2005 | OriginalPaper | Buchkapitel
Sub-linear Queries Statistical Databases: Privacy with Power
verfasst von : Cynthia Dwork
Erschienen in: Topics in Cryptology – CT-RSA 2005
Verlag: Springer Berlin Heidelberg
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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.