k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Preservation of Privacy in Publishing Social Network Data
ISECS '08 Proceedings of the 2008 International Symposium on Electronic Commerce and Security
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
k-symmetry model for identity anonymization in social networks
Proceedings of the 13th International Conference on Extending Database Technology
A New Approach to Manage Security against Neighborhood Attacks in Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
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The proliferation of social networks in digital media has proved to be fruitful, but this rise in popularity is accompanied by user privacy concerns. Social network data has been published in various ways and preserving the privacy of individuals in the published data has become an important concern. Several algorithms have been developed for privacy preservation in relational data, but these algorithms cannot be applied directly to social networks as the nodes here have structural properties along with labels. In this paper, we propose an algorithm to achieve k-anonymity and l-diversity in social network data which provides structural anonymity along with sensitive attribute protection. The proposed algorithm uses novel edge addition techniques which are also presented in this paper. We also propose a concept of partial anonymity to reduce anonymization cost for d1. The empirical study shows that our algorithm requires significantly less number of edge additions for anonymization of social network data and has a substantially lower running time than the other algorithms previously proposed in the field.