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.
Privacy Against Brute-Force Inference Attacks / Seyed Ali Osia, ; Borzoo, Rassouli; Hamed, Haddadi; Rabiee, Hamid R.; Gunduz, Deniz. - 2019-:(2019), pp. 637-641. (Intervento presentato al convegno 2019 IEEE International Symposium on Information Theory, ISIT 2019 tenutosi a La Maison de La Mutualite, fra nel 2019) [10.1109/ISIT.2019.8849291].
Privacy Against Brute-Force Inference Attacks
Deniz Gündüz
2019
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.Pubblicazioni consigliate
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