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 …

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 …