The accepted papers for NeurIPS 2021 were recently announced, and there’s a huge amount of differential privacy content. We found one relevant workshop and 48 papers. This is up from 31 papers last year, an over 50% increase! It looks like there’s huge growth in interest on differentially private machine learning. Impressively, at the time of this writing, all but five papers are already posted on arXiv! For the full list of accepted papers, see here. Please let us know if we missed relevant papers on differential privacy!

Workshops

• Privacy in Machine Learning (PriML) 2021

Papers

• A Central Limit Theorem for Differentially Private Query Answering
  Jinshuo Dong, Weijie Su, Linjun Zhang

• Adapting to function difficulty and growth conditions in private optimization
  Hilal Asi, Daniel Levy, John Duchi

• Adaptive Machine Unlearning
  Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites

• An Uncertainty Principle is a Price of Privacy-Preserving Microdata
  John Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson Garfinkel, Daniel Kifer, Philip Leclerc, William Sexton, Ashley Simpson, Christine Task, Pavel Zhuravlev

• Antipodes of Label Differential Privacy: PATE and ALIBI
  Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, Florian Tramer

• Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
  Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, Lydia Zakynthinou

• Deep Learning with Label Differential Privacy
  Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

• Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent
  Rishav Chourasia, Jiayuan Ye, Reza Shokri

• Differentially Private Empirical Risk Minimization under the Fairness Lens
  Cuong Tran, My Dinh, Ferdinando Fioretto

• Differentially Private Federated Bayesian Optimization with Distributed Exploration
  Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet

• Differentially Private Learning with Adaptive Clipping
  Galen Andrew, Om Thakkar, Swaroop Ramaswamy, Brendan McMahan

• Differentially Private Model Personalization
  Prateek Jain, John Rush, Adam Smith, Shuang Song, Abhradeep Guha Thakurta

• Differentially Private Multi-Armed Bandits in the Shuffle Model
  Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer

• Differentially Private n-gram Extraction
  Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni, Sergey Yekhanin

• Differential Privacy Over Riemannian Manifolds
  Matthew Reimherr, Karthik Bharath, Carlos Soto

• Differentially Private Sampling from Distributions
  Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith, Marika Swanberg

• Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings
  Raef Bassily, Cristóbal Guzmán, Michael Menart

• Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
  Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, Karsten Kreis

• Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization
  Pranav Subramani, Nicholas Vadivelu, Gautam Kamath

• Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms
  Jeremy Seeman, Matthew Reimherr, Aleksandra Slavković

• Fast and Memory Efficient Differentially Private-SGD via JL Projections
  Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Hanwen Shen, Uthaipon Tantipongpipat

• G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators
  Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl Gunter, Bo Li

• Generalized Linear Bandits with Local Differential Privacy
  Yuxuan Han, Zhipeng Liang, Yang Wang, Jiheng Zhang

• Individual Privacy Accounting via a Rényi Filter
  Vitaly Feldman, Tijana Zrnic

• Information-constrained optimization: can adaptive processing of gradients help?
  Jayadev Acharya, Clement Canonne, Prathamesh Mayekar, Himanshu Tyagi

• Instance-optimal Mean Estimation Under Differential Privacy
  Ziyue Huang, Yuting Liang, Ke Yi

• Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods
  Terrance Liu, Giuseppe Vietri, Steven Wu

• Learning with User-Level Privacy
  Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh

• Littlestone Classes are Privately Online Learnable
  Noah Golowich, Roi Livni

• Local Differential Privacy for Regret Minimization in Reinforcement Learning
  Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta

• Locally differentially private estimation of functionals of discrete distributions
  Cristina Butucea, Yann Issartel

• Locally private online change point detection
  Tom Berrett, Yi Yu

• Multiclass versus Binary Differentially Private PAC Learning
  Satchit Sivakumar, Mark Bun, Marco Gaboardi

• Numerical Composition of Differential Privacy
  Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz

• On the Sample Complexity of Privately Learning Axis-Aligned Rectangles
  Menachem Sadigurschi, Uri Stemmer

• Photonic Differential Privacy with Direct Feedback Alignment
  Ruben Ohana, Hamlet Medina, Julien Launay, Alessandro Cappelli, Iacopo Poli, Liva Ralaivola, Alain Rakotomamonjy

• Private and Non-private Uniformity Testing for Ranking Data
  Róbert Busa-Fekete, Dimitris Fotakis, Emmanouil Zampetakis

• Private learning implies quantum stability
  Yihui Quek, Srinivasan Arunachalam, John A Smolin

• Private Non-smooth ERM and SCO in Subquadratic Steps
  Janardhan Kulkarni, Yin Tat Lee, Daogao Liu

• Privately Learning Mixtures of Axis-Aligned Gaussians
  Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw

• Privately Learning Subspaces
  Vikrant Singhal, Thomas Steinke

• Privately Publishable Per-instance Privacy
  Rachel Redberg, Yu-Xiang Wang

• Relaxed Marginal Consistency for Differentially Private Query Answering
  Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau

• Remember What You Want to Forget: Algorithms for Machine Unlearning
  Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh

• Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning
  Antonious Girgis, Deepesh Data, Suhas Diggavi

• Robust and differentially private mean estimation
  Xiyang Liu, Weihao Kong, Sham Kakade, Sewoong Oh

• The Skellam Mechanism for Differentially Private Federated Learning
  Naman Agarwal, Peter Kairouz, Ken Liu

• User-Level Differentially Private Learning via Correlated Sampling
  Badih Ghazi, Ravi Kumar, Pasin Manurangsi