Protect User Privacy Research Articles

CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication

In the ever-changing world of technology, continuous authentication and comprehensive access management are essential during user interactions with a device. Split Learning (SL) and Federated Learning (FL) have recently emerged as promising technologies for training a decentralized Machine Learning (ML) model. With the increasing use of smartphones and Internet of Things (IoT) devices, these distributed technologies enable users with limited resources to complete neural network model training with server assistance and collaboratively combine knowledge between different nodes. In this study, we propose combining these technologies to address the continuous authentication challenge while protecting user privacy and limiting device resource usage. However, the model’s training is slowed due to SL sequential training and resource differences between IoT devices with different specifications. Therefore, we use a cluster-based approach to group devices with similar capabilities to mitigate the impact of slow devices while filtering out the devices incapable of training the model. In addition, we address the efficiency and robustness of training ML models by using SL and FL techniques to train the clients simultaneously while analyzing the overhead burden of the process. Following clustering, we select the best set of clients to participate in training through a Genetic Algorithm (GA) optimized on a carefully designed list of objectives. The performance of our proposed framework is compared to baseline methods, and the advantages are demonstrated using a real-life UMDAA-02-FD face detection dataset. The results show that CRSFL, our proposed approach, maintains high accuracy and reduces the overhead burden in continuous authentication scenarios while preserving user privacy.

Cybersecurity in Onion Routing Environments: Strategies to Thwart Cyber Threats

Onion routing networks, or darknets, enable anonymous communication, protecting user privacy and attracting cybercriminals. This paper analyzes cybercrime in these networks, including drug trafficking, fraud, and hacking. The challenges seen in the detection and mitigation of these crimes arising as the result of strong anonymity and explore countermeasures such as law enforcement, technological solutions, and policy interventions have been discussed, as also highlighting of the limitations of current measures and proposals for future research, with emphasis on the need for interdisciplinary approaches combining technical, legal, and social perspectives.

Current Trends on Advancement in Smart Textile Device Engineering

AbstractSmart textiles represent a revolutionary approach to wearable technology with applications ranging from healthcare to energy harvesting. This review paper explores the importance of textile technologies and highlights their potential to revolutionize consumer electronics. Conventional technologies are sometimes heavy, and lack comfort and flexibility, but smart textiles seamlessly integrate into everyday clothing, improving wearability and user experience. The article emphasizes the need for sustainable sourcing and environmentally friendly production methods, as well as responsible manufacturing and disposal practices. Manufacturing techniques such as wet spinning, melt spinning, electrostatic spinning, weaving, knitting, and printing are detailed and shed light on their role in incorporating electronics into textiles. Several applications of textile‐based devices are being explored, including biochemical sensing, temperature monitoring, energy harvesting, energy storage, and smart displays. Each application demonstrates the versatility and potential of smart textiles in different areas. Despite optimistic progress, challenges remain, from improving energy efficiency to protecting user privacy and data security. The review analyzes these problems and suggests future improvements, including interdisciplinary collaboration to find new solutions. Finally, an overview of the current state of smart textiles provides the future of this technology. It serves as an in‐depth reference for academics and readers interested in understanding recent advances and discoveries in textile technologies, highlighting the importance of this rapidly growing industry.

PP-LEM: Efficient and Privacy-Preserving Clearance Mechanism for Local Energy Markets

In this paper, we propose a novel Privacy-Preserving clearance mechanism for Local Energy Markets (PP-LEM), designed for computational efficiency and social welfare. PP-LEM incorporates a novel competitive game-theoretical clearance mechanism, modelled as a Stackelberg Game. Based on this mechanism, a privacy-preserving market model is developed using a partially homomorphic cryptosystem, allowing buyers’ reaction function calculations to be executed over encrypted data without exposing sensitive information of both buyers and sellers. The comprehensive performance evaluation demonstrates that PP-LEM is highly effective in delivering an incentive clearance mechanism with computational efficiency, enabling it to clear the market for 200 users within the order of seconds while concurrently protecting user privacy. Compared to the state of the art, PP-LEM achieves improved computational efficiency without compromising social welfare while still providing user privacy protection.

New Media User Privacy Protection Mechanism Based on Differential Privacy and Data Anonymization

With the rapid development of new media, user privacy issues have become increasingly important. New media platforms, such as TikTok, Twitter, and WhatsApp, process vast amounts of user data daily, including personal information, behavioral data, and social relationships. The extensive collection and use of this data present numerous privacy protection challenges. Many users are not fully aware of how their data is collected and used when using new media platforms, lacking informed consent regarding data collection. Consequently, relying on users to take proactive privacy measures to prevent the disclosure of critical information is difficult to achieve. To address this issue, this paper proposes a privacy protection mechanism that combines differential privacy and data anonymization. The mechanism protects query results through differential privacy techniques and hides user identity information using data anonymization techniques, thereby ensuring user privacy during data analysis and processing. This paper conducts necessary experimental analysis on the proposed method, and the results demonstrate its usability and effectiveness.

Exploring security and privacy enhancement technologies in the Internet of Things: A comprehensive review

AbstractIn the era heavily influenced by Internet of Things (IoT), prioritizing strong security and the protection of user privacy is of utmost importance. This comprehensive review paper embarks on a meticulous examination of the multifaceted challenges and risks facing IoT security and privacy. It encompasses the hardware, software, and data‐in‐transit domains, shedding light on potential vulnerabilities and associated threats. In response to these concerns, this paper puts forth recommendations for effective strategies to mitigate these risks. Providing a road‐map for enhancing security and privacy in IoT environments. Furthermore, this review thoroughly assesses a multitude of solutions proposed by various authors, with the primary aim of enhancing security and privacy within the IoT landscape. The analysis provides insights into the strengths and limitations of these solutions. This is aiding in the development of a holistic comprehension of the existing status of IoT security and privacy. Moreover, the paper delves into the complexities surrounding integrating emerging technologies into the IoT framework. It explores the obstacles and challenges inherent in this process and proposes potential strategies to address these hurdles. By doing so, the review provides a holistic perspective on existing security and privacy enhancement technologies and offers guidance on navigating the dynamic landscape of emerging technologies within the IoT domain. Publications included in the review consist of journal articles, conference papers, and book chapters from reputable sources indexed in SCI (Science Citation Index), Scopus, and Web of Science.

Computation Offloading with Privacy-Preserving in Multi-Access Edge Computing: A Multi-Agent Deep Reinforcement Learning Approach

The rapid development of mobile communication technologies and Internet of Things (IoT) devices has introduced new challenges for multi-access edge computing (MEC). A key issue is how to efficiently manage MEC resources and determine the optimal offloading strategy between edge servers and user devices, while also protecting user privacy and thereby improving the Quality of Service (QoS). To address this issue, this paper investigates a privacy-preserving computation offloading scheme, designed to maximize QoS by comprehensively considering privacy protection, delay, energy consumption, and the task discard rate of user devices. We first formalize the privacy issue by introducing the concept of privacy entropy. Then, based on quantified indicators, a multi-objective optimization problem is established. To find an optimal solution to this problem, this paper proposes a computation offloading algorithm based on the Twin delayed deep deterministic policy gradient (TD3-SN-PER), which integrates clipped double-Q learning, prioritized experience replay, and state normalization techniques. Finally, the proposed method is evaluated through simulation analysis. The experimental results demonstrate that our approach can effectively balance multiple performance metrics to achieve optimal QoS.

