# Profile-Based Privacy for Locally Private Computations

## TL;DR

This paper introduces profile-based privacy, a new local privacy model that improves utility by protecting distribution identities rather than individual data points, supported by mechanisms and simulations.

## Contribution

It proposes a novel profile-based privacy framework that enhances utility over local differential privacy by focusing on distribution indistinguishability.

## Key findings

- Profile-based privacy offers better privacy-utility tradeoff.
- Mechanisms in this framework outperform local differential privacy in simulations.
- The framework maintains privacy properties like post-processing invariance.

## Abstract

Differential privacy has emerged as a gold standard in privacy-preserving data analysis. A popular variant is local differential privacy, where the data holder is the trusted curator. A major barrier, however, towards a wider adoption of this model is that it offers a poor privacy-utility tradeoff. In this work, we address this problem by introducing a new variant of local privacy called profile-based privacy. The central idea is that the problem setting comes with a graph G of data generating distributions, whose edges encode sensitive pairs of distributions that should be made indistinguishable. This provides higher utility because unlike local differential privacy, we no longer need to make every pair of private values in the domain indistinguishable, and instead only protect the identity of the underlying distribution. We establish privacy properties of the profile-based privacy definition, such as post-processing invariance and graceful composition. Finally, we provide mechanisms that are private in this framework, and show via simulations that they achieve higher utility than the corresponding local differential privacy mechanisms.