Privacy Protection Mechanisms Research Articles

Enhancing Privacy in the Internet of Vehicles via Hyperelliptic Curve Cryptography

The Internet of Things (IoT) is a technological paradigm that has gained significant momentum the last decade and, among other features, enables the development of intelligent and interoperable device networks. In this respect, it has triggered the creation and evolution of vehicular ad-hoc networks (VANETs), which are initially implemented in order to guarantee the safety of drivers and the avoidance of traffic accidents. The drawback is that this fast evolution comes with serious concerns in terms of the privacy of users, while the population of attackers or entities that try to eavesdrop and intercept information has significantly increased. This imposes a serious risk for drivers moving across a Smart City. The research presented in this paper aims to evaluate privacy protection mechanisms in VANET environments, based on the efficiency and security level they ensure, considering the fact that VANETs provide limited resources to users/drivers. Moreover, the usage of elliptic curve cryptography in reduced resources environments is discussed. Finally, this paper compares the performance of three cryptographic algorithms, elliptic curve cryptography (ECC), hyperelliptic curve cryptography genus 2 (HECC-2) and HECC genus 3 (HECC-3), employed for an efficient authentication and safe message transmission mechanism in VANETs, aimed at reaching conclusions related to the implementation of each cryptographic scheme in this specific application area. The evaluation results indicate that ECC supersedes HECC-2 and HECC-3 in most metrics. However, HECC-2 and HECC-3 demonstrate better responses than ECC does in selected energy metrics. Overall, it is observed that HECC algorithms are not yet mature enough to compete with ECC. This is due to the fact that the research community has not sufficiently progressed toward the optimization of HECC, and moreover, HECC builds on quite complex mathematics. There are indications, however, that once HECC curves are indeed optimized, HECC will outperform ECC in speed as well as in other metrics, sinceHECC-2 and HECC-3 use a significantly smaller key size with the same level of security as that of ECC.

Evaluation of Chinese HIV Mobile Apps by Researchers and Patients With HIV: Quality Evaluation Study.

Against the backdrop of globalization, China remains one of the most heavily burdened countries in Asia with regard to AIDS. However, many high-risk groups and patients affected by AIDS may be less likely to actively seek care from medical institutions because of fear of experiencing shame or discrimination. Mobile apps provide a promising avenue for supporting the prevention, diagnosis, and treatment of AIDS. However, a comprehensive systematic evaluation of these mobile apps' functionality and quality has not been conducted yet. This study aims to identify the available mobile apps for AIDS in China, assess and discuss the functional features and quality of these Chinese AIDS mobile apps, and offer decision support for patients and clinical practitioners in accessing high-quality AIDS mobile apps. Furthermore, based on the evaluation results, recommendations for improvement will be provided. A systematic search was conducted on the Qimai app data platform, the Aladdin WeChat applet data platform, and WeChat to identify mobile apps related to AIDS. A snowball sampling method was used to supplement the potentially overlooked apps. The selected mobile apps underwent a rigorous screening process based on unified criteria. Subsequently, assessments were independently undertaken by 3 separate researchers and 2 patients with HIV, using both the Mobile App Rating Scale (MARS) and the User Mobile App Rating Scale (uMARS). Quantitative interpretations of the data were facilitated by the MedCalc statistical software (version 20.217, MedCalc Software). A total of 2901 AIDS mobile apps were included in the study, with 2897 identified through information retrieval and an additional 4 added via snowball sampling. After a rigorous selection process, 21 apps were determined to be usable. Among them, the Hong Feng Wan app achieved the highest combined average score, calculated based on the MARS (3.96, SD 0.33) and uMARS (4.47, SD 0.26). Overall, there was no significant correlation between MARS and uMARS (rapp quality total score=0.41; P=.07; rsubjective quality=0.39; P=.08). A notable issue was the widespread lack of user privacy protection, with only 24% (5/21) of the apps offering this feature. The number of available Chinese AIDS mobile apps is limited, with WeChat applets dominating the market. Nonetheless, the performance of WeChat mini-apps is generally inferior to that of independent apps, and there may be significant discrepancies between assessments conducted by researchers and those provided by genuine end users, emphasizing the necessity of involving real users in the development and evaluation of HIV mobile apps. In addition, developers of these Chinese HIV mobile apps need to devote attention to improving privacy protection mechanisms, in addition to considering the evaluations of researchers and real users. This will help attract more users and increase user loyalty.

