Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

Piao, Yangheran; Ye, Kai; Cui, Xiaohui

doi:10.1109/access.2021.3064208

Cited by 10 publications

(3 citation statements)

References 87 publications

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“…Apart from the BK and other auxiliary types of data, a new risk known as interdependent privacy risk (IPR) has become one of the major privacy threats in SNs in recent times [43][44][45]. Furthermore, inference attacks [46], ML-based attacks [47], privacy leakage in health SNs [48], profile cloning [49], profile matching [50], community-based threats [51], cross-SN user matching [52], and privacy concerns in different OSN services (e.g., recommendation systems [53], query evaluations [54], and sentiment analyses [55]) have made privacy preservation in OSNs an active area of research.…”

Section: Anonymization Operation That Can Be Applied To Gmentioning

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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Section: Anonymization Operation That Can Be Applied To Gmentioning

confidence: 99%

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

2022

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“…Attackers can infer the sensitive information contained in the statistically analytical data through the parameters of algorithms [2]. In addition, the other two attack methods: attribute inference [3] and model theft [4] may also lead to the disclosure of private data. Some scholars use technology based on k-anonymity [5] to protect datasets.…”

Section: Introductionmentioning

confidence: 99%

Secure computing: logistic regression analysis with differential privacy

Yao

Wang

et al. 2023

International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2023)

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Logistic regression has the threat of privacy disclosure during data analysis. Differential privacy can prevent this security threat. However, differential privacy mechanism needs to add noise to the protected algorithm, which will affect the quality of data analysis. To improve the quality of data analysis, we propose dpLogic algorithm based on differential privacy and functional mechanism. This algorithm constructs an auxiliary function, which can help us get the noisy model as close as possible to the original model. Besides, dpLogic optimizes privacy budget allocation, reducing the disturbance of noise on the model. Experimental results illustrate that this algorithm can effectively improve the accuracy of data analysis at a high level of privacy protection.

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“…OSNs are filled with many types of User Generated Data (UGD), among which the attributes of users and the relationships between users can be used by adversaries to launch various attacks 6,7 including privacy inference attacks 8,9 to infer the privacy of the target users. Since UGD represents a large percentage of the data in OSNs and is relatively easy to acquire, it is therefore important to study privacy inference methods and design counter‐measures against such attacks to prevent the leakage of user privacy 10,11 .…”

Section: Introductionmentioning

confidence: 99%

A privacy‐sensitive data identification model in online social networks

Yi,

Zhu,

et al. 2023

Trans Emerging Tel Tech

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Privacy 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.

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Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

Cited by 10 publications

References 87 publications

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

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

Secure computing: logistic regression analysis with differential privacy

A privacy‐sensitive data identification model in online social networks

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