69 citations · 116 across the 31 of their papers we have counts for
7 papers · 1 filter
Optimal Fairness under Local Differential Privacy
Hrad Ghoukasian, Shahab Asoodeh
We investigate how to optimally design local differential privacy (LDP) mechanisms that reduce data unfairness and thereby improve fairness in downstream classification. We first d…
Locally Optimal Private Sampling: Beyond the Global Minimax
Hrad Ghoukasian, Bonwoo Lee, Shahab Asoodeh
We study the problem of sampling from a distribution under local differential privacy (LDP). Given a private distribution , the goal is to generate a single samp…
Locally Private Sampling with Public Data
Behnoosh Zamanlooy, Mario Diaz, Shahab Asoodeh
Local differential privacy (LDP) is increasingly employed in privacy-preserving machine learning to protect user data before sharing it with an untrusted aggregator. Most LDP metho…
Exactly Minimax-Optimal Locally Differentially Private Sampling
Hyun-Young Park, Shahab Asoodeh, Si-Hyeon Lee
The sampling problem under local differential privacy has recently been studied with potential applications to generative models, but a fundamental analysis of its privacy-utility…
Differentially Private Fair Binary Classifications
Hrad Ghoukasian, Shahab Asoodeh
In this work, we investigate binary classification under the constraints of both differential privacy and fairness. We first propose an algorithm based on the decoupling technique…
Privacy Loss of Noisy Stochastic Gradient Descent Might Converge Even for Non-Convex Losses
Shahab Asoodeh, Mario Diaz
The Noisy-SGD algorithm is widely used for privately training machine learning models. Traditional privacy analyses of this algorithm assume that the internal state is publicly rev…