Research on android user privacy permission analysis and protection mechanism under big data environment

With the rapid development of big data technology, the issue of user privacy security on the Android platform is becoming increasingly prominent. This paper aims to conduct an in-depth analysis of the privacy permissions of Android users under the big data environment and explore effective protection mechanisms. Through research on permission management, application behavior, and user privacy leakage pathways in the Android system, this paper proposes a comprehensive privacy protection strategy to enhance the privacy security level of Android users in the big data environment.

Personalized trajectory differential privacy protection mechanism based on spatiotemporal correlation prediction

Personalized trajectory differential privacy protection mechanism based on spatiotemporal correlation prediction

Edge computing-based big data privacy preservation in motion trajectory prediction for martial arts training

Abstract The development of big data technology has brought about sweeping changes in many fields. Privacy protection has become a key concern for big data users. The study constructs a geographically indistinguishable location-based privacy protection mechanism based on differential privacy and geographic indistinguishability and further constructs an edge computing-based privacy protection model for martial arts movement trajectories. The performance of an edge computing-based privacy protection model for martial arts sports trajectory is examined by comparing it to other models in terms of quality of service loss, privacy protection strength, and range counting queries. And explore the effects of service type and number of users on the system. The perturbation distance on all three time periods of the Geo-In method proposed in this paper is the smallest among the five methods, and the perturbation distance decreases with the increase of the privacy budget, and the loss of quality of service is minimized. The Geo-In algorithm’s perturbed locations have less semantic similarity to the real locations of martial arts training, resulting in stronger privacy protection of the locations. Range counting queries experience a decrease in relative error as the number of users and query range increase.

Challenges for children’s rights in connection with the development of artificial intelligence

This article explores the importance of protecting children’s rights in the context of the rapid development of artificial intelligence. Artificial intelligence has the potential to change many aspects of a child’s life, and while it can have positive effects, its negative impact also needs due consideration.
 The article focuses on four key aspects: privacy, security, discrimination and ethics. It analyzes the risks associated with the collection and processing of children’s personal data by artificial intelligence and requires the establishment of effective privacy protection mechanisms. In addition, it considers the safety of children, especially in the context of the use of autonomous robots and toys, and safety standards that are required to be taken into account in the development of such systems.
 The article also focuses on the problem of discrimination that may arise from the systematic use of artificial intelligence. It demands developers to ensure the fairness of algorithms and avoid discrimination against children. Finally, the article considers the ethical aspects of the use of artificial intelligence, namely the question of the responsibility of developers and the need for ethical principles in all aspects of its use.
 In conclusion, the article emphasizes the need for constant monitoring and regulation of the development of artificial intelligence in order to protect the rights of the child. It recommends the implementation of an effective legislative framework that defines standards for the protection of children’s rights in the context of artificial intelligence. In addition, the article puts forward the idea of developing ethical directives and codes of conduct for developers, researchers and users of artificial intelligence from the perspective of children’s rights.
 In general, this scientific article emphasizes the importance of protecting the rights of the child in the context of the development of artificial intelligence. It calls for action at the level of legislation, technological standards and ethical principles, ensuring safety, privacy, non-discrimination and ethical responsibility in all aspects of the use of artificial intelligence to ensure the healthy development and protection of children’s rights in the digital age.
 Also, the article is devoted to the analysis of the controversial issue of the relationship between child rights and artificial intelligence. The article examines the consequences of the use of artificial intelligence in the context of children’s rights, the main issues related to the legal protection of children in the field of artificial intelligence, and suggests possible solutions for these problems.

