Abstract-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, C1 and C2, and the possibility of factoring C1 into C2C3, for some C3. Based on this connection, we propose a generalization of the Lattice of Information from deterministic to probabilistic channels.
Alvim et al. REF introduce g-leakage, a generalization of min-entropy leakage that uses gain functions to model other operational scenarios, including those where the adversary gains by guessing the secret only approximately or partially.
Measuring Information Leakage Using Generalized Gain Functions
2012 IEEE 25th Computer Security Foundations Symposium
2012 IEEE 25th Computer Security Foundations Symposium
Computer Science