Faster private release of marginals on small databases

Karthekeyan Chandrasekaran, Justin Thaler, Jonathan R. Ullman, Andrew Wan

Abstract: We study the problem of answering k-way marginal queries on a database D ϵ ({0,1}d)n, while preserving differential privacy. The answer to a k-way marginal query is the fraction of the database's records x in {0,1}d with a given value in each of a given set of up to k columns. Marginal queries enable a rich class of statistical analyses on a dataset, and designing efficient algorithms for privately answering marginal queries has been identified as an important open problem in private data analysis. For any k, we give a differentially private online algorithm that runs in time poly (n, 2o(d)) per query and answers any sequence of poly(n) many k-way marginal queries with error at most ±0.01 on every query, provided n ≥ d0.51. To the best of our knowledge, this is the first algorithm capable of privately answering marginal queries with a non-trivial worst-case accuracy guarantee for databases containing poly(d, k) records in time exp(o(d)). Our algorithm runs the private multiplicative weights algorithm (Hardt and Rothblum, FOCS '10) on a new approximate polynomial representation of the database. We derive our representation for the database by approximating the OR function restricted to low Hamming weight inputs using low-degree polynomials with coefficients of bounded L1-norm. In doing so, we show new upper and lower bounds on the degree of such polynomials, which may be of independent approximation-theoretic interest.