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…
Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation
Berivan Isik, Wei-Ning Chen, Ayfer Ozgur +2
We study the mean estimation problem under communication and local differential privacy constraints. While previous work has proposed \emph{order}-optimal algorithms for the same p…
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation
Wei-Ning Chen, Dan Song, Ayfer Ozgur +1
Privacy and communication constraints are two major bottlenecks in federated learning (FL) and analytics (FA). We study the optimal accuracy of mean and frequency estimation (canon…
The communication cost of security and privacy in federated frequency estimation
Wei-Ning Chen, Ayfer Özgür, Graham Cormode +1
We consider the federated frequency estimation problem, where each user holds a private item from a size- domain and a server aims to estimate the empirical frequency (i.e…
The Poisson binomial mechanism for secure and private federated learning
Wei-Ning Chen, Ayfer Özgür, Peter Kairouz
We introduce the Poisson Binomial mechanism (PBM), a discrete differential privacy mechanism for distributed mean estimation (DME) with applications to federated learning and analy…
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
Wei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz +1
We consider the problem of training a dimensional model with distributed differential privacy (DP) where secure aggregation (SecAgg) is used to ensure that the server only sees…
Optimal Compression of Locally Differentially Private Mechanisms
Abhin Shah, Wei-Ning Chen, Johannes Balle +2
Compressing the output of ε-locally differentially private (LDP) randomizers naively leads to suboptimal utility. In this work, we demonstrate the benefits of using schemes that jo…
Pointwise Bounds for Distribution Estimation under Communication Constraints
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
We consider the problem of estimating a -dimensional discrete distribution from its samples observed under a -bit communication constraint. In contrast to most previous resul…
Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
We consider the problem of estimating a -dimensional -sparse discrete distribution from its samples observed under a -bit communication constraint. The best-known previous…
Adaptive Group Testing on Networks with Community Structure: The Stochastic Block Model
Surin Ahn, Wei-Ning Chen, Ayfer Ozgur
Group testing was conceived during World War II to identify soldiers infected with syphilis using as few tests as possible, and it has attracted renewed interest during the COVID-1…
Breaking the Communication-Privacy-Accuracy Trilemma
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
Two major challenges in distributed learning and estimation are 1) preserving the privacy of the local samples; and 2) communicating them efficiently to a central server, while ach…
Fisher information under local differential privacy
Leighton Pate Barnes, Wei-Ning Chen, Ayfer Ozgur
We develop data processing inequalities that describe how Fisher information from statistical samples can scale with the privacy parameter under local differential pr…
Anonymous Heterogeneous Distributed Detection: Optimal Decision Rules, Error Exponents, and the Price of Anonymity
Wei-Ning Chen, I-Hsiang Wang
We explore the fundamental limits of heterogeneous distributed detection in an anonymous sensor network with n sensors and a single fusion center. The fusion center collects the si…