Measuring information leakage using generalized gain functions
SA M'rio, K Chatzikokolakis… - 2012 IEEE 25th …, 2012 - ieeexplore.ieee.org
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 …
quantitative information flow. In g-leakage, the benefit that an adversary derives from a …
Additive and multiplicative notions of leakage, and their capacities
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 …
complicated, and difficult to achieve, often because of unavoidable or even unpredictable …
Local differential privacy on metric spaces: optimizing the trade-off with utility
M Alvim, K Chatzikokolakis… - 2018 IEEE 31st …, 2018 - ieeexplore.ieee.org
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 …
obfuscation of the sensitive information is done at the level of the individual records, and in …
Recent developments in quantitative information flow (invited tutorial)
G Smith - 2015 30th Annual ACM/IEEE Symposium on Logic in …, 2015 - ieeexplore.ieee.org
In computer security, it is frequently necessary in practice to accept some leakage of
confidential information. This motivates the development of theories of Quantitative …
confidential information. This motivates the development of theories of Quantitative …
Quantifying opacity
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 …
system can be expressed. Its parameters are a predicate, given as a subset of runs of the …
Abstract channels and their robust information-leakage ordering
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 …
some information about that input. The system can be modelled as an information-theoretic …
On the relation between differential privacy and quantitative information flow
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 …
a response to the problem of protecting the privacy of the database's participants when …
Axioms for information leakage
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 …
by computer systems. A key insight in this area is that no single leakage measure is …
Analyzing the shuffle model through the lens of quantitative information flow
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 …
necessity of a trusted central curator, at the expense of a worse trade-off between privacy …
Optimal obfuscation mechanisms via machine learning
M Romanelli, K Chatzikokolakis… - arXiv preprint arXiv …, 2019 - arxiv.org
We consider the problem of obfuscating sensitive information while preserving utility, and we
propose a machine learning approach inspired by the generative adversarial networks …
propose a machine learning approach inspired by the generative adversarial networks …