Abstract
We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S,f) where S is a set of rows in the database and f is a function mapping database rows to {0,1}.The true response is ∑ r ∈ S f(DB r ),a noisy version of which is released. Results in [3, 4] show that a strong form of privacy can be maintained using a surprisingly small amount of noise, provided the total number of queries is sublinear in the number n of database rows. We call this a sub-linear queries (SuLQ) database. The assumption of sublinearity becomes reasonable as databases grow increasingly large.
The SuLQ primitive – query and noisy reply – gives rise to a calculus of noisy computation. After reviewing some results of [4] on multi-attribute SuLQ, we illustrate the power of the SuLQ primitive with three examples [2]: principal component analysis, k means clustering, and learning in the statistical queries learning model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adam, N.R., Wortmann, J.C.: Security-Control Methods for Statistical Databases: A Comparative Study. ACM Computing Surveys 21(4), 515–556 (1989)
Blum, A., Dwork, C., McSherry, F., Nissim, K.: On the Power of SuLQ Databases (2004) (manuscript in preparation)
Dinur, I., Nissim, K.: Revealing information while preserving privacy. In: Proceedings of the Twenty-Second ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 202–210 (2003)
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)
Kearns, M.: Efficient Noise-Tolerant Learning from Statistical Queries. JACM 45(6), 983–1006 (1998); See also Proc. 25th ACM STOC, pp. 392–401 (1993)
O’Connel, M.J.: Search Program for Significant Variables. Comp. Phys. Comm. 8 (1974)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dwork, C. (2005). Sub-linear Queries Statistical Databases: Privacy with Power. In: Menezes, A. (eds) Topics in Cryptology – CT-RSA 2005. CT-RSA 2005. Lecture Notes in Computer Science, vol 3376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30574-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-540-30574-3_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24399-1
Online ISBN: 978-3-540-30574-3
eBook Packages: Computer ScienceComputer Science (R0)