Research on the Application of Non-contact Sensing Technology in Real-time Emotional Monitoring and Feedback

With the rapid development of information technology, non-contact sensing technology has shown great potential in the field of real-time emotional monitoring and feedback. The purpose of this study is to deeply explore the application of this technology in improving the intelligence of human-computer interaction and realizing personalized service. By synthesizing the experimental results and related literature, a series of important research findings have been formed. First of all, we found that non-contact sensing technology effectively improved the objectivity of emotion monitoring. Secondly, the introduction of real-time feedback mechanism has significantly improved the user experience. However, non-contact sensing technology still faces some challenges in practical application, including privacy issues, cross-cultural adaptability, environmental interference and so on. To solve these problems, technological innovation, the establishment and standardization of privacy policies are needed to ensure the sustainable application of technology in a wider range of fields. This study emphasizes the importance and application prospect of non-contact sensing technology in real-time emotional monitoring and feedback. Future research should focus on the further innovation of technology, the improvement of privacy protection mechanism and the deepening of interdisciplinary cooperation, so as to promote the wider application of this technology in the field of human-computer interaction.

A Survey on Blockchain-Based Federated Learning

Federated learning (FL) and blockchains exhibit significant commonality, complementarity, and alignment in various aspects, such as application domains, architectural features, and privacy protection mechanisms. In recent years, there have been notable advancements in combining these two technologies, particularly in data privacy protection, data sharing incentives, and computational performance. Although there are some surveys on blockchain-based federated learning (BFL), these surveys predominantly focus on the BFL framework and its classifications, yet lack in-depth analyses of the pivotal issues addressed by BFL. This work aims to assist researchers in understanding the latest research achievements and development directions in the integration of FL with blockchains. Firstly, we introduced the relevant research in FL and blockchain technology and highlighted the existing shortcomings of FL. Next, we conducted a comparative analysis of existing BFL frameworks, delving into the significant problems in the realm of FL that the combination of blockchain and FL addresses. Finally, we summarized the application prospects of BFL technology in various domains such as the Internet of Things, Industrial Internet of Things, Internet of Vehicles, and healthcare services, as well as the challenges that need to be addressed and future research directions.

A Longitudinal and Comprehensive Measurement of DNS Strict Privacy

The DNS privacy protection mechanisms, DNS over TLS (DoT) and DNS over HTTPS (DoH), only work correctly if both the server and client support the Strict Privacy profile and no vulnerability exists in the implemented TLS/HTTPS. A natural question then arises: what is the landscape of DNS Strict Privacy? To this end, we provide the first longitudinal and comprehensive measurement of DoT/DoH deployments in recursive resolvers, authoritative servers, and browsers. With the collected data, we find the number of DoT/DoH servers increased substantially during our ten-month-long scan. However, around 60% of DoT and 44% of DoH recursive resolver certificates are invalid. Worryingly, our measurements confirm the centralization problem of DoT/DoH. Furthermore, we classify DNS Strict Privacy servers into four levels according to daily scanning results on TLS/HTTPS-related security features. Unfortunately, around 25% of DoH Strict Privacy recursive resolvers fail to meet the minimum level requirements. To help the Internet community better perceive the landscape of DNS Strict Privacy, we implement a DoT/DoH server search engine and recommender system. Additionally, we investigate five popular browsers across four operating systems and find some inconsistent behavior with their DNS privacy implementations. For example, Firefox in Windows, Linux, and Android allows DoH communication with the server without the SAN certificate. At last, we advocate that all participants head together for a bright DNS Strict Privacy landscape by discussing current hindrances and controversies in DNS privacy.

Hashing Fake: Producing Adversarial Perturbation for Online Privacy Protection Against Automatic Retrieval Models

The wide application of deep neural networks (DNNs) has significantly improved the performance of hashing models on multimodal retrieval issues. DNN-based deep models can automatically learn semantic features from raw data to make human-level decisions. However, the superior generalization leads to potential privacy leakage risks. Strong DNN-based retrieval models enable malicious crawlers to search for nontag private information based on semantic similarity matching. Hence, executing effective privacy protection mechanisms against those retrieval software is essential for reliable social website construction. In this article, we propose a retrieval task-based adversarial perturbation generation method called Hashing Fake to meet this request. Specifically, DNNs are recently found to be vulnerable to a specific set of attacks called adversarial perturbations, which denote some magnitude-restricted signals added on objective samples to misguide well-crafted DNN models, and perturbations’ magnitudes are small enough that will not induce humans’ perception. Moreover, since existing adversarial perturbation generation methods are designed for supervised tasks, Hashing Fake constructs a differential approximation substitution for perturbation production on unsupervised retrieval tasks. Through extensive experiments on several deep retrieval benchmarks, we demonstrate that well-crafted perturbations using Hashing Fake can effectively misguide objective models’ recognitions to make false predictions. The added norm-restricted perturbations on objective samples will not alter humans’ perception; hence, Hashing Fake can be applied on real-world social websites to protect subscribers’ privacy against malicious retrieval software.

