A Smart Access Control Mechanism Based on User Preference in Online Social Networks

Shan, Fangfang; Ji, Peiyu; Li, Fuyang; Li, Weiguang

doi:10.1007/978-3-030-89814-4_42

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Li et al 28 proposed a fine‐grained multiparty access control model, which detects all users in photos by face recognition, and then evaluates the access policy by combining the photo context and the combination information of co‐existing users in spatio‐temporal information. Shan et al 29 proposed a social network forwarding control mechanism based on game theory on the basis of analyzing the interests of both forwarding parties, which compared the historical data of forwarding operations with the threshold set by the publisher and gave the final decision whether to allow forwarding. Wu and Pan 30 verified the constructed privacy random game model and the new privacy risk measurement criterion, and solved the strategy by reinforcement learning algorithm, and obtained effective personal access control policy.…”

Section: Related Workmentioning

confidence: 99%

A smart access control mechanism based on user preference in online social networks

Shan

2022

Concurrency and Computation

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SummaryAccess control can effectively protect users' data and privacy in social networks. Different access control models address different data privacy requirements, and the corresponding implementation technologies and performance are also quite different. The existing access control mechanisms of social networks rarely take into account the individual preferences of users, and cannot provide personalized services for users. Therefore, we have studied an analysis mechanism of user preference to quantify user preferences, and then use user preference values and some key information of users' social resources as decision vectors, and proposed an access control mechanism based on user preference (ACMBUP). ACMBUP turns the problem of access decision‐making into a problem of classification, that is, whether to allow access or not to allow access. At the same time, we add an encryption and decryption mechanism in ACMBUP to prevent data leakage and protect data when users interact with access mechanisms. Experiments show that this mechanism can automatically generate appropriate access control policies to meet the potential privacy needs of different users, so as to better protect the privacy of social networks data.

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Section: Related Workmentioning

confidence: 99%

A smart access control mechanism based on user preference in online social networks

Shan

2022

Concurrency and Computation

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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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A Smart Access Control Mechanism Based on User Preference in Online Social Networks

Cited by 2 publications

References 16 publications

A smart access control mechanism based on user preference in online social networks

A smart access control mechanism based on user preference in online social networks

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

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