Publications

Please note that this page is no longer regularly updated and the information here may be outdated. For the most recent research updates, please refer to my Google Scholar profile.

Preprints

• “ \(L_q\) Lower Bounds on Distributed Estimation via Fisher Information ,” submitted,
  Wei-Ning Chen and Ayfer Özgür

• “Training generative models from privatized data,” submitted,
  Daria Reshatova Wei-Ning Chen, and Ayfer Özgür

Journal Articles

• “Adaptive Group Testing on Networks with Community Structure,” IEEE Transactions on Information Theory, Feb. 2023,
  Surin Ahn, Wei-Ning Chen and Ayfer Özgür

• “Breaking the Communication-Privacy-Accuracy Trilemma,” IEEE Transactions on Information Theory, Feb. 2023,
  Wei-Ning Chen, Peter Kairouz and Ayfer Özgür

• “Fisher information under local differential privacy,” IEEE Journal on Special Areas in Information Theory, Nov. 2020,
  Leighton Pate Barnes, Wei-Ning Chen and Ayfer Özgür

• “Anonymous Heterogeneous Distributed Detection: Optimal Decision Rules, Error Exponents, and the Price of Anonymity,” IEEE Transactions on Information Theory, Nov. 2019,
  Wei-Ning Chen and I-Hsiang Wang

Conference Proceedings

• “Federated Experiment Design under Distributed Differential Privacy,” to appear at AISTATS 2023 (early version appears at TPDP 2023),
  Wei-Ning Chen, Graham Cormode, Akash Baharadwaj, Ayfer Özgür, and Peter Romov

• “Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation,” NeurIPS 2023 (early version appears at the ICML-FL workshop),
  Wei-Ning Chen, Dan Song, Ayfer Özgür, and Peter Kairouz

• “Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection,” NeurIPS 2023,
  I (Eli) Chien, Wei-Ning Chen, Chao Pan, Pan Li, Ayfer Özgür, Olgica Milenkovic

• “Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation,” NeurIPS 2023 (early version appears at the ICML-FL workshop),
  Berivan Isik, Wei-Ning Chen, Ayfer Ozgur, Tsachy Weissman, and Albert No

• “Noisy Adaptive Group Testing for Community-Oriented Models,” ISIT 2023,
  Surin Ahn, Wei-Ning Chen and Ayfer Özgür

• “The Communication Cost of Security and Privacy in Federated Frequency Estimation,” AISTATS 2023,
  Wei-Ning Chen, Ayfer Özgür, Graham Cormode, and Akash Bharadwaj

• “The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation,” ICML 2022 (oral presentation),
  Wei-Ning Chen, Ayfer Özgür, and Peter Kairouz

• “The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning,” ICML 2022 (short version appeared at PriML Workshop on NeurIPS 2021),
  Wei-Ning Chen^*, Christopher A. Choquette-Choo, Peter Kairouz, and Ananda Theertha Suresh

• “Estimating Sparse Distributions Under Joint Communication and Privacy Constraints,” ISIT 2022,
  Surin Ahn, Wei-Ning Chen, and Ayfer Özgür

• “Optimal Compression of Locally Differentially Private Mechanisms,” AISTATS 2022,
  Abhin Shah, Wei-Ning Chen, Johannes Balle, Peter Kairouz, and Lucas Theis

• “Pointwise Bounds for Distribution Estimation under Communication Constraints,” NeurIPS 2021,
  Wei-Ning Chen, Peter Kairouz and Ayfer Özgür

• “Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints,” COLT 2021,
  Wei-Ning Chen, Peter Kairouz and Ayfer Özgür

• “Adaptive Group Testing on Networks with Community Structure,” ISIT 2021,
  Surin Ahn, Wei-Ning Chen and Ayfer Özgür

• “Differentially Private Federated Learning: An Information-Theoretic Perspective,” ISIT 2021,
  Shahab Asoodeh, Wei-Ning Chen, Flavio P. Calmon and Ayfer Özgür

• “Breaking the Communication-Privacy-Accuracy Trilemma,” NeurIPS 2020,
  Wei-Ning Chen, Peter Kairouz and Ayfer Özgür

• “On the Price of Source Anonymity in Heterogeneous Parametric Point Estimation,” ISIT 2019,
  Wei-Ning Chen and I-Hsiang Wang

• “On the Fundamental Limits of Heterogeneous Distributed Detection: Price of Anonymity,” ISIT 2018,
  Wei-Ning Chen, Ho-Chun Chen and I-Hsiang Wang

• “Partial Data Extraction via Noisy Histogram Queries: Information Theoretic Bounds,” ISIT 2017,
  Wei-Ning Chen and I-Hsiang Wang

Master Thesis

• “Fundamental Limits of Anonymous Statistical Inference: Privacy-Preserving Crowdsourcing and Heterogeneous Sensor Networks”, 2018, Wei-Ning Chen, [Slides].