Structural Attack to Anonymous Graph of Social Networks

Tao, Zhu; Wang, Shanshan; Li, Xiangtao; Zhou, Zhiguo; Zhang, Riming

doi:10.1155/2013/237024

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…Their main goal of anonymity scheme is to solve d ‐neighbor attacks with k ‐anonymity scheme. Zhu et al propose an effective structural attack to deanonymize social graph data, which is much different from previous work and provide other researches a new way to study the privacy protection against structural attacks. Benjamin C. M. Fung et al propose the first anonymization algorithm to achieve k ‐anonymity on social network data with the consideration of the minimization of the impact on frequent sharing patterns.…”

Section: Neighborhood Attacks and Protection Schemesmentioning

confidence: 94%

K‐anonymity against neighborhood attacks in weighted social networks

Liu

Yao

et al. 2015

Security Comm Networks

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Nowadays, with the advance of Internet technology, social network is getting popular, which combines the virtual network and the real world. People employ this network to communicate with all social things of their interest, including shopping, making friends, sharing experiences of life, and so on. In social networks, the social data expands rapidly and the malicious users can easily get the social data. With the social information, they could conjecture the relationship among social network data via the systematical analysis tool. Hence, the personal privacy data in social network may be exposed to some unknown risks, and recently, these issues arising in such a network catch much attention. The protection of personal privacy social data becomes an important and urgent research in social networks. One of personal privacy social information is the relations between the individuals and their social groups, namely human relationships.Neighborhood attacks are incident to the exposure of human relationships. Previous studies try to conceal human relationships information with well-known k-anonymity protection to resist this attack in social networks. However, those researches do not take care of those attacks in weighted social network, which does not make sense due to the fact that people should have different relationships with different persons. In such weighted social network, the edge denotes the human relationship and the weight denotes the degree of this human relationship in social networks. Our study focuses on the k-anonymity protection scheme in weighted social networks, and our scheme can achieve k-anonymity protection under the expected conditions, less virtual edges added and fewer weights changed. Through the analysis with MATLAB tool, we show that our k-anonymity algorithm can achieve high anonymity protection rate under various k-anonymity policies.

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Section: Neighborhood Attacks and Protection Schemesmentioning

confidence: 94%

K‐anonymity against neighborhood attacks in weighted social networks

Liu

Yao

et al. 2015

Security Comm Networks

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“…Zhou and Pei [172] proposed neighbourhood attacks, in which the adversary exploits the background knowledge about some target individuals' neighbors and the relationships among the neighbors, to re-identify the victims. Zhu et al [176] proposed n-hop neighFNR, which relied on the regional characteristic of the cumulative degree of n-hop neighbors and learned from the simulated annealing-based graph matching algorithm, to conduct re-identification. Qian et al [113] introduced knowledge graphs to strengthen the auxiliary information available in social networks, making the ability of deanonymization and inference attacks to a higher level.…”

Section: Structure Information-based De-anonymizationmentioning

confidence: 99%

Digital Privacy Under Attack: Challenges and Enablers

Song¹,

Deng²,

Pokhrel³

et al. 2023

Preprint

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Users have renewed interest in protecting their private data in the digital space. When they don't believe that their privacy is sufficiently covered by one platform, they will readily switch to another. Such an increasing level of privacy awareness has made privacy preservation an essential research topic. Nevertheless, new privacy attacks are emerging day by day. Therefore, a holistic survey to compare the discovered techniques on attacks over privacy preservation and their mitigation schemes is essential in the literature. We develop a study to fill this gap by assessing the resilience of privacy-preserving methods to various attacks and conducting a comprehensive review of countermeasures from a broader perspective. First, we introduce the fundamental concepts and critical components of privacy attacks. Second, we comprehensively cover major privacy attacks targeted at anonymous data, statistical aggregate data, and privacy-preserving models. We also summarize popular countermeasures to mitigate these attacks. Finally, some promising future research directions and related issues in the privacy community are envisaged. We believe this survey will successfully shed some light on privacy research and encourage researchers to entirely understand the resilience of different existing privacy-preserving approaches.

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“…It does not work with graphs where their common nodes are at distance of 2-hop from the centre (one node in a pair, or both nodes in a pair) as in [30] which covers n-hop and therefore can deanonymise more data.…”

Section: Deanonymisation Approachesmentioning

confidence: 99%

A Literature Survey and Classifications on Data Deanonymisation

Al-Azizy

Millard

Symeonidis

et al. 2016

Lecture Notes in Computer Science

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Abstract. The problem of disclosing private anonymous data has become increasingly serious particularly with the possibility of carrying out deanonymisation attacks on publishing data. The related work available in the literature is inadequate in terms of the number of techniques analysed, and is limited to certain contexts such as Online Social Networks. We survey a large number of state-of-the-art techniques of deanonymisation achieved in various methods and on different types of data. Our aim is to build a comprehensive understanding about the problem. For this survey, we propose a framework to guide a thorough analysis and classifications. We are interested in classifying deanonymisation approaches based on type and source of auxiliary information and on the structure of target datasets. Moreover, potential attacks, threats and some suggested assistive techniques are identified. This can inform the research in gaining an understanding of the deanonymisation problem and assist in the advancement of privacy protection.

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Structural Attack to Anonymous Graph of Social Networks

Cited by 8 publications

References 21 publications

K‐anonymity against neighborhood attacks in weighted social networks

K‐anonymity against neighborhood attacks in weighted social networks

Digital Privacy Under Attack: Challenges and Enablers

A Literature Survey and Classifications on Data Deanonymisation

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