# Privacy Against Brute-Force Inference Attacks

## TL;DR

This paper introduces Guessing Leakage, a new privacy measure for data release that models adversaries using brute-force guessing, and derives optimal utility-privacy trade-offs using linear programming.

## Contribution

It defines Guessing Leakage as a novel privacy measure and provides a method to compute optimal utility-privacy trade-offs for data release.

## Key findings

- Optimal utility is a concave, piece-wise linear function of privacy budget.
- Derived the utility-privacy trade-off using linear programming.
- Validated properties of Guessing Leakage as a privacy measure.

## Abstract

Privacy-preserving data release is about disclosing information about useful data while retaining the privacy of sensitive data. Assuming that the sensitive data is threatened by a brute-force adversary, we define Guessing Leakage as a measure of privacy, based on the concept of guessing. After investigating the properties of this measure, we derive the optimal utility-privacy trade-off via a linear program with any $f$-information adopted as the utility measure, and show that the optimal utility is a concave and piece-wise linear function of the privacy-leakage budget.