Break the Data Barriers While Keeping Privacy: A Graph Differential Privacy Method

Li, Yijing; Tao, Xiaofeng; Zhang, Xuefei; Wang, Mingsi; Wang, Shuo

doi:10.1109/jiot.2022.3151348

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…The shift from fuel to electric vehicles has elevated the importance of charging data, exposing personal routines and mobility patterns that are relevant to urban planning and traffic management [7,8]. Additionally, vulnerabilities in these data could be exploited to manipulate traffic information, impacting the broader transportation system [9].…”

Section: Definitionmentioning

confidence: 99%

“…In addition, ref. [8] delves into the intricacies of dismantling data barriers while concurrently upholding privacy protection. The study shifts the paradigm of vehicle data representation from textual format to a graph-structured data form, systematically accounting for the volume of interactive data and potential privacy leakage during data dissemination.…”

Section: Differential Privacymentioning

confidence: 99%

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Methods and Challenges of Cryptography-Based Privacy-Protection Algorithms for Vehicular Networks

Li,

Bi,

Jiang

et al. 2024

Electronics

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With the rapid development of wireless communication technology, positioning technology, and modern smart devices, Internet of Vehicles (IoVs) smart vehicles have brought great convenience to human production and life. Meanwhile, privacy and security issues are becoming extremely serious, with serious consequences if sensitive data such as vehicle location and trip patterns are leaked. This paper focuses on the demands for vehicular network security, especially privacy protection and existing privacy-protection techniques, including common cryptography methods and cryptography-based advanced technologies. At the same time, this paper also analyzes the advantages and challenges of these technologies in protecting privacy and network security in the Internet of Vehicles, such as the challenges of computational resource requirements and security efficiency in the implementation process, as well as the complexity of realizing effective privacy protection in the interactions among different entities. Finally, this paper envisions the development of privacy-preserving application scenarios and the prospects for crypotography-based privacy-preserving technologies.

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Section: Definitionmentioning

confidence: 99%

Section: Differential Privacymentioning

confidence: 99%

Methods and Challenges of Cryptography-Based Privacy-Protection Algorithms for Vehicular Networks

Li,

Bi,

Jiang

et al. 2024

Electronics

Self Cite

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“…A spatial index structure, such as a UB-tree, is a combination of a Z-order and B * data index structure proposed by Bayer et al [8]. Li et al [9] proposed an adaptively clocking region division to resist the threat under a location-based service. However, the actual spatial location data will have different distribution densities.…”

Section: Introductionmentioning

confidence: 99%

A Range Query Scheme for Spatial Data with Shuffled Differential Privacy

Li,

Zhang,

et al. 2024

Mathematics

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The existing high-dimensional or multi-dimensional geographic spatial datasets have a large amount of data. When third-party servers collect and publish them, privacy protection is required to prevent sensitive information from being leaked. Local differential privacy can be used to protect location-sensitive information during range queries. However, the accuracy of a range query based on local differential privacy is affected by the distribution and density of spatial data. Based on this, aiming at the distribution and density characteristics of data, we designed a dpKD tree that supports high-precision range queries with shuffled differential privacy, and designed an algorithm KDRQ for range queries based on shuffled differential privacy. First, we employed the dpKD to divide the data. Then, we shuffled the data based on SRRQ and reconstructed the tree. Finally, we used the SDRQ algorithm for the response range query. The experimental results show that the query accuracy of the KDRQ algorithm was at least 1–4.5 times higher than that of the existing algorithms RAPPOR, PSDA and GT-R under the same privacy budget.

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“…Recently, many solutions have been proposed to preserve the privacy of SN users in G publishing [15][16][17][18][19][20][21]. These solutions have been used to preserve either nodes' or edges' privacy in the release of G. Recently, differential privacy-based solutions have also been proposed to alter the G's structure for privacy preservation [22]. Despite the success of these solutions, privacy issues can stem in multiple formats, and robust solutions are needed to overcome all types of privacy issues.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

2022

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Owning to the massive growth in internet connectivity, smartphone technology, and digital tools, the use of various online social networks (OSNs) has significantly increased. On the one hand, the use of OSNs enables people to share their experiences and information. On the other hand, this ever-growing use of OSNs enables adversaries to launch various privacy attacks to compromise users’ accounts as well as to steal other sensitive information via statistical matching. In general, a privacy attack is carried out by the exercise of linking personal data available on the OSN site and social graphs (or statistics) published by the OSN service providers. The problem of securing user personal information for mitigating privacy attacks in OSNs environments is a challenging research problem. Recently, many privacy-preserving solutions have been proposed to secure users’ data available over OSNs from prying eyes. However, a systematic overview of the research dynamics of OSN privacy, and findings of the latest privacy-preserving approaches from a broader perspective, remain unexplored in the current literature. Furthermore, the significance of artificial intelligence (AI) techniques in the OSN privacy area has not been highlighted by previous research. To cover this gap, we present a comprehensive analysis of the state-of-the-art solutions that have been proposed to address privacy issues in OSNs. Specifically, we classify the existing privacy-preserving solutions into two main categories: privacy-preserving graph publishing (PPGP) and privacy preservation in application-specific scenarios of the OSNs. Then, we introduce a high-level taxonomy that encompasses common as well as AI-based privacy-preserving approaches that have proposed ways to combat the privacy issues in PPGP. In line with these works, we discuss many state-of-the-art privacy-preserving solutions that have been proposed for application-specific scenarios (e.g., information diffusion, community clustering, influence analysis, friend recommendation, etc.) of OSNs. In addition, we discuss the various latest de-anonymization methods (common and AI-based) that have been developed to infer either identity or sensitive information of OSN users from the published graph. Finally, some challenges of preserving the privacy of OSNs (i.e., social graph data) from malevolent adversaries are presented, and promising avenues for future research are suggested.

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Break the Data Barriers While Keeping Privacy: A Graph Differential Privacy Method

Cited by 10 publications

References 37 publications

Methods and Challenges of Cryptography-Based Privacy-Protection Algorithms for Vehicular Networks

Methods and Challenges of Cryptography-Based Privacy-Protection Algorithms for Vehicular Networks

A Range Query Scheme for Spatial Data with Shuffled Differential Privacy

A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

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