Algorithms with More Granular Differential Privacy Guarantees

TL;DR

This paper introduces a framework for partial differential privacy that assigns privacy guarantees to individual attributes, enabling more granular privacy control in data analysis and learning tasks.

Contribution

It proposes algorithms with per-attribute privacy guarantees that are tighter than the overall privacy bounds, enhancing privacy customization.

Findings

Per-attribute privacy guarantees are achievable in data analysis tasks.

Algorithms with finer privacy control outperform traditional methods in privacy-utility trade-offs.

Framework supports more personalized privacy settings for multi-attribute data.

Abstract

Differential privacy is often applied with a privacy parameter that is larger than the theory suggests is ideal; various informal justifications for tolerating large privacy parameters have been proposed. In this work, we consider partial differential privacy (DP), which allows quantifying the privacy guarantee on a per-attribute basis. In this framework, we study several basic data analysis and learning tasks, and design algorithms whose per-attribute privacy parameter is smaller that the best possible privacy parameter for the entire record of a person (i.e., all the attributes).