ICML 2021, one of the biggest conferences in machine learning, naturally has a ton of interesting sounding papers on the topic of differential privacy. We went through this year’s accepted papers and aggregated all the relevant papers we could find. In addition, this year features three workshops on the topic of privacy, as well as a tutorial. As always, please inform us if we overlooked any papers on differential privacy.

Workshops

• Federated Learning for User Privacy and Data Confidentiality

• Machine Learning for Data: Automated Creation, Privacy, Bias

• Theory and Practice of Differential Privacy

Tutorial

• Privacy in Learning: Basics and the Interplay
  Huishuai Zhang, Wei Chen

Papers

• A Framework for Private Matrix Analysis in Sliding Window Model
  Jalaj Upadhyay, Sarvagya Upadhyay

• Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
  Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni

• Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message
  Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha

• Differentially Private Bayesian Inference for Generalized Linear Models
  Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela

• Differentially-Private Clustering of Easy Instances
  Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia

• Differentially Private Correlation Clustering
  Mark Bun, Marek Elias, Janardhan Kulkarni

• Differentially Private Densest Subgraph Detection
  Dung Nguyen, Anil Vullikanti

• Differentially Private Quantiles
  Jennifer Gillenwater, Matthew Joseph, Alex Kulesza

• Differentially Private Query Release Through Adaptive Projection
  Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit Siva

• Differentially Private Sliced Wasserstein Distance
  Alain Rakotomamonjy, Liva Ralaivola

• Large Scale Private Learning via Low-rank Reparametrization
  Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu

• Leveraging Public Data for Practical Private Query Release
  Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, Steven Wu

• Locally Private k-Means in One Round
  Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

• Lossless Compression of Efficient Private Local Randomizers
  Vitaly Feldman, Kunal Talwar

• Oneshot Differentially Private Top-k Selection
  Gang Qiao, Weijie Su, Li Zhang

• PAPRIKA: Private Online False Discovery Rate Control
  Wanrong Zhang, Gautam Kamath, Rachel Cummings

• Practical and Private (Deep) Learning without Sampling or Shuffling
  Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu

• Private Adaptive Gradient Methods for Convex Optimization
  Hilal Asi, John Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar

• Private Alternating Least Squares: (Nearly) Optimal Privacy/Utility Trade-off for Matrix Completion
  Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang

• Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry
  Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar

• The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
  Peter Kairouz, Ziyu Liu, Thomas Steinke