Links Reconstruction Attack

Fire, Michael; Katz, Gilad; Rokach, Lior; Elovici, Yuval

doi:10.1007/978-1-4614-4139-7_9

Cited by 16 publications

(3 citation statements)

References 17 publications

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“…To mitigate the vulnerability of network anonymization mechanisms, Zhang et al [28] developed an enhanced network anonymization method by generating fake edges as plausible as possible. Fire et al [29] presented a classifier based link reconstruction attack method to identify sensitive relationships. Wu et al [30] defined a low-rank approximation based de-anonymization algorithm to reconstruct a network from link randomized observation.…”

Section: B Network Data De-anonymizationmentioning

confidence: 99%

Multi-View Low-Rank Coding-Based Network Data De-Anonymization

Xian

Qiao

et al. 2020

IEEE Access

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Social networks are extensively exploited by third-party consumers such as researchers and advertisers to understand user characteristics and behaviors. In general, before network data is published, sensitive relationships should be anonymized to prevent the compromise of individual privacy. To quantify the guarantee level of privacy-preserving mechanisms and mitigate users' privacy concerns, numerous studies concerning network data de-anonymization have been carried out. However, most existing studies focus on single-view data, and privacy protection for multi-view data that is ubiquitous in the era of big data has not been yet extensively explored. In this study, we are interested in answering the following question: Are the traditional privacy protection methods still valid for the anonymization of multi-view data? In this study, we propose a Multi-View Low-Rank Coding (MVLRC) based network data de-anonymization framework to assess the vulnerability of privacy protection techniques by accurately reconstructing a large portion of the original data. Specifically, the framework assumes that in principle, the target and auxiliary networks have common structural patterns, and they can be modeled together to infer the hidden structure of the target network. The essential components of our work include the following: (1) a robust network representation model for structural pattern learning; (2) the network representation based multiview modeling of target network and auxiliary network; (3) the inference of the anonymized links via target network reconstruction. Experimental results on synthetic networks and three real-world networks demonstrate that auxiliary networks can be utilized by malicious adversaries for privacy inference attacks. Thus, the privacy protection of multi-view network data needs more sophisticated anonymization techniques. INDEX TERMS Privacy preserving, de-anonymization, network data, multi-view learning, low-rank coding.

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Section: B Network Data De-anonymizationmentioning

confidence: 99%

Multi-View Low-Rank Coding-Based Network Data De-Anonymization

Xian

Qiao

et al. 2020

IEEE Access

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“…Even if the data owner removes sensitive links from the published network dataset, it may still be disclosed by link prediction and consequently lead to privacy breach. Michael et al [26] presented a link reconstruction attack, which is a method that attacker can use link prediction to infer a user's connections to others with high accuracy, but they did not mention how to defend the so-called linkreconstruction attack. Naturally, one can consider finding a way to prevent the link-prediction attack.…”

Section: Introductionmentioning

confidence: 99%

Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations

Zhao

et al. 2019

IEEE Trans. Knowl. Data Eng.

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In social networks, by removing some target-sensitive links, privacy protection might be achieved. However, some hidden links can still be re-observed by link prediction methods on observable networks. In this paper, the conventional link prediction method named Resource Allocation Index (RA) is adopted for privacy attacks. Several defense methods are proposed, including heuristic and evolutionary approaches, to protect targeted links from RA attacks via evolutionary perturbations. This is the first time to study privacy protection on targeted links against link-prediction-based attacks. Some links are randomly selected from the network as targeted links for experimentation. The simulation results on six real-world networks demonstrate the superiority of the evolutionary perturbation approach for target defense against RA attacks. Moreover, transferring experiments show that, although the evolutionary perturbation approach is designed to against RA attacks, it is also effective against other link-prediction-based attacks.

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“…If used with malicious intent, link prediction algorithms may constitute a serious threat to both the privacy and the security of the general public. In particular, inspired by the saying "tell me who your friends are and I'll tell you who are", a network analyser may use link-prediction algorithms to perform a link reconstruction attack [16], which not only reveals your undisclosed "friends", but may also enhance the severity of the more general attribute inference attack [27] whereby the goal is to infer various private information about "who you are" [47,35].…”

Section: Introductionmentioning

confidence: 99%

Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network

Waniek,

Zhou,

Vorobeychik

et al. 2018

Preprint

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Link prediction is one of the fundamental research problems in network analysis. Intuitively, it involves identifying the edges that are most likely to be added to a given network, or the edges that appear to be missing from the network when in fact they are present. Various algorithms have been proposed to solve this problem over the past decades. For all their benefits, such algorithms raise serious privacy concerns, as they could be used to expose a connection between two individuals who wish to keep their relationship private. With this in mind, we investigate the ability of such individuals to evade link prediction algorithms. More precisely, we study their ability to strategically alter their connections so as to increase the probability that some of their connections remain unidentified by link prediction algorithms. We formalize this question as an optimization problem, and prove that finding an optimal solution is NP-complete. Despite this hardness, we show that the situation is not bleak in practice. In particular, we propose two heuristics that can easily be applied by members of the general public on existing social media. We demonstrate the effectiveness of those heuristics on a wide variety of networks and against a plethora of link prediction algorithms.

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Links Reconstruction Attack

Cited by 16 publications

References 17 publications

Multi-View Low-Rank Coding-Based Network Data De-Anonymization

Multi-View Low-Rank Coding-Based Network Data De-Anonymization

Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations

Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network

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