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A Model for Privacy Preserving Publication of Social Network Data  

Sung, Min-Kyung (고려대학교 컴퓨터 전파통신공학과)
Chung, Yon-Dohn (고려대학교 컴퓨터학과)
Publication Information
Journal of KIISE:Databases / v.37, no.4, 2010 , pp. 209-219 More about this Journal
Abstract
Online social network services that are rapidly growing recently store tremendous data and analyze them for many research areas. To enhance the effectiveness of information, companies or public institutions publish their data and utilize the published data for many purposes. However, a social network containing information of individuals may cause a privacy disclosure problem. Eliminating identifiers such as names is not effective for the privacy protection, since private information can be inferred through the structural information of a social network. In this paper, we consider a new complex attack type that uses both the content and structure information, and propose a model, $\ell$-degree diversity, for the privacy preserving publication of the social network data against such attacks. $\ell$-degree diversity is the first model for applying $\ell$-diversity to social network data publication and through the experiments it shows high data preservation rate.
Keywords
Social network; Privacy; Data publication; k-anonymity; $\ell$-diversity; $\ell$-degree diversity;
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