In this paper, we establish anti-concentration inequalities for additive...
Privacy protection methods, such as differentially private mechanisms,
i...
Local differential privacy (LDP) is a differential privacy (DP) paradigm...
Differential private (DP) mechanisms protect individual-level informatio...
The test statistics for many nonparametric hypothesis tests can be expre...
A canonical noise distribution (CND) is an additive mechanism designed t...
f-DP has recently been proposed as a generalization of classical
definit...
Differential privacy (DP) offers strong theoretical privacy guarantees, ...
We propose a general method of producing synthetic data, which is widely...
This paper presents a new mechanism for producing sanitized statistical
...
We study elliptical distributions in locally convex vector spaces, and
d...
We derive uniformly most powerful (UMP) tests for simple and one-sided
h...
The exponential mechanism is a fundamental tool of Differential Privacy ...
We derive uniformly most powerful (UMP) tests for simple and one-sided
h...
A common way to protect privacy of sensitive information is to introduce...