Distribution-Invariant Differential Privacy

TL;DR

This paper introduces a distribution-invariant privatization method that maintains statistical accuracy while providing strict differential privacy, effectively balancing privacy protection and data utility across various applications.

Contribution

The paper proposes a novel distribution-invariant privatization (DIP) technique that preserves data distribution, improving accuracy without compromising privacy in differential privacy applications.

Findings

DIP achieves higher statistical accuracy than existing methods.

DIP maintains data utility across diverse real-world benchmarks.

The method effectively balances privacy and accuracy in various scenarios.

Abstract

Differential privacy is becoming one gold standard for protecting the privacy of publicly shared data. It has been widely used in social science, data science, public health, information technology, and the U.S. decennial census. Nevertheless, to guarantee differential privacy, existing methods may unavoidably alter the conclusion of the original data analysis, as privatization often changes the sample distribution. This phenomenon is known as the trade-off between privacy protection and statistical accuracy. In this work, we mitigate this trade-off by developing a distribution-invariant privatization (DIP) method to reconcile both high statistical accuracy and strict differential privacy. As a result, any downstream statistical or machine learning task yields essentially the same conclusion as if one used the original data. Numerically, under the same strictness of privacy protection, DIP achieves superior statistical accuracy in a wide range of simulation studies and real-world benchmarks.