An overview of information-theoretic security and privacy: Metrics, limits and applications
This tutorial reviews fundamental contributions to information security. An integrative
viewpoint is taken that explains the security metrics, including secrecy, privacy, and others …
viewpoint is taken that explains the security metrics, including secrecy, privacy, and others …
Context-aware generative adversarial privacy
Preserving the utility of published datasets while simultaneously providing provable privacy
guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such …
guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such …
Context-aware local information privacy
In this paper, we study Local Information Privacy (LIP). As a context-aware privacy notion,
LIP relaxes the de facto standard privacy notion of local differential privacy (LDP) by …
LIP relaxes the de facto standard privacy notion of local differential privacy (LDP) by …
Generalization Error Bounds via Rényi-, f-Divergences and Maximal Leakage
In this work, the probability of an event under some joint distribution is bounded by
measuring it with the product of the marginals instead (which is typically easier to analyze) …
measuring it with the product of the marginals instead (which is typically easier to analyze) …
Optimal utility-privacy trade-off with total variation distance as a privacy measure
B Rassouli, D Gündüz - IEEE Transactions on Information …, 2019 - ieeexplore.ieee.org
The total variation distance is proposed as a privacy measure in an information disclosure
scenario when the goal is to reveal some information about available data in return of utility …
scenario when the goal is to reveal some information about available data in return of utility …
Estimation efficiency under privacy constraints
We investigate the problem of estimating a random variable Y under a privacy constraint
dictated by another correlated random variable X. When X and Y are discrete, we express …
dictated by another correlated random variable X. When X and Y are discrete, we express …
On the robustness of information-theoretic privacy measures and mechanisms
Consider a data publishing setting for a dataset composed by both private and non-private
features. The publisher uses an empirical distribution, estimated from n iid samples, to …
features. The publisher uses an empirical distribution, estimated from n iid samples, to …
Local information privacy and its application to privacy-preserving data aggregation
In this article, we propose local information privacy (LIP), and design LIP based mechanisms
for statistical aggregation while protecting users' privacy without relying on a trusted third …
for statistical aggregation while protecting users' privacy without relying on a trusted third …
Hypothesis testing under maximal leakage privacy constraints
The problem of publishing privacy-guaranteed data for hypothesis testing is studied using
the maximal leakage (ML) as a metric for privacy and the type-II error exponent as the utility …
the maximal leakage (ML) as a metric for privacy and the type-II error exponent as the utility …
A tunable measure for information leakage
A tunable measure for information leakage called maximal a-leakage is introduced. This
measure quantifies the maximal gain of an adversary in refining a tilted version of its prior …
measure quantifies the maximal gain of an adversary in refining a tilted version of its prior …