Abstract
Differential privacy is a recent notion, and while it is nice conceptually it has been difficult to apply in practice. The parameters of differential privacy have an intuitive theoretical interpretation, but the implications and impacts on the risk of disclosure in practice have not yet been studied, and choosing appropriate values for them is non-trivial. Although the privacy parameter ε in differential privacy is used to quantify the privacy risk posed by releasing statistics computed on sensitive data, ε is not an absolute measure of privacy but rather a relative measure. In effect, even for the same value of ε, the privacy guarantees enforced by differential privacy are different based on the domain of attribute in question and the query supported. We consider the probability of identifying any particular individual as being in the database, and demonstrate the challenge of setting the proper value of ε given the goal of protecting individuals in the database with some fixed probability.
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References
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 Twenty-Sixth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 273–282. ACM, New York (2007)
Blum, A., Dwork, C., McSherry, F., Nissim, K.: Practical privacy: the SuLQ framework. In: Proceedings of the Twenty-Fourth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 128–138. ACM, New York (2005)
Blum, A., Ligett, K., Roth, A.: A learning theory approach to non-interactive database privacy. In: Proceedings of the 40th Annual ACM Symposium on Theory of Computing, pp. 609–618. ACM, New York (2008)
Dalenius, T.: Towards a methodology for statistical disclosure control. Statistik Tidskrift 15(429-444), 2–1 (1977)
Dinur, I., Nissim, K.: Revealing information while preserving privacy. In: Proceedings of the Twenty-Second ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 202–210. ACM, New York (2003)
Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006, Part II. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)
Dwork, C.: Differential privacy: A survey of results. In: Agrawal, M., Du, D.-Z., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008)
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our data, ourselves: Privacy via distributed noise generation. In: Vaudenay, S. (ed.) EUROCRYPT 2006. LNCS, vol. 4004, pp. 486–503. Springer, Heidelberg (2006)
Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006)
Dwork, C., Nissim, K.: Privacy-preserving datamining on vertically partitioned databases. In: Franklin, M. (ed.) CRYPTO 2004. LNCS, vol. 3152, pp. 528–544. Springer, Heidelberg (2004)
Kasiviswanathan, S., Smith, A.: A note on differential privacy: Defining resistance to arbitrary side information. Arxiv preprint arXiv:0803.3946 (2008)
McSherry, F.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: Proceedings of the 35th SIGMOD International Conference on Management of Data, pp. 19–30. ACM, New York (2009)
Nergiz, M.E., Clifton, C.: δ-presence without complete world knowledge. IEEE Transactions on Knowledge and Data Engineering 22(6), 868–883 (2010), http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.125
Nergiz, M., Atzori, M., Clifton, C.: Hiding the presence of individuals from shared databases. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, Beijing, China, June 11-14, pp. 665–676 (2007), http://doi.acm.org/10.1145/1247480.1247554
Nissim, K., Raskhodnikova, S., Smith, A.: Smooth sensitivity and sampling in private data analysis. In: Proceedings of the Thirty-Ninth Annual ACM Symposium on Theory of Computing, pp. 75–84. ACM, New York (2007)
Roy, I., Setty, S., Kilzer, A., Shmatikov, V., Witchel, E.: Airavat: Security and privacy for MapReduce. In: Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation, p. 20. USENIX Association (2010)
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Lee, J., Clifton, C. (2011). How Much Is Enough? Choosing ε for Differential Privacy. In: Lai, X., Zhou, J., Li, H. (eds) Information Security. ISC 2011. Lecture Notes in Computer Science, vol 7001. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24861-0_22
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DOI: https://doi.org/10.1007/978-3-642-24861-0_22
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