Social Network De-anonymization with Overlapping Communities: Analysis, Algorithm and Experiments

Wu, Xinyu; Hu, Zhongzhao; Fu, Xinzhe; Fu, Luoyi; Wang, Xinbing; Lu, Songwu

doi:10.1109/infocom.2018.8486260

Cited by 18 publications

(7 citation statements)

References 17 publications

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“…The main drawback of this study is the assumption of disjoint communities, which fails to reflect the real-world situations. Wu et al [181] extend Fu et al's study by considering overlapping communities. In contrast to Fu et al's work [58], which uses Maximum a Posteriori estimation to find the correct mappings, Wu et al introduces a new cost function, Minimum Mean Square Error, which minimizes the expected number of mismatched users by incorporating all possible true mappings.…”

Section: Theoretical Analysis and De-anonymizationmentioning

confidence: 81%

A Survey on Privacy in Social Media

Beigi

Liu

2020

ACM/IMS Trans. Data Sci.

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The increasing popularity of social media has attracted a huge number of people to participate in numerous activities on a daily basis. This results in tremendous amounts of rich user-generated data. These data provide opportunities for researchers and service providers to study and better understand users' behaviors and further improve the quality of the personalized services. Publishing user-generated data risks exposing individuals' privacy. Users privacy in social media is an emerging research area and has attracted increasing attention recently. These works study privacy issues in social media from the two different points of views: identification of vulnerabilities and mitigation of privacy risks. Recent research has shown the vulnerability of user-generated data against the two general types of attacks, identity disclosure and attribute disclosure. These privacy issues mandate social media data publishers to protect users' privacy by sanitizing user-generated data before publishing it. Consequently, various protection techniques have been proposed to anonymize user-generated social media data. There is vast literature on privacy of users in social media from many perspectives. In this survey, we review the key achievements of user privacy in social media. In particular, we review and compare the state-of-the-art algorithms in terms of the privacy leakage attacks and anonymization algorithms. We overview the privacy risks from different aspects of social media and categorize the relevant works into five groups: (1) social graphs and privacy, (2) authors in social media and privacy, (3) profile attributes and privacy, (4) location and privacy, and (5) recommendation systems and privacy. We also discuss open problems and future research directions regarding user privacy issues in social media.

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Section: Theoretical Analysis and De-anonymizationmentioning

confidence: 81%

A Survey on Privacy in Social Media

Beigi

Liu

2020

ACM/IMS Trans. Data Sci.

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“…pre-identified node pairs that are known to be correctly matched. However, in many situations, it is difficult to obtain such seed nodes due to the limited access to user profiles [9] [24]. Pedarsani and Grossglauer [19] first studied the seedless de-anonymization problem in the context of Erdős-Rènyi model, and they took the number of mismatched edge as the objective function.…”

Section: Related Work 21 De-anonymization Algorithmsmentioning

confidence: 99%

“…Pedarsani and Grossglauer [19] first studied the seedless de-anonymization problem in the context of Erdős-Rènyi model, and they took the number of mismatched edge as the objective function. A different cost function based on Maximum a Posterior (MAP) was proposed in [17] and also used in [9] [24]. Recent works for correlated Erdős-Rènyi networks were reported in [20] [4]; Nitish and Silvio also proposed algorithm in [14] for the preferential attachment (PA) model.…”

Section: Related Work 21 De-anonymization Algorithmsmentioning

confidence: 99%

“…classical Erdős-Rènyi network [16] [19] [13] [9], correlated Erdős-Rènyi network model [18] [24] [4] and power law model [14], which may not represent real networks. Second, almost all of the previous studies [16] [19] [14] [13] [9] [18] [24] [4] only focus on the asymptotic regime, i.e. the regime where the probability of successfully matching all nodes approaches either 1 or 0, as the number of nodes approaches infinity, while there are no non-asymptotic results on de-anonymizability yet.…”

Section: Introductionmentioning

confidence: 99%

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De-anonymizability of social network

Miao

Wang

et al. 2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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Social network de-anonymization, which refers to re-identifying users by mapping their anonymized network to a correlated network, is an important problem that has received intensive study in network science. However, it remains less understood how network structural features intrinsically affect whether or not the network can be successfully de-anonymized. To find the answer, this paper offers the first general study on the relation between deanonymizability and network symmetry. To this end, we propose to capture the symmetry of a graph by the concept of graph bijective homomorphism. By defining the matching probability matrix, we are able to characterize the de-anonymizability, i.e., the expected number of correctly matched nodes. Specifically, we show that for a graph pair with arbitrary topology, the de-anonymizability is equal to the maximal diagonal sum of the matching probability matrix generated from homomorphisms. Due to the prohibitive cost of enumerating all possible homomorphisms, we further obtain an upper bound of such de-anonymizability by counting the orbits of each of the two graphs, which significantly reduces the computational cost. Such a general result allows us to theoretically obtain the de-anonymizability of any networks with more specific topology structure. For example, for any classic Erdős-Rènyi graph with designated and , we can represent its de-anonymizability numerically by calculating the local symmetric structure that it contains. Extensive experiments are performed to validated our findings. CCS CONCEPTS • Security and privacy → Privacy protections; • Networks → Network privacy and anonymity; • Theory of computation → Random network models.

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“…Lee et al [40] proposed a seedless de-anonymization method incorporating multi-hops neighborhood information and exploiting an improved machine learning technique for matching. Wu et al [41] provided a systematic study on the effect of overlapping communities on deanonymization without seed, aiming at minimizing the de-anonymizaiton error.…”

Section: Structural Attacks Without Seedmentioning

confidence: 99%

Understanding structure-based social network de-anonymization techniques via empirical analysis

Mao

Tian

Jiang

et al. 2018

J Wireless Com Network

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The rapid development of wellness smart devices and apps, such as Fitbit Coach and FitnessGenes, has triggered a wave of interaction on social networks. People communicate with and follow each other based on their wellness activities. Though such IoT devices and data provide a good motivation, they also expose users to threats due to the privacy leakage of social networks. Anonymization techniques are widely adopted to protect users' privacy during social data publishing and sharing. However, de-anonymization techniques are actively studied to identify weaknesses in current social network data-publishing mechanisms. In this paper, we conduct a comprehensive analysis on the typical structure-based social network de-anonymization algorithms. We aim to understand the de-anonymization approaches and disclose the impacts on their application performance caused by different factors, e.g., topology properties and anonymization methods adopted to sanitize original data. We design the analysis framework and define three experiment environments to evaluate a few factors' impacts on the target algorithms. Based on our analysis architecture, we simulate three typical de-anonymization algorithms and evaluate their performance under different pre-configured environments.

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Social Network De-anonymization with Overlapping Communities: Analysis, Algorithm and Experiments

Cited by 18 publications

References 17 publications

A Survey on Privacy in Social Media

A Survey on Privacy in Social Media

De-anonymizability of social network

Understanding structure-based social network de-anonymization techniques via empirical analysis

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