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 …

Protecting sensitive information from improper disclosure is a fundamental security goal. It is
complicated, and difficult to achieve, often because of unavoidable or even unpredictable …

Local differential privacy (LPD) is a distributed variant of differential privacy (DP) in which the
obfuscation of the sensitive information is done at the level of the individual records, and in …

In computer security, it is frequently necessary in practice to accept some leakage of
confidential information. This motivates the development of theories of Quantitative …

Opacity is a general language-theoretic framework in which several security properties of a
system can be expressed. Its parameters are a predicate, given as a subset of runs of the …

The observable output of a probabilistic system that processes a secret input might reveal
some information about that input. The system can be modelled as an information-theoretic …

Differential privacy is a notion that has emerged in the community of statistical databases, as
a response to the problem of protecting the privacy of the database's participants when …

Quantitative information flow aims to assess and control the leakage of sensitive information
by computer systems. A key insight in this area is that no single leakage measure is …

Local differential privacy (LDP) is a variant of differential privacy (DP) that avoids the
necessity of a trusted central curator, at the expense of a worse trade-off between privacy …

We consider the problem of obfuscating sensitive information while preserving utility, and we
propose a machine learning approach inspired by the generative adversarial networks …