@article {DBLP:journals/corr/abs-1304-3754,
	title = {Faster Private Release of Marginals on Small Databases},
	journal = {CoRR},
	volume = {abs/1304.3754},
	year = {2013},
	note = {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.},
	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{\textquoteright}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 {\textquoteright}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 $d^{1-O(1/\sqrt{k})}$.},
	url = {http://arxiv.org/abs/1304.3754},
	author = {Karthekeyan Chandrasekaran and Justin Thaler and Jonathan Ullman and Andrew Wan}
}
