Orthogonal mechanism for answering batch queries with differential privacy

Abstract

Differential privacy has recently become very promising in achieving data privacy guarantee. Typically, one can achieve ε-differential privacy by adding noise based on Laplace distribution to a query result. To reduce the noise magnitude for higher accuracy, various techniques have been proposed. They generally require high computational complexity, making them inapplicable to large-scale datasets. In this paper, we propose a novel orthogonal mechanism (OM) to represent a query set Q with a linear combination of a new query set Q, where Q consists of orthogonal query sets and is derived by exploiting the correlations between queries in Q. As a result of orthogonality of the derived queries, the proposed technique not only greatly reduces computational complexity, but also achieves better accuracy than the existing mechanisms. Extensive experimental results demonstrate the effectiveness and efficiency of the proposed technique.