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.
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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
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DOI: https://doi.org/10.1007/978-3-540-30574-3_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24399-1
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