Application of energy combined thermal comfort in intelligent building management in complex environments

The efficient operation of heating ventilation and air conditioning systems relies on advanced control strategies. However, current control methods are often limited by issues such as uncertain system parameter information and spatial coupling constraints related to the supply rate of the air supply fan. To this end, an energy joint thermal comfort management method for complex environments in multiple regions is proposed. The long-term total cost minimization of the system is established, and then the Lyapunov optimization technology is used to design the distributed control algorithm. Simulation validation shows that the proposed method reduces the energy cost by an average of 11.24% compared to other methods with a thermal discomfort cost coefficient of 0. The average temperature deviation in the area is improved by 0.15 °C and 0.68 °C, respectively. The method saves more than 10% of the total energy cost under different thermal perturbations with an average total temperature deviation of 0.04 °C. The results indicate that the proposed energy joint thermal comfort management method can flexibly balance energy costs and user thermal comfort without knowing any prior information of system parameters, which can also greatly protect user privacy information. This method has application value in the control of heating ventilation and air conditioning systems in complex environments such as commercial buildings.

Privacy-preserving method for sensitive partitions of electricity consumption data based on hybrid differential privacy and k-anonymity

Abstract With the development of smart grid, traditional privacy protection methods are no longer sufficient to cope with the mining and analysing of users’ electricity consumption data, which can easily lead to the leakage of users’ privacy. In this paper, for the challenge of privacy protection of user electricity data in smart grid, we propose a privacy protection method for sensitive partition of electricity data based on hybrid differential privacy and k-anonymity technique, which identifies pattern-specific quasi-identifiers by random forest technique and divides the data into privacy-invasive and non-privacy-invasive partitions, and adopts the Laplacian noise addition (numerical quasi-identifiers) and data generalisation (sub-types of quasi-identifiers) techniques to achieve differential privacy protection for different partitions. Finally, it is experimentally verified that the method performs better in terms of information reduction accuracy and execution time, effectively striking a balance between protecting user privacy and maintaining data utility.

IMPLEMENTASI DESAIN PRIVASI SEBAGAI PELINDUNGAN PRIVASI ATAS DATA BIOMETRIK

Attention to biometric data security has become urgent for protecting user privacy. In the context of the Protection of Data Privacy (PDP) Law, biometric data are classified as specific data, requiring extra protection due to their unique, non-exchangeable characteristics. This study uses a normative approach, analyzing legislation and legal comparisons through regional and international regulations, to examine two issues: the position of biometric data as specific data under the Electronic Information and Transactions Law and PDP Law, and the technical solutions through privacy by design to protect biometric data. The research findings are: (1) Biometric data are correlated with privacy and personal rights, classifying them as specific data. Their use for public and private interests raises the potential for privacy violations. (2) Technical solutions through privacy by design can begin with implementing consent at the registration stage by personal data controllers, ensuring the processing of biometric data achieves specific purposes.

Enhancing User Privacy Protection on Social Media Platforms: A Comprehensive Analysis of Self-Regulatory and Supervisory Mechanisms

Enhancing User Privacy Protection on Social Media Platforms: A Comprehensive Analysis of Self-Regulatory and Supervisory Mechanisms

DCPFS: A Conditional Privacy-Preserving Authentication Scheme with Dual Cuckoo Pseudonym Filters for Vanets

With the increasing frequency of information dissemination and exchange, the attention paid to security and privacy in the Internet of Vehicles (IoV) is also on the rise. To protect user privacy, many anonymous authentication scheme have been proposed. These schemes assign pseudonyms to vehicles to hide their true identities when communicating with other vehicles or Roadside Units (RSUs). However, existing schemes also face pseudonym issues such as pseudonym abuse, high storage overhead, and low query efficiency. Motivated by this, we propose an RSU-based pseudonym assistance management scheme. In this scheme, RSUs employ dual cuckoo pseudonym filters to manage pseudonyms, effectively improving pseudonym query efficiency, preventing pseudonym abuse, and reducing storage overhead. Additionally, security and performance analyses demonstrate that the proposed protocol offers stronger security features and lower overhead. Besides, our scheme has reduced the generation time of pseudonym to only 0.0039 ms.