Frame-Level Teacher-Student Learning With Data Privacy for EEG Emotion Recognition.

Recently, electroencephalogram (EEG) emotion recognition has gradually attracted a lot of attention. This brief designs a novel frame-level teacher-student framework with data privacy (FLTSDP) for EEG emotion recognition. The framework first proposes a teacher-student network without prior professional information for automated filtering of useful frame-level features by a gated mechanism and extracting high-level features by using knowledge distillation to capture the results of EEG emotion recognition from a teacher network and student networks. Then, the results from subnetworks are integrated by using the novel decision module, which, motivated by the voting mechanism, adjusts the composition of feature vectors and improves the weight of accurate prediction to optimize the integration effect. During training, an innovative data privacy protection mechanism is applied for avoiding data sharing, where each student network only inherits weights from all trained networks and does not inherit the training dataset. Here, the framework can be repeatedly optimized and improved by only training the next student subnetwork on new EEG signals. Experimental results show that our framework improves the accuracy of EEG emotion recognition by more than 5% and gets state-of-the-art performance for EEG emotion recognition in the subject-independent mode.

Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing

The rapid proliferation of handheld intelligent devices and the advent of 5G technology have brought about convenient and fast services for people. In perception-oriented application services, participating users will upload sensitive mobile data in order to obtain benefits. While devising privacy protection strategies to ensure data security, it is crucial to accomplish task perception related to data collection to the fullest extent possible. To address this challenge, this paper proposes a personalized data privacy protection algorithm based on an adaptive dynamic adjustment grid and the minimum wage task allocation strategy. According to the different levels of users’ needs for privacy protection, combined with the privacy budget allocation strategy, we design a different-level differential privacy protection mechanism and consider the reward mechanism in task allocation to balance the effectiveness and security of the location data uploaded by users. Experiments show that the strategy proposed in this paper can not only protect the data but also enable users to freely choose the level of privacy protection.

Selection of node with editing rights and privacy protection mechanisms based on dual-blockchain

The inability to edit and modify the data on the blockchain limits its practical applications. Current editable blockchain schemes with little regard for security in the editing process, and there is a lack of effective monitoring and auditing measures. These are also susceptible to the formation of a centralized trend of editing rights and bring privacy protection problems. Aiming at editable application requirements, to solve the problems of “who will edit” and “the security of editable”, selection of node with editing rights and privacy protection mechanisms based on dual-blockchain are proposed, including data chain and audit chain. In the data chain, to avoid some nodes being in the chain for a long time, being used by attackers and editing rights tending to be centralized, heterogeneity among nodes is used as one of the decisions. An Execution Node Authority Confirmation Mechanism (ENACM) based on confidence, reliability, and heterogeneity is proposed. The performance of nodes at different stages can be effectively used. Node with editing rights is ensured to be unpredictable and protected from targeted attacks. In the audit chain, the Parameter Audit Mechanism (PAM) is proposed. To anonymously update and verify the parameters used in the ENACM, Concise Linkable Spontaneous Anonymous Group signature, Pedersen commitment, and Schnorr signature are employed. Privacy protection is ensured for associated information of the node. A prototype system of the proposed scheme based on Hyperledger Fabric has been implemented. System performance, security, parameter settings, and comparison with related schemes are analyzed. Experimental and test results show that the proposed scheme is feasible, effective, and reliable.

