocol aims to securely display notifications on a locked device and ensures that cryptographic keys are stored in a location that is isolated from the user's device to prevent offline attacks. This approach enhances the security of private key storage, safeguarding private keys against various external threats. The innovative design strengthens the off-site key management system, rendering it resilient against offline attacks and mitigating the risk of key compromise. Additionally, the proposed protocol is highly efficient, requiring no specialized hardware for implementation. It offers confidentiality of cryptographic keys and protection against offline attacks, further enhancing the overall security of the system. We evaluate the protocol's effectiveness by analyzing multiple independent implementations that pass a suite of formal tests via ProVerif. [ABSTRACT FROM AUTHOR]

In this paper, we perform neural cryptanalysis on five block ciphers: Data Encryption Standard (DES), Simplified DES (SDES), Advanced Encryption Standard (AES), Simplified AES (SAES), and SPECK. The block ciphers are investigated on three different deep learning-based attacks, Encryption Emulation (EE), Plaintext Recovery (PR), Key Recovery (KR), and Ciphertext Classification (CC) attacks. The attacks attempt to break the block ciphers in various cases, such as different types of plaintexts (i.e., block-sized bit arrays and texts), different numbers of round functions and quantity of training data, different text encryption methods (i.e., Word-based Text Encryption (WTE) and Sentence-based Text Encryption (STE)), and different deep learning model architectures. As a result, the block ciphers can be vulnerable to EE and PR attacks using a large amount of training data, and STE can improve the strength of the block ciphers, unlike WTE, which shows almost the same classification accuracy as the plaintexts, especially in a CC attack. Moreover, especially in the KR attack, the Recurrent Neural Network (RNN)-based deep learning model shows higher average Bit Accuracy Probability than the fully connected-based deep learning model. Furthermore, the RNN-based deep learning model is more suitable than the transformer-based deep learning model in the CC attack. Besides, when the keys are the same as the plaintexts, the KR attack can perfectly break the block ciphers, even if the plaintexts are randomly generated. Additionally, we identify that DES and SPECK32/64 applying two round functions are more vulnerable than those applying the single round function by performing the KR attack with randomly generated keys and randomly generated single plaintext. [ABSTRACT FROM AUTHOR]

Cloud computing technology offers flexible and expedient services that carry a variety of profits for both societies as well as individuals. De-duplication techniques were developed to minimize redundant data in the cloud storage. But, one of the main challenges of cloud storage is data deduplication with secure data storage.To overcome the issue, we propose Boneh Goh Nissim Bilinear Attribute-based Optimal Cache Oblivious (BGNBA-OCO) access control and secure de-duplication for data storage in cloud computing in this paper. The proposed method achieves fne-grained access control with low computation consumption. We design Boneh Goh Nissim Privacy Preserving Revocable Attribute-based Encryption that reinforces attribute revocation and averts the discharge of sensitive information. Furthermore, we utilize Optimal Cache Oblivious algorithm to prevent disclosure of access patterns to hide the access patterns in cloud storage via rand pattern matching. We support updating both encrypted data and access control policies to minimize communication and computation overhead of data duplication and encryption processes concurrently. We perform secure data sharing to achieve higher data confidentiality and integrity. Finally, we conducted the extensive experiments in cloud and the results illustrated that our proposed BGNBA-OCO method is more efficient than related works. [ABSTRACT FROM AUTHOR]

In this paper, we propose a camera projection approach to enhance the physical layer security of coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. The data are converted to the new location by the camera projection module in the encryption system, where the 5D hyperchaotic system provides the keys for the camera projection module. The simulated 16QAM CO-OFDM security system over 80 km SSMF is shown to provide a key space of about 9 × 1090 through the five-dimensional (5D) hyperchaotic system, making it impossible for eavesdroppers to obtain valid information, and the peak-to-average power ratio (PAPR) is reduced by about 0.8 dB. [ABSTRACT FROM AUTHOR]

