Data publishing under privacy constraints can be achieved with mechanisms that add
randomness to data points when released to an untrusted party, thereby decreasing the …

We investigate the in-distribution generalization of machine learning algorithms. We depart
from traditional complexity-based approaches by analyzing information-theoretic bounds …

This paper explores the implications of guaranteeing privacy by imposing a lower bound on
the information density between the private and the public data. We introduce an …

We examine the relationship between privacy metrics that utilize information density to
measure information leakage between a private and a disclosed random variable. Firstly, we …

This paper introduces a paradigm shift in the way privacy is defined, driven by a novel
interpretation of the fundamental result of Dwork and Naor about the impossibility of …

In this paper, we extend the framework of quantitative information flow (QIF) to include
adversaries that use Kolmogorov-Nagumo $ f $-mean to infer secrets of a private system …

Data-driven advancements significantly contribute to societal progress, yet they also pose
substantial risks to privacy. In this landscape, differential privacy (DP) has become a …

This paper investigates the privacy funnel, a privacy-utility tradeoff problem in which mutual
information quantifies both privacy and utility. The objective is to maximize utility while …

For several decades now, safeguarding sensitive information from disclosure has been a
key focus in computer science and information theory. Especially, in the past two decades …