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

EURASIP Journal on Wireless Communications and Networking

Zhihong Zhou¹  Jianwei Liu²  Wenqian Tian²  Jian Mao²  Zhaoyuan He³  Jingbo Jiang³ 

[1] Laboratory of Integrate Administration Technologies for Information Security, Shanghai Jiao Tong University;School of Cyber Science and Technology, Beihang University;School of Electronic and Information Engineering, Beihang University;

关键词: Social Network;    De-anonymization;    Privacy;   

DOI  :  10.1186/s13638-018-1291-2

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown