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 answer is ΣiεS f(di), and a noisy version is released as the response to the query. Results of Dinur, Dwork, and Nissim show that a strong form of privacy can be maintained using a surprisingly small amount of noise -- much less than the sampling error -- provided the total number of queries is sublinear in the number of database rows. We call this query and (slightly) noisy reply the SuLQ (Sub-Linear Queries) primitive. The assumption of sublinearity becomes reasonable as databases grow increasingly large.We extend this work in two ways. First, we modify the privacy analysis to real-valued functions f and arbitrary row types, as a consequence greatly improving the bounds on noise required for privacy. Second, we examine the computational power of the SuLQ primitive. We show that it is very powerful indeed, in that slightly noisy versions of the following computations can be carried out with very few invocations of the primitive: principal component analysis, k means clustering, the Perceptron Algorithm, the ID3 algorithm, and (apparently!) all algorithms that operate in the in the statistical query learning model [11].

Keywords

Computer scienceRowDifferential privacyViewSublinear functionDatabaseNoise (video)Set (abstract data type)Theoretical computer scienceCluster analysisData miningAlgorithmMathematicsDatabase designDiscrete mathematicsArtificial intelligence

Affiliated Institutions

Related Publications

Publication Info

Year
2005
Type
article
Citations
813
Access
Closed

External Links

Social Impact

Altmetric
PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

813
OpenAlex

Cite This

Avrim Blum, Cynthia Dwork, Frank McSherry et al. (2005). Practical privacy. . https://doi.org/10.1145/1065167.1065184

Identifiers

DOI
10.1145/1065167.1065184