Conditional anonymity with non-probabilistic adversary

Chen, Weien; Cao, Yongzhi; Wang, Hanpin

doi:10.1016/j.ins.2015.06.018

Cited by 2 publications

(2 citation statements)

References 48 publications

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“…We do so because the privacy level of the perturbed graph is measured in terms of the initial threats present in the original graph. This is consistent with previous uses of the terminology conditional privacy in microdata anonymisation [5,9], to reflect the fact that the privacy of a dataset is measured in terms of the a priori adversary knowledge.…”

Section: Definition 33 ((K )-Anonymity)supporting

confidence: 87%

Conditional adjacency anonymity in social graphs under active attacks

2018

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Social network data is typically made available in a graph format, where users and their relations are represented by vertices and edges, respectively. In doing so, social graphs need to be anonymised to resist various privacy attacks. Among these, the so-called active attacks, where an adversary has the ability to enrol sybil accounts in the social network, have proven difficult to counteract. In this article, we provide an anonymisation technique that successfully thwarts active attacks while causing low structural perturbation. We achieve this goal by introducing (k, G,)-adjacency anonymity: a privacy property based on (k,)-anonymity that alleviates the computational burden suffered by anonymisation algorithms based on (k,)-anonymity and relaxes some of its assumptions on the adversary capabilities. We show that the proposed method is efficient and establish tight bounds on the number of modifications that it performs on the original graph. Experimental results on real-life and randomly generated graphs show that when compared to methods based on (k,)-anonymity, the new method continues to provide protection from equally capable active attackers while introducing a much smaller number of changes in the graph structure.

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Section: Definition 33 ((K )-Anonymity)supporting

confidence: 87%

Conditional adjacency anonymity in social graphs under active attacks

2018

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“…In 2015, Chen et al [109] proposed the concept of settheoretic conditional anonymity by considering the threat from non-probabilistic adversary. They also proposed a metric for set-theoretic conditional anonymity and a variant of an existing metric for probabilistic conditional anonymity to evaluate system's degree of anonymity quantitatively.…”

Section: A Metrics Based On Set Theorymentioning

confidence: 99%

A Survey on Measuring Anonymity in Anonymous Communication Systems

Wang

2019

IEEE Access

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The popularity of Internet applications has made communication privacy an increasingly important security requirement. As an important aspect of privacy, anonymity ensures that a subject may use resources or services without disclosing user identity or corresponding relationships. Since the seminal work by Chaum for anonymous communication, many different anonymous communication systems, and anonymous protocols have been developed and investigated extensively. In recent years, anonymous communication systems have evolved from academic tools used by specialists to mass-market software used by millions of ordinary people. How to evaluate and quantify the anonymity that different anonymous communication systems can offer has been a new focus. Though some efforts have been made on anonymity metrics and measuring techniques, systematic research on measuring anonymity of anonymous communication systems is still needed. In this paper, we give a comprehensive overview of state-of-the-art research on measuring anonymity. We first summarize the anonymous mechanisms applied by different anonymous systems. We then introduce the formalization of the notion of anonymity for measuring the anonymity degree. Further, metrics based on various theories and approaches are reviewed. We particularly elaborate on the metrics based on information theory as a separate section because of its popularity and multiple branches. The metrics based on information entropy is probably of the greatest interest.

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Conditional anonymity with non-probabilistic adversary

Cited by 2 publications

References 48 publications

Conditional adjacency anonymity in social graphs under active attacks

Conditional adjacency anonymity in social graphs under active attacks

A Survey on Measuring Anonymity in Anonymous Communication Systems

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