Pufferfish Privacy Mechanisms for Correlated Data

TL;DR

This paper introduces the Wasserstein Mechanism, a novel approach for ensuring privacy in correlated data using Pufferfish, addressing limitations of differential privacy, with practical and efficient solutions demonstrated on real datasets.

Contribution

It presents the first Wasserstein Mechanism for Pufferfish privacy, extending privacy guarantees to correlated data and providing practical, computationally efficient mechanisms.

Findings

The Wasserstein Mechanism effectively balances privacy and utility.

The practical mechanism performs well on real physical activity data.

Experimental results show strong privacy guarantees with minimal utility loss.

Abstract

Many modern databases include personal and sensitive correlated data, such as private information on users connected together in a social network, and measurements of physical activity of single subjects across time. However, differential privacy, the current gold standard in data privacy, does not adequately address privacy issues in this kind of data. This work looks at a recent generalization of differential privacy, called Pufferfish, that can be used to address privacy in correlated data. The main challenge in applying Pufferfish is a lack of suitable mechanisms. We provide the first mechanism -- the Wasserstein Mechanism -- which applies to any general Pufferfish framework. Since this mechanism may be computationally inefficient, we provide an additional mechanism that applies to some practical cases such as physical activity measurements across time, and is computationally efficient. Our experimental evaluations indicate that this mechanism provides privacy and utility for synthetic as well as real data in two separate domains.