# Faster Private Release of Marginals on Small Databases

### Citation:

K. Chandrasekaran, Thaler, J., Ullman, J., and Wan, A., “Faster Private Release of Marginals on Small Databases,” CoRR, , vol. abs/1304.3754, 2013.
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### Abstract:

We study the problem of answering \emph{$k$-way marginal} queries on a database $D \in (\{0,1\}^d)^n$, 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 $$\min{\exp(d^{1-\Omega(1/\sqrt{k})}), \exp(d / \log^{.99} d)\}$$ per query and answers any (possibly superpolynomially long and adaptively chosen) sequence of $k$-way marginal queries up to error at most $\pm .01$ on every query, provided $n \gtrsim d^{.51}$. 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 on a database of size $\poly(d, k)$ in time $\exp(o(d))$. Our algorithms are a variant of the private multiplicative weights algorithm (Hardt and Rothblum, FOCS '10), but using a different low-weight representation of the database. We derive our low-weight representation using approximations to the OR function by low-degree polynomials with coefficients of bounded $L_1$-norm. We also prove a strong limitation on our approach that is of independent approximation-theoretic interest. Specifically, we show that for any $k = o(\log d)$, any polynomial with coefficients of $L_1$-norm $poly(d)$ that pointwise approximates the $d$-variate OR function on all inputs of Hamming weight at most $k$ must have degree $d^{1-O(1/\sqrt{k})}$.

### Notes:

Chandrasekaran is supported by Simons Fellowship. Thaler is supported by an NSF Graduate Research Fellowship and NSF grants CNS-1011840 and CCF-0915922. Ullman is supported by NSF grant CNS-1237235 and a Siebel Scholarship. Wan is supported by NSF grant CCF-0964401 and NSFC grant 61250110218.

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