Rethinking Disclosure Prevention with Pointwise Maximal Leakage

TL;DR

This paper introduces pointwise maximal leakage as a new privacy measure that allows meaningful inferential privacy guarantees by focusing on high-entropy features, enabling more flexible and utility-preserving privacy mechanisms.

Contribution

It proposes a novel privacy framework based on pointwise maximal leakage, challenging the impossibility of absolute disclosure prevention and linking to existing privacy notions like differential privacy.

Findings

PML provides strong privacy guarantees against various adversaries.

The framework allows mechanism design that balances utility and privacy based on data entropy.

PML offers insights into and compatibility with differential privacy.

Abstract

This paper introduces a paradigm shift in the way privacy is defined, driven by a novel interpretation of the fundamental result of Dwork and Naor about the impossibility of absolute disclosure prevention. We propose a general model of utility and privacy in which utility is achieved by disclosing the value of low-entropy features of a secret $X$, while privacy is maintained by hiding the value of high-entropy features of $X$. Adopting this model, we prove that, contrary to popular opinion, it is possible to provide meaningful inferential privacy guarantees. These guarantees are given in terms of an operationally-meaningful information measure called pointwise maximal leakage (PML) and prevent privacy breaches against a large class of adversaries regardless of their prior beliefs about $X$. We show that PML-based privacy is compatible with and provides insights into existing notions such as differential privacy. We also argue that our new framework enables highly flexible mechanism designs, where the randomness of a mechanism can be adjusted to the entropy of the data, ultimately, leading to higher utility.