Fast Private Data Release Algorithms for Sparse Queries

TL;DR

This paper introduces efficient differential privacy algorithms for answering large classes of sparse statistical queries, achieving improved accuracy and runtime independence from universe size, suitable for infinite universe models.

Contribution

The paper presents novel algorithms for private data release tailored to sparse queries, with runtime and accuracy advantages over prior methods.

Findings

Algorithms achieve universe-size-independent accuracy

Runtime of interactive mechanism is independent of universe size

Applicable to infinite universe models

Abstract

We revisit the problem of accurately answering large classes of statistical queries while preserving differential privacy. Previous approaches to this problem have either been very general but have not had run-time polynomial in the size of the database, have applied only to very limited classes of queries, or have relaxed the notion of worst-case error guarantees. In this paper we consider the large class of sparse queries, which take non-zero values on only polynomially many universe elements. We give efficient query release algorithms for this class, in both the interactive and the non-interactive setting. Our algorithms also achieve better accuracy bounds than previous general techniques do when applied to sparse queries: our bounds are independent of the universe size. In fact, even the runtime of our interactive mechanism is independent of the universe size, and so can be implemented in the "infinite universe" model in which no finite universe need be specified by the data curator.