An Zhu
An Zhu is currently a Staff Software Engineer at Google. Her home page is at: http://www.cs.stanford.edu/~anzhu.
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Achieving anonymity via clustering
Tomás Feder
Krishnaram Kenthapadi
Samir Khuller
Rina Panigrahy
Dilys Thomas
ACM Transactions on Algorithms, 6 (2010), 49:1-49:19
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Publishing data for analysis from a table containing personal records, while maintaining individual privacy, is a problem of increasing importance today. The traditional approach of
de-identifying records is to remove identifying fields such as social security number, name etc. However, recent research has shown that a large fraction of the US population can be
identified using non-key attributes (called quasi-identifiers) such as date of birth, gender, and zip code. Sweeney proposed the k-anonymity model for privacy where non-key
attributes that leak information are suppressed or generalized so that, for every record in the modified table, there are at least k−1 other records having exactly the same values for
quasi-identifiers. We propose a new method for anonymizing data records, where quasi-identifiers of data records are first clustered and then cluster centers are published. To
ensure privacy of the data records, we impose the constraint that each cluster must contain no fewer than a pre-specified number of data records. This technique is more general since we have a much larger choice for cluster centers than k-Anonymity. In many cases, it lets us release a lot more information without compromising privacy. We also provide constant-factor approximation algorithms to come up with such a clustering. This is the first set of algorithms for the anonymization problem where the performance is independent of the anonymity parameter k. We further observe that a few outlier points can significantly increase the cost of anonymization. Hence, we extend our algorithms to allow an epsilon fraction of points to remain unclustered, i.e., deleted from the anonymized publication. Thus, by not releasing a small fraction of the database records, we can ensure that the data published for analysis has less distortion and hence is more useful. Our approximation algorithms for new clustering objectives are of independent interest and could be applicable in other clustering scenarios as well.
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Asymptotic Polynomial-Time Approximation Schemes. Handbook of Approximation Algorithms and Metaheuristics.
Rajeev Motwani
Liadan O'Callaghan
Handbook of Approximation Algorithms and Metaheuristics, Teofilo Gonzalez, ed., Chapman and Hall/CRC (2007)
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Approximation schemes are presented for BIN
PACKING, including a PAS due to Vega and Lueker, and an FPAS due to
Karmakar and Karp. It is shown that the latter can be modified into an
approximation algorithm whose absolute error is bounded by a
poly-logarithmic function in the value of the optimal solution.
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Achieving Anonymity via Clustering
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Tomás Feder
Krishnaram Kenthapadi
Samir Khuller
Rina Panigrahy
Dilys Thomas
Proceedings of the 25th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS) (2006), pp. 153-162
