Fast De-anonymization of Social Networks with Structural Information

Shao, Yingxia; Liu, Jialin; Shi, Shuyang; Zhang, Yuemei; Cui, Bin

doi:10.1007/s41019-019-0086-8

Cited by 32 publications

(24 citation statements)

References 22 publications

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“…Combining formula (4) and formula (5), the posterior probability of visiting a hidden location is given in formula (6): P u,l = 3 P prior u,l · P(∆s ≤ ∆t) = 3 P(x u,l ) · P(y u,l ) · P(z u,l ) · P(∆s ≤ ∆t)…”

Section: Hlpi: Hidden Location Inference Model Based On Multi-factor mentioning

confidence: 99%

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Mobile User Location Inference Attacks Fusing with Multiple Background Knowledge in Location-Based Social Networks

Pan

Weizhang

2020

Mathematics

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Location-based social networks have been widely used. However, due to the lack of effective and safe data management, a large number of privacy disclosures commonly occur. Thus, academia and industry have needed to focus more on location privacy protection. This paper proposes a novel location attack method using multiple background options to infer the hidden locations of mobile users. In order to estimate the possibility of a hidden position being visited by a user, two hidden location attack models are proposed, i.e., a Bayesian hidden location inference model and the multi-factor fusion based hidden location inference model. Multiple background factors, including the check-in sequences, temporal information, user social networks, personalized service preferences, point of interest (POI) popularities, etc., are considered in the two models. Moreover, a hidden location inference algorithm is provided as well. Finally, a series of experiments are conducted on two real check-in data examples to evaluate the accuracy of the model and verify the validity of the proposed algorithm. The experimental results show that multiple background knowledge fusion provides benefits for improving location inference precision.

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Section: Hlpi: Hidden Location Inference Model Based On Multi-factor mentioning

confidence: 99%

“…However, due to the lack of effective and safe data management, users' privacy is under the risk of leakage. Many research efforts have been devoted to investigating location privacy protection [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Mobile User Location Inference Attacks Fusing with Multiple Background Knowledge in Location-Based Social Networks

Pan

Weizhang

2020

Mathematics

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“…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%

“…The problem is initially proposed by Narayanan and Shimatikov [16]. In the past decades, a large number of works [16] [20] have emerged focusing on the de-anonymization problem with different aspect.…”

Section: Introductionmentioning

confidence: 99%

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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“…RoleSim, conceived by Jin et al [9], is a promising role-oriented graph-theoretic measure that quantifies the similarity between two objects based on graph automorphism, with a proliferation of real-life applications [9,10,25], such as link prediction (social network), co-citation analysis (bibliometrics), motif discovery (bioinformatics), and collaborative filtering (information retrieval). It recursively follows a SimRank-like reasoning that "two nodes are assessed as role similar if they interact with automorphically equivalent sets of in-neighbors".…”

Section: Introductionmentioning

confidence: 99%

RoleSim*: Scaling axiomatic role-based similarity ranking on large graphs

Iranmanesh

Haldar

et al. 2021

World Wide Web

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RoleSim and SimRank are among the popular graph-theoretic similarity measures with many applications in, e.g., web search, collaborative filtering, and sociometry. While RoleSim addresses the automorphic (role) equivalence of pairwise similarity which SimRank lacks, it ignores the neighboring similarity information out of the automorphically equivalent set. Consequently, two pairs of nodes, which are not automorphically equivalent by nature, cannot be well distinguished by RoleSim if the averages of their neighboring similarities over the automorphically equivalent set are the same. To alleviate this problem: 1) We propose a novel similarity model, namely RoleSim*, which accurately evaluates pairwise role similarities in a more comprehensive manner. RoleSim* not only guarantees the automorphic equivalence that SimRank lacks, but also takes into account the neighboring similarity information outside the automorphically equivalent sets that are overlooked by RoleSim. 2) We prove the existence and uniqueness of the RoleSim* solution, and show its three axiomatic properties (i.e., symmetry, boundedness, and non-increasing monotonicity). 3) We provide a concise bound for iteratively computing RoleSim* formula, and estimate the number of iterations required to attain a desired accuracy. 4) We induce a distance metric based on RoleSim* similarity, and show that the RoleSim* metric fulfills the triangular inequality, which implies the sum-transitivity of its similarity scores. 5) We present a threshold-based RoleSim* model that reduces the computational time further with provable accuracy guarantee. 6) We propose a single-source RoleSim* model, which scales well for sizable graphs. 7) We also devise methods to scale RoleSim* based search by incorporating its triangular inequality property with partitioning techniques. Our experimental results on real datasets demonstrate that RoleSim* achieves higher accuracy than its competitors while scaling well on sizable graphs with billions of edges.

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Fast De-anonymization of Social Networks with Structural Information

Cited by 32 publications

References 22 publications

Mobile User Location Inference Attacks Fusing with Multiple Background Knowledge in Location-Based Social Networks

Mobile User Location Inference Attacks Fusing with Multiple Background Knowledge in Location-Based Social Networks

De-anonymizability of social network

RoleSim*: Scaling axiomatic role-based similarity ranking on large graphs

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