In the face of advancing quantum computing capabilities posing significant threats to current cryptographic protocols, the need for post-quantum cryptography has become increasingly urgent. This paper presents a comprehensive analysis of the performance of various post-quantum cryptographic algorithms specifically applied to digital signatures. It focuses on the implementation and performance analysis of selected algorithms, including CRYSTALS-Dilithium, Falcon, and SPHINCS+, using the liboqs library. Performance tests reveal insights into key pair generation, file signing, and signature verification processes. Comparative tests with the well-known and popular RSA algorithm highlight the trade-offs between security and time efficiency. The results can help to select secure and efficient ciphers for specific 5G/6G services. [ABSTRACT FROM AUTHOR]

With the rapid construction and implementation of smart grid, lots of studies have been conducted to explore how to ensure the security of information privacy. At present, most privacy-preserving data aggregation schemes in smart grid achieve privacy data protection through homomorphically encrypted data aggregation. However, these data aggregation schemes tend to rely on a trusted third party (TTP), and fail to efficiently handle the case of a meter failure. Besides, they are less flexible for overall user management, and resistance to collusion attacks needs to be improved. In this paper, we propose an efficient and robust privacy-preserving data aggregation scheme without TTP, called EFTA. Overall, the scheme eliminates the reliance on a TTP, combines with Shamir threshold secret sharing scheme to increase overall fault tolerance, supports flexible and dynamic user management, and effectively defends against entity initiated collusion attacks. According to security and performance analysis results, the scheme proposed in this paper meets the multiple security requirements of smart grid, and is more efficient in terms of overall overhead compared to the existing privacy-preserving data aggregation schemes. [ABSTRACT FROM AUTHOR]

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Medical data sharing is pivotal in enhancing accessibility and collaboration among healthcare providers, researchers, and institutions, ultimately leading to enhanced patient outcomes and more efficient healthcare delivery. However, due to the sensitive nature of medical information, ensuring both privacy and confidentiality is paramount. Access control-based data sharing methods have been explored to address these issues, but data privacy concerns still remain. Therefore, this paper proposes a secure and privacy-preserving data sharing scheme that achieves an equilibrium between data confidentiality and privacy. By leveraging key aggregate encryption and private set intersection techniques, our scheme ensures secure data sharing while protecting against the exposure of sensitive information related to data. We conduct informal and formal security analyses, including Burrow–Abadi–Needham logic and Scyther, to demonstrate its resilience against potential adversarial attacks. We also implement the execution time for cryptographic operations using multiprecision integer and a rational arithmetic cryptographic library and perform comparative analysis with existing related schemes in terms of security, computational cost, and time complexity. Our findings demonstrate a high level of security and efficiency, demonstrating that the proposed scheme contributes to the field by providing a solution that protects data privacy while enabling secure and flexible sharing of medical data. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) employs various devices with varying hardware capabilities, including those with restricted resources like wireless sensor networks and those with ample resources like satellites. One of the primary hurdles is developing a streamlined encryption algorithm suitable for IoT devices with limited hardware capabilities. This paper introduces an enhanced lightweight algorithm that not only addresses side-channel vulnerabilities but also guards against nonce misuse attacks In this work, we present a design that generates encryption keys using chaotic systems, thereby increasing their unpredictability and randomness. The primary objective of this research is to fortify security measures against a range of novel attack techniques, guaranteeing comprehensive defense, unpredictability, and resilience. The aim of putting strategic defenses and tactics into place is to shield valuable assets from possible threats. [ABSTRACT FROM AUTHOR]

