4 papers
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies
Zhanyu Wang, Guang Cheng, Jordan Awan
Differentially private (DP) mechanisms protect individual-level information by introducing randomness into the statistical analysis procedure. Despite the availability of numerous…
Optimizing Noise for -Differential Privacy via Anti-Concentration and Stochastic Dominance
Jordan Awan, Aishwarya Ramasethu
In this paper, we establish anti-concentration inequalities for additive noise mechanisms which achieve -differential privacy (-DP), a notion of privacy phrased in terms of a…
Simulation-based, Finite-sample Inference for Privatized Data
Jordan Awan, Zhanyu Wang
Privacy protection methods, such as differentially private mechanisms, introduce noise into resulting statistics which often produces complex and intractable sampling distributions…
Structure and Sensitivity in Differential Privacy: Comparing K-Norm Mechanisms
Jordan Awan, Aleksandra Slavkovic
Differential privacy (DP), provides a framework for provable privacy protection against arbitrary adversaries, while allowing the release of summary statistics and synthetic data.…