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Algorithms 2016, 9(4), 85; doi:10.3390/a9040085

A Differentiated Anonymity Algorithm for Social Network Privacy Preservation

School of Management Science and Engineering, Shandong Normal University, Jinan 250014, China
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Academic Editor: Francesco Bergadano
Received: 8 October 2016 / Revised: 24 November 2016 / Accepted: 7 December 2016 / Published: 14 December 2016
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Abstract

Devising methods to publish social network data in a form that affords utility without compromising privacy remains a longstanding challenge, while many existing methods based on k-anonymity algorithms on social networks may result in nontrivial utility loss without analyzing the social network topological structure and without considering the attributes of sparse distribution. Toward this objective, we explore the impact of the attributes of sparse distribution on data utility. Firstly, we propose a new utility metric that emphasizes network structure distortion and attribute value loss. Furthermore, we design and implement a differentiated k-anonymity l-diversity social network anonymity algorithm, which seeks to protect users’ privacy in social networks and increase the usability of the published anonymized data. Its key idea is that it divides a node into two child nodes and only anonymizes sensitive values to satisfy anonymity requirements. The evaluation results show that our method can effectively improve the data utility as compared to generalized anonymizing algorithms. View Full-Text
Keywords: social network; privacy; data utility; anonymity; differentiated social network; privacy; data utility; anonymity; differentiated
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xie, Y.; Zheng, M. A Differentiated Anonymity Algorithm for Social Network Privacy Preservation. Algorithms 2016, 9, 85.

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