Differential Privacy and the Risk-Utility Tradeoff for Multi-dimensional Contingency Tables
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The methodology of differential privacy has provided a strong definition of privacy which in some settings, using a mechanism of doubly-exponential noise addition, also allows for extraction of informative statistics from databases. A recent paper extends this approach to the release of a specified set of margins from a multi-way contingency table. Privacy protection in such settings implicitly focuses on small cell counts that might allow for the identification of units that are unique in the database. We explore how well the mechanism works in the context of a series of examples, and the extent to which the proposed differential-privacy mechanism allows for sensible inferences from the released data.
KeywordsContingency Table Maximum Likelihood Estimator Privacy Protection Differential Privacy Total Variation Distance
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- 1.Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., Talwar, K.: Privacy, accuracy, and consistency too: A holistic solution to contingency table release. In: Proceedings of the 26th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (2007)Google Scholar
- 3.Christiansen, S.K., Giese, H.: Genetic analysis of obligate barley powdery mildew fungus based on rfpl and virulence loci. Theoretical and Applied Genetics 79, 705–712 (1991)Google Scholar
- 4.Dobra, A., Fienberg, S.E., Rinaldo, A., Slavkovic, A.B., Zhou, Y.: Algebraic statistics and contingency table problems: Log-linear models, likelihood estimation, and disclosure limitation. In: Putinar, M., Sullivant, S. (eds.) Emerging Applications of Algebraic Geometry. IMA Series in Applied Mathematics, pp. 63–88. Springer, Heidelberg (2008)Google Scholar
- 5.Duncan, G.T., Fienberg, S.E., Krishnan, R., Padman, R., Roehrig, S.F.: Disclosure limitation methods and information loss for tabular data. In: Doyle, P., Lane, J., Theeuwes, J., Zayatz, L. (eds.) Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, pp. 135–166. Elsevier, Amsterdam (2001)Google Scholar
- 11.Fienberg, S.E., Slavkovic, A.B.: A survey of statistical approaches to preserving confi- dentiality of contingency table entries. In: Aggarwal, C., Yu, P.S. (eds.) Privacy Preserving Data Mining: Models and Algorithms, pp. 289–310. Springer, Heidelberg (2008)Google Scholar
- 12.Lauritzen, S.L.: Graphical Models. Oxford University Press, Oxford (1996)Google Scholar
- 15.Winkler, W.: General Discret-data Modeling Methods for Producing Synthetic Data with Reduced Re-identification Risk that Preserve Analytic Properties. Research Report Series, Statistics 2010-02 (2008)Google Scholar