An identity authentication and key agreement protocol for the internet of vehicles based on trusted cloud-edge-terminal architecture

The continuous progression in cloud computing, edge computing, and associated technologies has notably hastened the progress of vehicle networking technology. This advancement is increasingly assuming a crucial role in enhancing driving safety, optimizing traffic management, and revolutionizing traffic control methodologies. The principal aim of Internet of Vehicles (IoV) technology is to establish a secure, convenient, and efficient novel driving paradigm, enabling intelligent transportation through wireless communication connecting roadside units and vehicles. Nevertheless, this wireless communication method is susceptible to potential attacks, including remote control, information monitoring, and identity simulation. Given this situation, effective authentication is required to address this security concern. Thus, this study proposes an identity authentication and key negotiation protocol grounded in a trusted cloud-edge-terminal architecture. This protocol facilitates mutual authentication, generates secure session keys for communication, guarantees the security of vehicle communication, and supports functionalities including privacy protection and password alteration for vehicle users. Time tree technology is employed for managing the edge nodes, facilitating the sharing of vehicle certification information among these nodes, and enhancing certification efficiency. Formal security analysis and informal security analysis are conducted to demonstrate the security of the proposed protocol, evaluating its security and practicality. Theoretical comparisons and experimental results demonstrate the outstanding computational and communication performance of the proposed protocol.

Fostering social media user intentions: AI-enabled privacy and intrusiveness concerns

Purpose This paper aims to empirically examine the impact of psychological factors (i.e. privacy and intrusiveness concerns) on user intentions regarding artificial intelligence (AI)-enabled social commerce applications at their core through perceived usefulness. The theoretical model is supported by the theory of planned behaviour (TPB). Design/methodology/approach Data was gathered from 488 social media users in Saudi Arabia. Findings Privacy concerns significantly affect perceived usefulness. Furthermore, the link between privacy concerns and behavioural intentions was mediated by perceived usefulness. Research limitations/implications Business leaders should raise users’ awareness about the effectiveness of AI-powered tools that can influence their behavioural intentions. Furthermore, managers must be aware of the regulations that protect user privacy, track online activity and offer secure communication channels. Originality/value This paper expands on TPB by bridging the theoretical and practical divide. It further develops a theoretical framework for practitioners to better understand customers’ physiological aspects of using AI-powered social commerce platforms.

A Privacy-Preserving Friend Matching Scheme Based on Attribute Encryption in Mobile Social Networks

In mobile social networks, users can easily communicate with others through smart devices. Therefore, the protection of user privacy in social networks is becoming a significant subject. To solve this problem, this paper proposes a fine-grained data access control scheme that uses attributes to match friends. In our scheme, the friend-making parties generate friend preference and self-description lists, respectively, realizing attribute hiding by converting friendship preference into ciphertext access control policies and self-description into attribute keys. The social platform matches user profiles to quickly eliminate unmatched users and avoids invalid decryption. In order to reduce the computational burden and communication cost of mobile devices, we adopt an algorithm mechanism for outsourcing decryption. When the user meets the matching conditions, the algorithm outsources the bilinear pair operation with large computation to the friend server. After that, the user finally decrypts the ciphertext. Security analysis shows that our scheme is safe and effective. In addition, performance evaluation shows that the proposed scheme is efficient and practical.

A secure hierarchical deterministic wallet with stealth address from lattices

The concept of Hierarchical Deterministic Wallet (HDW) was introduced by Wuille in Bitcoin Improvement Proposal 32 (BIP32). HDW enables an individual/organization to generate cryptographic keys and subsequently ease the key management problems (e.g., backup and recovery). Since the first HDW algorithm in 2012, HDW has gradually shown its fit for many promising use cases, such as Bitcoin-like cryptocurrencies, global key revocation in FIDO2 standard. In order to achieve all the features (i.e., deterministic derivation, master public key and hierarchy) and the security (i.e., safety of cryptocurrencies and privacy protection of users) requirements for HDW, Yin et al. (ESORICS 2022) conceptualized Hierarchical Deterministic Wallet supporting Stealth Address (HDWSA), and gave a provably secure construction from the standard Computational Diffie-Hellman Assumption. Unfortunately, the construction is not quantum-resistant. In this work, we propose the first HDWSA construction from lattices to fill this gap, we provide the security proof for the construction in the random oracle model (ROM) based on hard problems over lattices. Compared with existing works, to the best of our knowledge, our construction not only captures all the HDW features and security properties, but also provides the potential quantum resistance.

