Measuring Information Leakage Using Generalized Gain Functions

This paper introduces g-leakage, a rich generalization of the min-entropy model of quantitative information flow. In g-leakage, the benefit that an adversary derives from a certain guess about a secret is specified using a gain function g. Gain functions allow a wide variety of operational scenarios...

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Bibliographic Details
Main Authors: Alvim, M. S., Chatzikokolakis, K., Palamidessi, C., Smith, G.
Format: Conference Proceeding
Language:English
Subjects:
Computer security
Entropy
Gain measurement
Lattices
Probabilistic logic
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Summary:This paper introduces g-leakage, a rich generalization of the min-entropy model of quantitative information flow. In g-leakage, the benefit that an adversary derives from a certain guess about a secret is specified using a gain function g. Gain functions allow a wide variety of operational scenarios to be modeled, including those where the adversary benefits from guessing a value close to the secret, guessing a part of the secret, guessing a property of the secret, or guessing the secret within some number of tries. We prove important properties of g-leakage, including bounds between min-capacity, g-capacity, and Shannon capacity. We also show a deep connection between a strong leakage ordering on two channels, C 1 and C 2 , and the possibility of factoring C 1 into C 2 C 3 , for some C 3 . Based on this connection, we propose a generalization of the Lattice of Information from deterministic to probabilistic channels.
ISSN:1063-6900
2377-5459
DOI:10.1109/CSF.2012.26