# Profile-Based Privacy for Locally Private Computations

arXiv: https://arxiv.org/abs/1903.09084 · HTML: https://mlsanity.com/paper/1903.09084/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2019.

## Built on by (most cited first, 5 of 5)

- [A Shuffling Framework for Local Differential Privacy](https://mlsanity.com/paper/2106.06603/) (2021)
- [Rainbow Differential Privacy](https://mlsanity.com/paper/2202.03974/) (2022)
- [Privacy Blur: Quantifying Privacy and Utility for Image Data Release](https://mlsanity.com/paper/2512.16086/) (2025)
- [Classification Under Local Differential Privacy with Model Reversal and Model Averaging](https://mlsanity.com/paper/2602.05797/) (2026)
- [Integrating Feature Correlation in Differential Privacy with Applications in DP-ERM](https://mlsanity.com/paper/2605.03945/) (2026)

## Abstract (arXiv)

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.