Ethical keylogger solution for monitoring user activities in cybersecurity networks

This paper presents the development of a software keylogger to monitor user activities within a cyber network, enhancing security and facilitating comprehensive cybersecurity analysis. Guided by the agile methodology, the work adopts a flexible and iterative approach to development, ensuring continuous collaboration, adaptation to evolving requirements, and swift response to feedback. Leveraging the versatility and efficiency of the Python programming language, this work transforms the traditional notion of a keylogger, which is a program that records keystrokes, into an ethical and legally compliant tool. By capturing and logging keystrokes, the system provides valuable insights into user behavior and potential security threats, enabling proactive measures to prevent cyber-attacks and data breaches. Prioritizing transparency and consent, the system design adheres to ethical standards and privacy regulations, ensuring the protection of sensitive information and user privacy. This technical solution offers cybersecurity professionals, system administrators, and organizations a valuable tool for network monitoring while showcasing responsible development practices and contributing to the advancement of ethical cybersecurity solutions.

Legal Regulation in the Field of Cybersecurity of Intelligent Transport (Foreign Experience)

Abstract: At present, intelligent transport is a critical part of the modernization of the transport industry around the world. An intelligent transport system combines information technology, electronic technology, data transfer technology and system engineering technology to realize intellectual features, coordinate the elements of traffic system and create an integrated management system for transport, therefore making it a subject of critical information infrastructure. In this regard, data security and its protection from cyber attacks becomes a prerequisite for ensuring reliable and efficient operation of transport systems. In fact, with the development of new energy vehicles and intelligent connected vehicles, in addition to protecting user privacy data, the security of the entire complex of smart transportation data becomes extremely important for the automotive industry, since the security concerns of their networks and data are already closely related to public safety. In this regard, many countries recognize the need for security regulations governing intelligent transport systems. The People's Republic of China is no exception in this sense. Within the country, the security system for the Internet of Vehicles (IoV) is based on regulations at various levels, which are constantly being improved as intelligent transport system develops.

STP-KDE: A spatiotemporal trajectory protection and publishing method based on kernel density estimation

Promoted by the Internet of Thing era, the widespread use of mobile sensing devices equipped with positioning functions has led to the generation of substantial trajectory data. Mining and analyzing trajectory data has high research value, but poses a risk of user privacy leakage, resulting in fewer publicly available trajectory datasets for research and analysis. Therefore, a trajectory data publishing method that ensures high data utility while protecting user privacy has become a hot topic. In this paper, we propose a spatiotemporal trajectory data protection and publishing method based on kernel density estimation (STP-KDE), which protects the trajectory data and trajectory count values while improving data utility. In the protection process of trajectory data, we designed a kernel density clustering framework that is combined with the differential privacy exponential mechanism. In the protection process of trajectory counts value, the adaptive Laplace noise perturbation mechanism is proposed to differentially protect these counts. Experimental results show that STP-KDE can provide more useful data and stronger privacy protection than existing studies.

A News Recommendation Method for User Privacy Protection

News recommendations play an important role in daily life, but there is a problem of privacy leakage. To address this problem, a news recommendation method for user privacy protection is proposed. It combines news recommendation with a federated learning framework to ensure accurate recommendations while protecting user privacy. To be specific, the method contains three key components: 1) It combines federated learning to ensure that user data is processed locally and not leaked to third parties, thus achieving privacy protection. 2) It fully considers the long-term and short-term interests of users and learns their short-term interests using the GRU model based on the item features of interaction, which provides a more comprehensive perspective on user modelling. 3) We design a score prediction method that fuses linear and nonlinear features to better capture the interaction between users and news and improve the accuracy of recommendation. The experimental validation on two public news datasets, Adressa and MIND, demonstrates that the method can still achieve efficient news recommendation without compromising user privacy.