Ensuring the usefulness of electronic data sources while providing necessary privacy
guarantees is an important unsolved problem. This problem drives the need for an analytical …

Differential privacy offers a way to answer queries about sensitive information while
providing strong, provable privacy guarantees, ensuring that the presence or absence of a …

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 …

In this paper, we design user-centric obfuscation mechanisms that impose the minimum
utility loss for guaranteeing user's privacy. We optimize utility subject to a joint guarantee of …

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 …

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 …

We present formal models of the associative and causal views of differential privacy. Under
the associative view, the possibility of dependencies between data points precludes a …

Abstract f-divergences form a class of measures of distance between probability
distributions; they are widely used in areas such as information theory and signal …

Industries and governments are increasingly compelled by regulations and public pressure
to handle sensitive information responsibly. Regulatory requirements and user expectations …

In this paper, we consider the scenario in which the profile of a user is represented in a
compact way, as a Bloom filter, and the main objective is to privately compute in a distributed …