Differential privacy (DP) is by far the most widely accepted framework for mitigating privacy
risks in machine learning. However, exactly how small the privacy parameter $\epsilon …

A rate-distortion problem motivated by the consideration of semantic information is
formulated and solved. The starting point is to model an information source as a pair …

This tutorial paper focuses on the variants of the bottleneck problem taking an information
theoretic perspective and discusses practical methods to solve it, as well as its connection to …

A new source model, which consists of an intrinsic state part and an extrinsic observation
part, is proposed and its information-theoretic characterization, namely its rate-distortion …

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 …

We propose a data-driven framework for optimizing privacy-preserving data release
mechanisms to attain the information-theoretically optimal tradeoff between minimizing …

Sequential rate-distortion (SRD) theory provides a framework for studying the fundamental
trade-off between data-rate and data-quality in real-time communication systems. In this …

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 …

Given two discrete random variables X and Y, an operational approach is undertaken to
quantify the “leakage” of information from X to Y. The resulting measure ℒ (X→ Y) is called …

Deep learning driven joint source-channel coding (JSCC) for wireless image or video
transmission, also called DeepJSCC, has been a topic of interest recently with very …