# Privacy Against Brute-Force Inference Attacks

arXiv: https://arxiv.org/abs/1902.00329 · HTML: https://mlsanity.com/paper/1902.00329/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

## Built on by (most cited first, 2 of 2)

- [Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary](https://mlsanity.com/paper/2102.08308/) (2021)
- [Active Privacy-Utility Trade-off Against Inference in Time-Series Data Sharing](https://mlsanity.com/paper/2202.05833/) (2022)

## Abstract (arXiv)

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.