This tutorial reviews fundamental contributions to information security. An integrative
viewpoint is taken that explains the security metrics, including secrecy, privacy, and others …

In a federated learning scenario where multiple parties jointly learn a model from their
respective data, there exist two conflicting goals for the choice of appropriate algorithms. On …

Given two 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 maximal …

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 …

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

Privacy-preserving instance encoding aims to encode raw data into feature vectors without
revealing their privacy-sensitive information. When designed properly, these encodings can …

The Internet of Vehicles (IoV) has emerged as a robust solution to improve real-time road
traffic conditions, safety, and driving comfort. However, the road's dynamic environment and …

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 …

We study a statistical signal processing privacy problem, where an agent observes useful
data and wants to reveal the information to a user. Since the useful data is correlated with …