In recent years, Censorship and anti-censorship technology is developing rapidly. The censorship and surveillance systems track traffic on the network; they identify protocols and traffic, block malicious and illegal transportation, and keep the network under control. It can protect the security of network users but also bring risks to their private security, especially that of users chat. In this paper, we introduce MuChat, a free convert chat application for instant communication on mobile, which is designed as censorship as well as surveillance-resistant platform for coverting communication in the censorship region. To evade detection by Internet censors, MuChat uses multi-instant messaging channels provided by public companies to transform messages. It applies end-to-end encryption for information security and transmits data covertly by steganography technology for individual privacy. It also provides perfect forward security protection of users' historical messages. We design and implement MuChat, with this report on the security and performance of MuChat. The evaluation shows that MuChat has proved its high security in terms of running against active attacks, passive attacks, and traffic analysis methods in recent years. It supports all the functions of instant messages with low latency. For short words, it takes only 1–2 s while the base channels take about 0.5 s, and for 20-megabyte files messages with 35–45 s while about 30–40 s for base channels. In our view, this meets the instant communication needs of users. Besides, its architecture can fully support any other public communication channels which can be added by the user. [ABSTRACT FROM AUTHOR]

In the rapid evolving digital landscape, the imperative to ensure data security has never been more crucial. This paper addresses the pressing challenges in data security by introducing a file encryption management system, leveraging a modified advanced encryption standard (AES) algorithm with reduced round iterations and bit permutation. This system aims to comprehensively secure various file types, providing a dependable solution for file exchange. Our findings reveal substantial improvements in both encryption and decryption processes using the reduced-round permutationbased AES (RRPBA). The adapted algorithm demonstrates a significant 38.8% acceleration in encryption time and a remarkable 44.86% improvement in decryption time, positioning it as a pivotal component for efficient file operations within the management system. Moreover, the throughput assessments showcase a remarkable 33.73% improvement in encryption and 23.72% in decryption, outperforming the original AES, emphasizing the algorithm's superior computational effectiveness, signaling positive implications for future high-performance applications. In conclusion, the study not only addresses critical security challenges but also presents a viable solution with tangible speed advantages for file encryption and decryption processes within digital file management systems. [ABSTRACT FROM AUTHOR]

Developing a security solution for spatial files within today's enterprise Geographical Information System (GIS) that is also usable presents a multifaceted challenge. These files exist in "data silos" of different file server types, resulting in limited collaboration and increased vulnerability. While cloud-based data storage offers many benefits, the associated security concerns have limited its uptake in GIS, making it crucial to explore comparable alternative security solutions that can be deployed on-premise and are also usable. This paper introduces a reasonably usable security solution for spatial files within collaborative enterprise GIS. We explore a Database File System (DBFS) as a potential repository to consolidate and manage spatial files based on its enterprise document management capabilities and security features inherited from the underlying legacy DBMS. These files are protected using the Advanced Encryption Standard (AES) algorithm with practical encryption times of 8 MB per second. The final part focuses on an automated encryption solution with schemes for single- and multi-user files that is compatible with various GIS programs and protocol services. Usability testing is carried out to assess the solution's usability and focuses on effectiveness, efficiency, and user satisfaction, with the results demonstrating its usability based on the minimal changes it makes to how users work in a collaborative enterprise GIS environment. The solution furnishes a viable means for consolidating and protecting spatial files with various formats at the storage layer within enterprise GIS. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and blockchain technology have emerged as increasingly prevalent and influential elements shaping global trends in Information and Communications Technology (ICT). Namely, the synergistic combination of blockchain and AI introduces beneficial, unique features with the potential to enhance the performance and efficiency of existing ICT systems. However, presently, the confluence of these two disruptive technologies remains in a rather nascent stage, undergoing continuous exploration and study. In this context, the work at hand offers insight regarding the most significant features of the AI and blockchain intersection. Sixteen outstanding, recent articles exploring the combination of AI and blockchain technology have been systematically selected and thoroughly investigated. From them, fourteen key features have been extracted, including data security and privacy, data encryption, data sharing, decentralized intelligent systems, efficiency, automated decision systems, collective decision making, scalability, system security, transparency, sustainability, device cooperation, and mining hardware design. Moreover, drawing upon the related literature stemming from major digital databases, we constructed a timeline of this technological convergence comprising three eras: emerging, convergence, and application. For the convergence era, we categorized the pertinent features into three primary groups: data manipulation, potential applicability to legacy systems, and hardware issues. For the application era, we elaborate on the impact of this technology fusion from the perspective of five distinct focus areas, from Internet of Things applications and cybersecurity, to finance, energy, and smart cities. This multifaceted, but succinct analysis is instrumental in delineating the timeline of AI and blockchain convergence and pinpointing the unique characteristics inherent in their integration. The paper culminates by highlighting the prevailing challenges and unresolved questions in blockchain and AI-based systems, thereby charting potential avenues for future scholarly inquiry. [ABSTRACT FROM AUTHOR]

