De-anonymizing Scale-Free Social Networks by Using Spectrum Partitioning Method

Volume: 147, Pages: 441 - 445
Published: Jan 1, 2019
Abstract
Social network data is widely shared, forwarded and published to third parties, which led to the risks of privacy disclosure. Even thought the network provider always perturbs the data before publishing it, attackers can still recover anonymous data according to the collected auxiliary information. In this paper, we transform the problem of de-anonymization into node matching problem in graph, and the de-anonymization method can reduce the...
Paper Details
Title
De-anonymizing Scale-Free Social Networks by Using Spectrum Partitioning Method
Published Date
Jan 1, 2019
Volume
147
Pages
441 - 445
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