Differential Privacy via Wavelet Transforms

TL;DR

This paper introduces a wavelet transform-based method for differentially private data publishing that significantly improves the accuracy of range-count query responses while maintaining strong privacy guarantees.

Contribution

It proposes a novel framework using wavelet transforms to enhance data utility in differential privacy, especially for range-count queries, with theoretical and experimental validation.

Findings

Effective privacy-utility trade-off demonstrated

Significant accuracy improvements over existing methods

Efficient for both ordinal and nominal data

Abstract

Privacy preserving data publishing has attracted considerable research interest in recent years. Among the existing solutions, {\em $\epsilon$-differential privacy} provides one of the strongest privacy guarantees. Existing data publishing methods that achieve $\epsilon$-differential privacy, however, offer little data utility. In particular, if the output dataset is used to answer count queries, the noise in the query answers can be proportional to the number of tuples in the data, which renders the results useless. In this paper, we develop a data publishing technique that ensures $\epsilon$-differential privacy while providing accurate answers for {\em range-count queries}, i.e., count queries where the predicate on each attribute is a range. The core of our solution is a framework that applies {\em wavelet transforms} on the data before adding noise to it. We present instantiations of the proposed framework for both ordinal and nominal data, and we provide a theoretical analysis on their privacy and utility guarantees. In an extensive experimental study on both real and synthetic data, we show the effectiveness and efficiency of our solution.