A privacy‐sensitive data identification model in online social networks

AbstractPrivacy protection in online social networks (OSNs) has received a great deal of attention in recent years. One way of circumventing conventional privacy protection is privacy inference based on data that can be easily obtained in OSNs. Previous work on privacy inference has studied the issue mostly from the viewpoint of the attackers and methods thus designed were mostly aimed at pursuing the accuracy of the inference results with little regard on the causes of privacy breaches. To develop more effective privacy protection mechanisms that takes privacy inference into consideration, it is necessary to identify the information that plays a more important role in privacy breaches. In this paper, we propose a privacy‐sensitive data identification model in OSNs, which can identify key pieces of data that are most sensitive as far the privacy of the user is concerned. Firstly, a privacy inference method is proposed based on conditional random fields to infer the privacy of the target users. Then, a privacy‐sensitive data identification method is proposed by using the intermediate data of the proposed privacy inference method based on the targeted influence maximization algorithm. Thus, the key pieces of data in the form of user attributes and relationships on which the privacy of the target user depends can be determined to facilitate the implementation of privacy protection mechanisms. The effectiveness as well as the advantages of the proposed model is verified and demonstrated through experiment using real datasets. The impact of the key factors on privacy inference is also analyzed to guide the design of effective privacy protection strategies.

A Triple Real-Time Trajectory Privacy Protection Mechanism Based on Edge Computing and Blockchain in Mobile Crowdsourcing

With the rapid development of the Internet of Things (IoT) and the rapid popularization of 5G networks, the data that needs to be processed in Mobile Crowdsourcing (MCS) system is increasing every day. Traditional cloud computing can no longer meet the needs of crowdsourcing for real-time data and processing efficiency, thus, edge computing was born. Edge computing can be calculated at the edge of network so that greatly improve the efficiency and real-time performance of data processing. In addition, most of the existing privacy protection technologies are based on the trusted third parties. Therefore, in view of the semi-trustworthiness of edge servers and the transparency of blockchain, this paper proposes a triple real-time trajectory privacy protection mechanism (T-LGEB) based on edge computing and blockchain. Through combining the localized differential privacy and multiple probability extension mechanism, the T-LGEB mechanism is proposed to send the requests and data to the edge server in this paper. Then, through the spatio-temporal dynamic pseudonym mechanism proposed in the paper, the entire trajectory of task participants is divided into multiple unrelated trajectory segments with different pseudonymous identities in order to protect the trajectory privacy of task participants while ensuring high data availability and real-time data. Through a large number of experiments and comparative analysis on multiple real data sets, the proposed T-LGEB has extremely high privacy protection capabilities and data availability, and the resource consumption caused is relatively low.

Performance analysis of perturbation-based privacy preserving techniques: an experimental perspective

<span lang="EN-US">Nowadays, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several hybrid perturbation strategies that may be used to protect data privacy. For this, two perturbation-based techniques named improved random projection perturbation (IRPP) and enhanced principal component analysis-based technique (EPCAT) were used. These methods are employed to assess the precision, run time, and accuracy of the experimental results. This paper provides the impacts of perturbation-based privacy preserving techniques. It is observed that hybrid approaches are more efficient than the traditional approach.</span>

FRNet: An MCS Framework for Efficient and Secure Data Sensing and Privacy Protection in IoVs

Mobile Crowdsensing (MCS) is a kind of sensing mode based on mobile nodes, which provides a cost-effective solution for data collection of urban sensing. However, when participants collect and upload sensing data in MCS system, they often overlap in time and space. Therefore, MCS system obtains a lot of redundant data, and ultimately cannot meet the MCS task requirements. Meanwhile, existing schemes cannot realize the dynamic allocation of sensing tasks and the dynamic selection of task participants. In addition, task participants will generate and transfer a large amount of sensitive information, such as location, identity, personal habits, etc., in the process of uploading sensing data and completing sensing tasks, thus leading to the risk of privacy leakage. At present, many schemes use federated learning and other technologies to protect privacy, but there are still challenges that adversaries can extract parameters information during model training. Therefore, in order to the above problems, this paper puts forward an efficient and secure MCS framework FRNet in Internet of Vehicles (IoVs). Specifically, MCS task completion is described as a two-phase process. This paper not only proposes a method to dynamically select candidate participants in MCS vehicular networks to collect high-quality sensing data, but also establishes a data privacy protection mechanism for the whole process of model training in federated learning. Finally, the effectiveness of FRNet is verified through security analysis and experimental evaluation. Experimental results show that FRNet can effectively improve the quality of sensing data, maximize TCV, and enhance data privacy protection ability.

Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing.

The increasing popularity of portable smart devices has led to the emergence of vehicular crowdsensing as a novel approach for real-time sensing and environmental data collection, garnering significant attention across various domains. Within vehicular crowdsensing, task assignment stands as a fundamental research challenge. As the number of vehicle users and perceived tasks grows, the design of efficient task assignment schemes becomes crucial. However, existing research solely focuses on task deadlines, neglecting the importance of task duration. Additionally, the majority of privacy protection mechanisms in the current task assignment process emphasize safeguarding user location information but overlook the protection of user-perceived duration. This lack of protection exposes users to potential time-aware inference attacks, enabling attackers to deduce user schedules and device information. To address these issues in opportunistic task assignment for vehicular crowdsensing, this paper presents the minimum number of participants required under the constraint of probability coverage and proposes the User-Based Task Assignment (UBTA) mechanism, which selects the smallest set of participants to minimize the payment cost while measuring the probability of accomplishing perceived tasks by user combinations. To ensure privacy protection during opportunistic task assignment, a privacy protection method based on differential privacy is introduced. This method fuzzifies the sensing duration of vehicle users and calculates the probability of vehicle users completing sensing tasks, thus avoiding the exposure of users' sensitive data while effectively assigning tasks. The efficacy of the proposed algorithm is demonstrated through theoretical analysis and a comprehensive set of simulation experiments.

Differential Privacy-Based Spatial-Temporal Trajectory Clustering Scheme for LBSNs

Location privacy preserving for location-based social networks (LBSNs) has been attracting a great deal of attention. Existing location privacy protection methods are disadvantaged by issues such as information leakage and low data availability, which are no longer suitable for the current diverse and personalized location-based services. To address these issues, we propose a differential privacy-based spatial-temporal trajectory clustering (DP-STTC) scheme, which mainly transforms the existing location privacy protection mechanism into a spatial-temporal trajectory protection mechanism by adjusting the privacy parameters. Then, the trajectories were clustered to uncover users with similar trajectory characteristics. Finally, experiments were conducted on two real datasets. The experimental results show that our DP-STTC scheme can not only achieve better accuracy in trajectory clustering, but also protect user privacy.

Privacy-preserving mobile crowd sensing task assignment with Stackelberg game

Mobile Crowd Sensing (MCS) has emerged as an effective method for sensing and collecting large-scale computational data in real time, which is favored by researchers from a wide range of industries. Task assignment is an important research direction in mobile crowd sensing. In the scenario of multiple workers and multiple tasks, it is a key challenge to develop an excellent task assignment scheme to ensure that both the platform and workers can also get high utility while workers’ location privacy is not leaked. In this regard, this paper proposes a task assignment scheme (PCTA-SG) with worker location privacy protection and worker merit-based selection mechanism, which improves platform utility and equilibrates worker utility through worker path planning and Stackelberg game under the constraint of a limited perceived budget. First, the platform publishes the task set and its related information. Next, each candidate worker perturbs its own location locally based on geographical indistinguishability, and uploads the required information to the platform after selecting the tasks. Then a worker priority factor model is established by comprehensively considering the factors such as worker credibility, perceived device power and so on. According to the priority factors of workers, the platform uses a greedy strategy to select high-quality and suitable workers and assign tasks to them, thereby forming an initial solution for task assignment. To enhance the utility of the platform, the initial solution for task assignment is continued as the initial population of the genetic algorithm to optimize the moving distance of workers and generate the final solution for task assignment. Finally, on the basis of the final solution for task assignment, a two-stage Stackelberg game is carried out between the platform and workers to further optimize the utility of the platform and take into account the utility of workers, thus perfecting the task assignment scheme. The experimental evaluation results with other baseline schemes under real-world dataset show that the scheme in this paper achieves the best overall effect. It not only protects the location privacy of workers, but also achieves good worker utility while obtaining the optimal platform utility and platform input–output ratio.