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On the Privacy and Utility of Anonymized Social Networks

On the Privacy and Utility of Anonymized Social Networks

Yi Song, Xuesong Lu, Sadegh Nobari, Stéphane Bressan, Panagiotis Karras
Copyright: © 2013 |Volume: 4 |Issue: 2 |Pages: 34
ISSN: 1947-9220|EISSN: 1947-9239|EISBN13: 9781466632905|DOI: 10.4018/jaras.2013040101
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MLA

Song, Yi, et al. "On the Privacy and Utility of Anonymized Social Networks." IJARAS vol.4, no.2 2013: pp.1-34. http://doi.org/10.4018/jaras.2013040101

APA

Song, Y., Lu, X., Nobari, S., Bressan, S., & Karras, P. (2013). On the Privacy and Utility of Anonymized Social Networks. International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS), 4(2), 1-34. http://doi.org/10.4018/jaras.2013040101

Chicago

Song, Yi, et al. "On the Privacy and Utility of Anonymized Social Networks," International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS) 4, no.2: 1-34. http://doi.org/10.4018/jaras.2013040101

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Abstract

One is either on Facebook or not. Of course, this assessment is controversial and its rationale arguable. It is nevertheless not far, for many, from the reason behind joining social media and publishing and sharing details of their professional and private lives. Not only the personal details that may be revealed, but also the structure of the networks are sources of invaluable information for any organization wanting to understand and learn about social groups, their dynamics and members. These organizations may or may not be benevolent. It is important to devise, design and evaluate solutions that guarantee some privacy. One approach that reconciles the different stakeholders’ requirement is the publication of a modified graph. The perturbation is hoped to be sufficient to protect members’ privacy while it maintains sufficient utility for analysts wanting to study the social media as a whole. In this paper, the authors try to empirically quantify the inevitable trade-off between utility and privacy. They do so for two state-of-the-art graph anonymization algorithms that protect against most structural attacks, the k-automorphism algorithm and the k-degree anonymity algorithm. The authors measure several metrics for a series of real graphs from various social media before and after their anonymization under various settings.

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