Bounding privacy leakage over compositions, ie, privacy accounting, is a key challenge in
differential privacy (DP). However, the privacy parameter ($\varepsilon $ or $\delta $) is often …

Characterizing the privacy degradation over compositions, ie, privacy accounting, is a
fundamental topic in differential privacy (DP) with many applications to differentially private …

Differential privacy has seen remarkable success as a rigorous and practical formalization of
data privacy in the past decade. This privacy definition and its divergence based relaxations …

We summarize the experience of participating in two differential privacy competitions
organized by the National Institute of Standards and Technology (NIST). In this paper, we …

In this paper, we study the problem of privacy-preserving data sharing, wherein only a
subset of the records in a database are sensitive, possibly based on predefined privacy …

Gaussian differential privacy (GDP) is a single-parameter family of privacy notions that
provides coherent guarantees to avoid the exposure of sensitive individual information …

Composition is a key feature of differential privacy. Well-known advanced composition
theorems allow one to query a private database quadratically more times than basic privacy …

There are numerous methods of achieving $\epsilon $-differential privacy (DP). The
question is what is the appropriate value of $\epsilon $, since there is no common …

We study the problem of privacy-preserving data sharing, wherein only a subset of the
records in a database is sensitive, possibly based on predefined privacy policies. Existing …

When analyzing data that has been perturbed for privacy reasons, one is often concerned
about its usefulness. Recent research on differential privacy has shown that the accuracy of …