Jordan Awan | DeepAI

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∙ 08/16/2023

Optimizing Noise for f-Differential Privacy via Anti-Concentration and Stochastic Dominance

In this paper, we establish anti-concentration inequalities for additive...

0 Jordan Awan, et al. ∙

∙ 03/09/2023

Simulation-based, Finite-sample Inference for Privatized Data

Privacy protection methods, such as differentially private mechanisms, i...

0 Jordan Awan, et al. ∙

∙ 01/04/2023

Locally Private Causal Inference

Local differential privacy (LDP) is a differential privacy (DP) paradigm...

0 Yuki Ohnishi, et al. ∙

∙ 10/12/2022

Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies

Differential private (DP) mechanisms protect individual-level informatio...

0 Zhanyu Wang, et al. ∙

∙ 08/12/2022

Differentially Private Kolmogorov-Smirnov-Type Tests

The test statistics for many nonparametric hypothesis tests can be expre...

0 Jordan Awan, et al. ∙

∙ 06/09/2022

Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy

A canonical noise distribution (CND) is an additive mechanism designed t...

0 Jordan Awan, et al. ∙

∙ 08/09/2021

Canonical Noise Distributions and Private Hypothesis Tests

f-DP has recently been proposed as a generalization of classical definit...

0 Jordan Awan, et al. ∙

∙ 08/02/2021

Privacy-Aware Rejection Sampling

Differential privacy (DP) offers strong theoretical privacy guarantees, ...

0 Jordan Awan, et al. ∙

∙ 06/03/2020

One Step to Efficient Synthetic Data

We propose a general method of producing synthetic data, which is widely...

0 Jordan Awan, et al. ∙

∙ 05/23/2019

KNG: The K-Norm Gradient Mechanism

This paper presents a new mechanism for producing sanitized statistical ...

0 Matthew Reimherr, et al. ∙

∙ 05/23/2019

Elliptical Perturbations for Differential Privacy

We study elliptical distributions in locally convex vector spaces, and d...

0 Matthew Reimherr, et al. ∙

∙ 03/31/2019

Differentially Private Inference for Binomial Data

We derive uniformly most powerful (UMP) tests for simple and one-sided h...

0 Jordan Awan, et al. ∙

∙ 01/30/2019

Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA

The exponential mechanism is a fundamental tool of Differential Privacy ...

0 Jordan Awan, et al. ∙

∙ 05/23/2018

Differentially Private Uniformly Most Powerful Tests for Binomial Data

We derive uniformly most powerful (UMP) tests for simple and one-sided h...

0 Jordan Awan, et al. ∙

∙ 01/28/2018

Structure and Sensitivity in Differential Privacy: Comparing K-Norm Mechanisms

A common way to protect privacy of sensitive information is to introduce...

0 Jordan Awan, et al. ∙

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