Towards link inference attack against network structure perturbation

Xian, Xingping; Wu, Tao; Liu, Yanbing; Wang, Wei; Wang, Chao; Xu, Guangxia; Xiao, Yonggang

doi:10.1016/j.knosys.2020.106674

Cited by 17 publications

(16 citation statements)

References 63 publications

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“…Therefore, in network science, link prediction is a paradigmatic and fundamental problem with long attractivity and vitality. Beyond an algorithm predicting missing and future links, link prediction is also a powerful analyzing tool, which has already been utilized in evaluating and inferring network evolving mechanisms ( Wang et al., 2012 ; Zhang et al., 2015 ; Zhang, 2017 ), testing the privacy-protection algorithms (as an attaching method) ( Xian et al., 2021 ), evaluating and designing network embedding algorithms ( Dehghan-Kooshkghazi et al., 2021 ; Gu et al., 2021 ), and so on. Though the last decade has witnessed plentiful and substantial achievements, the study of link prediction is just unfolding and more efforts are required toward a full picture of how links do emerge and vanish.…”

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Progresses and challenges in link prediction

Zhou

2021

iScience

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Summary Link prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of nonobserved links, based on known topology. After a brief introduction of the standard problem and evaluation metrics of link prediction, this review will summarize representative progresses about local similarity indices, link predictability, network embedding, matrix completion, ensemble learning, and some others, mainly extracted from related publications in the last decade. Finally, this review will outline some long-standing challenges for future studies.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Progresses and challenges in link prediction

Zhou

2021

iScience

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“…These attacks are categorized as identity disclosure, sensitive attribute disclosure, and sensitive link disclosure. Identity disclosure [6] is a type of breach that occurs when an adversary reveals the individual behind the record. Sensitive attribute disclosure collects sensitive or confidential details about the individual [7].…”

Section: Motivational Examplementioning

confidence: 99%

“…Privacy preservation is widely considered an important topic for data publishing, and many models are proposed as well as many effective algorithms. However, these methods cannot be directly applied to social graphs as they are ideally proposed for relational datasets and graph anonymization is more complicated than relational data [6]. The reasons are listed below:…”

Section: Challengesmentioning

confidence: 99%

k-NDDP: An Efficient Anonymization Model for Social Network Data Release

et al. 2021

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With the evolution of Internet technology, social networking sites have gained a lot of popularity. People make new friends, share their interests, experiences in life, etc. With these activities on social sites, people generate a vast amount of data that is analyzed by third parties for various purposes. As such, publishing social data without protecting an individual’s private or confidential information can be dangerous. To provide privacy protection, this paper proposes a new degree anonymization approach k-NDDP, which extends the concept of k-anonymity and differential privacy based on Node DP for vertex degrees. In particular, this paper considers identity disclosures on social data. If the adversary efficiently obtains background knowledge about the victim’s degree and neighbor connections, it can re-identify its victim from the social data even if the user’s identity is removed. The contribution of this paper is twofold. First, a simple and, at the same time, effective method k–NDDP is proposed. The method is the extension of k-NMF, i.e., the state-of-the-art method to protect against mutual friend attack, to defend against identity disclosures by adding noise to the social data. Second, the achieved privacy using the concept of differential privacy is evaluated. An extensive empirical study shows that for different values of k, the divergence produced by k-NDDP for CC, BW and APL is not more than 0.8%, also added dummy links are 60% less, as compared to k-NMF approach, thereby it validates that the proposed k-NDDP approach provides strong privacy while maintaining the usefulness of data.

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“…Although most OSNs provide the option or privacy setting for the protection of user privacy, third parties or malicious users can still manage to infer users' private attributes through various privacy inference methods. [1][2][3] Such violations of privacy would pose a serious threat to the individual right of users. To develop effective solutions that can protect user privacy, it is necessary to understand how inference attacks can be effectively launched and what factors play the important role in such attacks.…”

Section: Introductionmentioning

confidence: 99%

Social influence‐based privacy inference attacks in online social networks

Zhu

et al. 2021

Security and Privacy

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Privacy inference attacks in online social networks aim at inferring hidden attributes of target users based on released attributes of the users as well as information about social relationships in the networks. Although many privacy inference methods have been proposed in recent years, most of the methods have mostly focused on pursuing high accuracy of the inference results without paying too much attention on identifying users who make the most contributions to the inference, making it less useful in the development of effective countermeasures. In this paper, by taking full consideration of the roles of users in privacy inference, we propose a privacy inference method based on graph attention networks that can provide information about not only the private attributes of the target users, but also the importance of the users in the process of inferring the results. We also design a novel mechanism for the selection of influential users to make our proposed method adaptive to the characteristics of user influence diffusion, resulting in involving fewer number of users in privacy inference and hence improving the efficiency. We also performed some experiment to evaluate the proposed method using a real online social network dataset to show the effectiveness of the proposed method. Comparison with other comparable methods also shows that the results of our proposed method are more accurate.

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Towards link inference attack against network structure perturbation

Cited by 17 publications

References 63 publications

Progresses and challenges in link prediction

Progresses and challenges in link prediction

k-NDDP: An Efficient Anonymization Model for Social Network Data Release

Social influence‐based privacy inference attacks in online social networks

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