Secure communication techniques are important due to the increase in the number of technology users across the world. Likewise, a more random encryption algorithm suitable to secure data from unauthorised users is highly expected. This paper proposes a two-parameter nonlinear chaos map that is sensitive to the trio seed (s0, α, λ) and has better information encryption. We introduce the parameter a to linearise the conventional chaos system, which in turn brings a delay in the cryptosystems. The delay is a phenomenon that changes the chaotic features of a system. A small delay in the system leads to more aperiodicity and the unpredictability of the chaotic attractions. We normalise the new chaos map and use the Lipschitz and pseudo-contractive operators to obtain its irregularity region in Hilbert spaces. We also analyse the chaos map in terms of trajectory, Lyapunov exponent, complexity, and information entropy. Results obtained show that the new chaos map has a wide chaotic range and better statistical properties. It also maintains low complexity due to its linearity and produces more key spaces than most existing chaotic maps. [ABSTRACT FROM AUTHOR]

Researchers have created cryptography algorithms that encrypt data using a public or private key to secure it from intruders. It is insufficient to protect the data by using such a key. No research article has identified an algorithm capable of protecting both the data and the associated key, nor has any mechanism been developed to determine whether access to the data is permissible or impermissible based on the authentication of the key. This paper presents a WEDEx-Kerberotic Framework for data protection, in which a user-defined key is firstly converted to a cipher key using the "Secure Words on Joining Key (SWJK)" algorithm. Subsequently, a WEDEx-Kerberotic encryption mechanism is created to protect the data by encrypting it with the cipher key. The first reason for making the WEDEx-Kerberotic Framework is to convert the user-defined key into a key that has nothing to do with the original key, and the length of the cipher key is much shorter than the original key. The second reason is that each ciphertext and key value are interlinked. When an intruder utilizes the snatching mechanism to obtain data, the attacker obtains data or a key unrelated to the original data. No matter how efficient the algorithm is, an attacker cannot access the data when these methods and algorithms are used to protect it. Finally, the proposed algorithm is compared to the previous approaches to determine the uniqueness of the algorithm and assess its superiority to the previous algorithms. [ABSTRACT FROM AUTHOR]

With the rapid development of the information age, smart meters play an important role in the smart grid. However, there are more and more attacks on smart meters, which mainly focus on the identity authentication of smart meters and the security protection of electricity consumption data. In this paper, an efficient lightweight smart meter authentication scheme is proposed based on the Chinese Remainder Theorem (CRT), which can realize the revocation of a single smart meter user by publishing a secret random value bound to the smart meter identity. The proposed scheme not only protects the security of smart meter electricity consumption data by using encryption, but also resists identity attacks from both internal and external adversaries by using hash functions and timestamps. Experiment shows that the proposed scheme has lower computation overhead and communication overhead than other authentication schemes and is more suitable for smart meter authentication. [ABSTRACT FROM AUTHOR]

