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• Local Pan-Privacy for Federated Analytics
  2025
• Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
  2016
• On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
  2017
• Oblivious Stash Shuffle
  2017
• Scalable Private Learning with PATE
  2018
• Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
  2018
• Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC
  2020
• Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
  2020
• Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation
  2022
• Private Online Prediction from Experts: Separations and Faster Rates
  2022
• Private Federated Statistics in an Interactive Setting
  2022
• Fast Optimal Locally Private Mean Estimation via Random Projections
  2023
• Differentially Private Heavy Hitter Detection using Federated Analytics
  2023
• PINE: Efficient Norm-Bound Verification for Secret-Shared Vectors
  2023
• Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages
  2024