Privacy Against Brute-Force Inference Attacks
This work addresses privacy concerns in data release for scenarios where sensitive information is vulnerable to brute-force attacks, representing an incremental improvement in privacy metrics and optimization methods.
The paper tackles the problem of protecting sensitive data from brute-force inference attacks by introducing Guessing Leakage as a privacy measure, and derives an optimal utility-privacy trade-off using a linear program, showing that optimal utility is concave and piece-wise linear with respect to the privacy-leakage budget.
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.