32 citations · 65 across the 17 of their papers we have counts for
18 papers
Leveraging Randomness in Model and Data Partitioning for Privacy Amplification
Andy Dong, Wei-Ning Chen, Ayfer Ozgur
We study how inherent randomness in the training process -- where each sample (or client in federated learning) contributes only to a randomly selected portion of training -- can b…
Universal Exact Compression of Differentially Private Mechanisms
Yanxiao Liu, Wei-Ning Chen, Ayfer Özgür +1
To reduce the communication cost of differential privacy mechanisms, we introduce a novel construction, called Poisson private representation (PPR), designed to compress and simula…
Lower Bounds on Distributed Estimation via Fisher Information
Wei-Ning Chen, Ayfer Özgür
Van Trees inequality, also known as the Bayesian Cramér-Rao lower bound, is a powerful tool for establishing lower bounds for minimax estimation through Fisher information. It easi…
Federated Experiment Design under Distributed Differential Privacy
Wei-Ning Chen, Graham Cormode, Akash Bharadwaj +2
Experiment design has a rich history dating back over a century and has found many critical applications across various fields since then. The use and collection of users' data in…
Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection
Eli Chien, Wei-Ning Chen, Chao Pan +3
GNNs can inadvertently expose sensitive user information and interactions through their model predictions. To address these privacy concerns, Differential Privacy (DP) protocols ar…
Training generative models from privatized data
Daria Reshetova, Wei-Ning Chen, Ayfer Özgür
Local differential privacy is a powerful method for privacy-preserving data collection. In this paper, we develop a framework for training Generative Adversarial Networks (GANs) on…