It is crucial to protect users' location traces against inference attacks on aggregate mobility
data collected from multiple users in various real-world applications. Most of the existing …

We introduce a privacy measure called pointwise maximal leakage, generalizing the pre-
existing notion of maximal leakage, which quantifies the amount of information leaking about …

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 …

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 …

In this paper, we investigate the privacy-utility trade-off (PUT) problem, which considers the
minimal privacy loss at a fixed expense of utility. Several different kinds of privacy in the PUT …

We introduce a family of information leakage measures called maximal-leakage (beL),
parameterized by real numbers and greater than or equal to 1. The measure is formalized …

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 …

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 …