Abstract
Differential privacy is a well established definition guaranteeing that queries to a database do not reveal “too much” information about specific individuals who have contributed to the database. The standard definition of differential privacy is information theoretic in nature, but it is natural to consider computational relaxations and to explore what can be achieved with respect to such notions. Mironov et al. (Crypto 2009) and McGregor et al. (FOCS 2010) recently introduced and studied several variants of computational differential privacy, and show that in the two-party setting (where data is split between two parties) these relaxations can offer significant advantages.
Left open by prior work was the extent, if any, to which computational differential privacy can help in the usual client/server setting where the entire database resides at the server, and the client poses queries on this data. We show, for queries with output in ℝn (for constant n) and with respect to a large class of utilities, that any computationally private mechanism can be converted to a statistically private mechanism that is equally efficient and achieves roughly the same utility.
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References
Blum, A., Dwork, C., McSherry, F., Nissim, K.: Practical privacy: The SuLQ framework. In: 24th ACM Symposium on Principles of Database Systems (PODS), pp. 128–138. ACM Press, New York (2005)
Blum, A., Ligett, K., Roth, A.: A learning theory approach to non-interactive database privacy. In: 40th Annual ACM Symposium on Theory of Computing (STOC), pp. 609–618. ACM Press, New York (2008)
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., 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)
Gennaro, R., Gertner, Y., Katz, J., Trevisan, L.: Bounds on the efficiency of generic cryptographic constructions. SIAM Journal on Computing 35(1), 217–246 (2005)
Impagliazzo, R., Rudich, S.: Limits on the provable consequences of one-way permutations. In: 21st Annual ACM Symposium on Theory of Computing (STOC), pp. 44–61. ACM Press, New York (1989)
Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S., Smith, A.: What can we learn privately? In: 49th Annual Symposium on Foundations of Computer Science (FOCS), pp. 531–540. IEEE, Los Alamitos (2008)
McGregor, A., Mironov, I., Pitassi, T., Reingold, O., Talwar, K., Vadhan, S.P.: The limits of two-party differential privacy. In: 51st Annual Symposium on Foundations of Computer Science (FOCS), pp. 81–90. IEEE, Los Alamitos (2010)
Mironov, I., Pandey, O., Reingold, O., Vadhan, S.: Computational differential privacy. In: Halevi, S. (ed.) CRYPTO 2009. LNCS, vol. 5677, pp. 126–142. Springer, Heidelberg (2009)
Nissim, K., Raskhodnikova, S., Smith, A.: Smooth sensitivity and sampling in private data analysis. In: 39th Annual ACM Symposium on Theory of Computing (STOC), pp. 75–84. ACM Press, New York (2007)
Reingold, O., Trevisan, L., Vadhan, S.P.: Notions of reducibility between cryptographic primitives. In: Naor, M. (ed.) TCC 2004. LNCS, vol. 2951, pp. 1–20. Springer, Heidelberg (2004)
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© 2011 International Association for Cryptologic Research
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Groce, A., Katz, J., Yerukhimovich, A. (2011). Limits of Computational Differential Privacy in the Client/Server Setting. In: Ishai, Y. (eds) Theory of Cryptography. TCC 2011. Lecture Notes in Computer Science, vol 6597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19571-6_25
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DOI: https://doi.org/10.1007/978-3-642-19571-6_25
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