MOTIVATION: With the enhancement of people's awareness of the protection of personal privacy information, how to provide better services on the premise of protecting users' privacy has become an urgent problem to be solved. Therefore, it is a necessary motivation to build a network intelligent platform for privacy protection and integrated big data mining. OBJECTIVE: In view of the existing network platform of data privacy leakage, low efficiency of data mining and user satisfaction is not high, this paper will adopt advanced privacy technology, to ensure the confidentiality of users' personal information and security, to enhance the user trust and use experience, to better meet the needs of users. METHODS: In order to better protect the privacy of users, the network intelligent platform should adopt more advanced privacy protection technology. This paper uses the differential privacy algorithm to reduce the risk of data leakage and abuse, and ensure the accuracy and efficiency of data analysis and mining. In the design of the platform, the performance of the platform is fully taken into account to realize the secure storage and efficient processing of data, with good scalability and flexibility to meet the growing user needs and business needs. The performance of the network intelligent platform is also analyzed by experimental simulation. RESULT: The experimental results of this article indicated that in a network intelligent platform based on privacy protection and integrated big data mining, its data transmission encryption score was 9.5; the data storage encryption score was 9.8; the score of access control mechanism was 9.3; the privacy protection score was 9.6; the response time was 80 ms; the processing speed was 121GB/h; the user satisfaction rating was 6.6. CONCLUSION: This indicated that the network intelligent platform had good platform performance and user friendliness while ensuring data security and privacy protection. It could efficiently conduct data mining and ensure data security and privacy. [ABSTRACT FROM AUTHOR]

To secure sensitive medical records in the healthcare clouds, this paper proposes an End-to-End Encryption (E2EE) to enhance a patient-centric blockchain-based system for electronic health record (EHR) management. The suggested system with a focus on the patient enables individuals to oversee their medical records within various involved parties by authorizing or withdrawing permission for access to their records. Utilizing the inter-planetary file system (IPFS) for record storage is chosen due to its decentralized nature and its ability to guarantee the unchangeability of records. Then an E2EE enhancement maintains the medical data integrity using dual level-Hybrid encryption: symmetric Advanced Encryption Standard (AES) and asymmetric Elliptic Curve Cryptography (ECC) cryptographic techniques. The proposed system is implemented using the Ethereum blockchain system for EHR data sharing and integration utilizing a web-based interface for the patient and all users to initiate the EHR sharing transactions over the IPFS cloud. The proposed system performance is evaluated in a working system prototype. For different file sizes between 512 KB to 100 MB, the performance metrics used to evaluate the proposed system were the time consumed for generating key, encryption, and decryption. The results demonstrate the proposed system's superiority over other cutting-edge systems and its practical ability to share secure health data in cloud environments. [ABSTRACT FROM AUTHOR]

Cloud Service Providers, exemplified by industry leaders like Google Cloud Platform, Microsoft Azure, and Amazon Web Services, deliver a dynamic array of cloud services in an ever-evolving landscape. This sector is witnessing substantial growth, with enterprises such as Netflix and PayPal heavily relying on cloud infrastructure for various needs such as data storage, computational resources, and various other services. The adoption of cloud solutions by businesses not only facilitates cost reduction but also fosters flexibility and supports scalability. Despite the undeniable advantages, concerns surrounding security and privacy persist in the realm of Cloud Computing. Given that Cloud services are accessible via the internet, there is a potential vulnerability to unauthorized access by hackers or malicious entities from anywhere in the world. A crucial aspect of addressing this challenge is the implementation of robust security measures, particularly focusing on data protection. To safeguard data in the Cloud, a fundamental recommendation is the encryption of data prior to uploading. Encryption should be maintained consistently, both during storage and in transit. While encryption enhances security, it introduces a potential challenge for data owners who may need to perform various operations on their encrypted data, such as accessing, modifying, updating, deleting, reading, searching, or sharing them with others. One viable solution to balance the need for data security and operational functionality is the adoption of Searchable Encryption (SE). SE operates on encrypted data, allowing authorized users to perform certain operations without compromising the security of sensitive information. The effectiveness of SE has notably advanced since its inception, and ongoing research endeavors aim to further enhance its capabilities. This paper provides a comprehensive review of the functionality of Searchable Encryption, with a primary focus on its applications in Cloud services during the period spanning 2019 to 2023. Additionally, the study evaluates one of its prominent schemes, namely Fully Homomorphic Encryption (FHE). The analysis indicates an overall positive trajectory in SE research, showcasing increased efficiency as multiple functionalities are aggregated and rigorously tested. [ABSTRACT FROM AUTHOR]