Privacy Against Brute-Force Inference Attacks

2019/02/01 by Seyed Ali Osia, Borzoo Rassouli, Osia, Seyed Ali +7
Computer Science · Mathematics · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information Theory (cs.IT) #cs.CR #cs.IT #math.IT

paper · pdf · doi:10.48550/arxiv.1902.00329

arxiv created 2019/02/01 · arxiv updated 2019/02/04

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.

Related

• Privacy Against Statistical Inference
• Hypothesis Testing under Maximal Leakage Privacy Constraints
• Measuring Information Leakage in Non-stochastic Brute-Force Guessing
• Bounds for Privacy-Utility Trade-off with Non-zero Leakage
• Privacy with Estimation Guarantees