ABSTRACT
The proliferation of network data in various application domains has raised privacy concerns for the individuals involved. Recent studies show that simply removing the identities of the nodes before publishing the graph/social network data does not guarantee privacy. The structure of the graph itself, and in its basic form the degree of the nodes, can be revealing the identities of individuals. To address this issue, we study a specific graph-anonymization problem. We call a graph k-degree anonymous if for every node v, there exist at least k-1 other nodes in the graph with the same degree as v. This definition of anonymity prevents the re-identification of individuals by adversaries with a priori knowledge of the degree of certain nodes. We formally define the graph-anonymization problem that, given a graph G, asks for the k-degree anonymous graph that stems from G with the minimum number of graph-modification operations. We devise simple and efficient algorithms for solving this problem. Our algorithms are based on principles related to the realizability of degree sequences. We apply our methods to a large spectrum of synthetic and real datasets and demonstrate their efficiency and practical utility.
- Aggarwal, C. C., and Yu, P. S. Privacy-Preserving Data Mining: Models and Algorithms, vol. 34 of Advances in Database Systems. Springer, 2008. Google ScholarDigital Library
- Backstrom, L., Dwork, C., and Kleinberg, J. M. Wherefore art thou R3579X?: Anonymized social networks, hidden patterns, and structural steganography. In Proceedings of the 16th International Conference on World Wide Web (WWW'07) (Alberta, Canada, May 2007), pp. 181--190. Google ScholarDigital Library
- Barabási, A.-L., and Albert, R. Emergence of scaling in random networks. Science 286, 5439 (October 1999), 509--512.Google Scholar
- Bayardo, R. J., and Agrawal, R. Data privacy through optimal k-anonymization. In Proceedings of the 21st International Conference on Data Engineering (ICDE'05) (Tokyo, Japan, April 2005), pp. 217--228. Google ScholarDigital Library
- Cormen, T., Leiserson, C., and Rivest, R. Introduction to Algorithms. MIT Press, 1990. Google ScholarDigital Library
- Erdös, P., and Gallai, T. Graphs with prescribed degrees of vertices. Mat. Lapok (1960).Google Scholar
- Frikken, K. B., and Golle, P. Private social network analysis: How to assemble pieces of a graph privately. In Proceedings of the 5th ACM Workshop on Privacy in Electronic Society (WPES'06) (Alexandria, VA, 2006), pp. 89--98. Google ScholarDigital Library
- Getoor, L., and Diehl, C. P. Link mining: a survey. ACM SIGKDD Explorations Newsletter 7, 2 (2005), 3--12. Google ScholarDigital Library
- Hakimi, S. L. On realizability of a set of integers as degrees of the vertices of a linear graph. Journal of the Society for Industrial and Applied Mathematics 10, 3 (1962), 496--506.Google ScholarCross Ref
- Hay, M., Miklau, G., Jensen, D., Weis, P., and Srivastava, S. Anonymizing social networks. Technical report, University of Massachusetts Amherst, 2007.Google Scholar
- Lee, Y.-S. Graphical demonstration of an optimality property of the median. The American Statistician 49, 4 (November 1995), 369--372.Google Scholar
- Machanavajjhala, A., Gehrke, J., Kifer, D., and Venkitasubramaniam, M. l-diversity: Privacy beyond k-anonymity. In Proceedings of the 22nd International Conference on Data Engineering (ICDE'06) (Atlanta, GA, April 2006), p. 24. Google ScholarDigital Library
- Meyerson, A., and Williams, R. On the complexity of optimal k-anonymity. In Proceedings of the Twenty-third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS'04) (Paris, France, 2004), pp. 223--228. Google ScholarDigital Library
- Pei, J., and Zhou, B. Preserving privacy in social networks against neighborhood attacks. In Proceedings of the 24th International Conference on Data Engineering (ICDE'08) (Cancun, Mexico, April 2008). Google ScholarDigital Library
- Samarati, P., and Sweeney, L. Generalizing data to provide anonymity when disclosing information. In Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS'98) (Seattle, WA, 1998), p. 188. Google ScholarDigital Library
- Watts, D. J. Networks, dynamics, and the small-world phenomenon. American Journal of Sociology 105, 2 (September 1999), 493--527.Google ScholarCross Ref
- Watts, D. J., and Strogatz, S. H. Collective dynamics of small-world networks. Nature 393, 6684 (June 1998), 409--410.Google Scholar
- Ying, X., and Wu, X. Randomizing social networks: a spectrum preserving approach. In Proceedings of SIAM International Conference on Data Mining (SDM'08) (Atlanta, GA, April 2008).Google ScholarCross Ref
- Zheleva, E., and Getoor, L. Preserving the privacy of sensitive relationships in graph data. In Proceedings of the International Workshop on Privacy, Security, and Trust in KDD (PinKDD'07) (San Jose, CA, August 2007). Google ScholarDigital Library
Index Terms
- Towards identity anonymization on graphs
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