Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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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∈Sf(DBr),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.