Guarding Multiple Secrets: Enhanced Summary Statistic Privacy for Data Sharing

TL;DR

This paper introduces a new framework for protecting multiple sensitive summary statistics in data sharing, addressing privacy risks and designing mechanisms to balance privacy with data utility.

Contribution

It develops a comprehensive privacy framework for multiple secrets, including tailored metrics and mechanisms, extending beyond single-secret protections in prior work.

Findings

Effective privacy-preserving mechanisms for multiple secrets

Tradeoff analysis between privacy and data distortion

Successful application on real-world datasets

Abstract

Data sharing enables critical advances in many research areas and business applications, but it may lead to inadvertent disclosure of sensitive summary statistics (e.g., means or quantiles). Existing literature only focuses on protecting a single confidential quantity, while in practice, data sharing involves multiple sensitive statistics. We propose a novel framework to define, analyze, and protect multi-secret summary statistics privacy in data sharing. Specifically, we measure the privacy risk of any data release mechanism by the worst-case probability of an attacker successfully inferring summary statistic secrets. Given an attacker's objective spanning from inferring a subset to the entirety of summary statistic secrets, we systematically design and analyze tailored privacy metrics. Defining the distortion as the worst-case distance between the original and released data distribution, we analyze the tradeoff between privacy and distortion. Our contribution also includes designing and analyzing data release mechanisms tailored for different data distributions and secret types. Evaluations on real-world data demonstrate the effectiveness of our mechanisms in practical applications.