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The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy

12 February 2019

T. Tony Cai

Yichen Wang

Linjun Zhang

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Papers citing "The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy"

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Title
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data Ryumei Nakada Halil Ibrahim Gulluk Zhun Deng Wenlong Ji James Zou Linjun Zhang SSL VLM 97 41 0 13 Feb 2023
Differentially Private Distributed Bayesian Linear Regression with MCMC Barics Alparslan S. Yıldırım cS. .Ilker Birbil FedML 45 1 0 31 Jan 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 68 9 0 30 Jan 2023
Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions Gavin Brown Samuel B. Hopkins Adam D. Smith FedML 73 21 0 28 Jan 2023
Differentially Private Confidence Intervals for Proportions under Stratified Random Sampling Shurong Lin Mark Bun Marco Gaboardi E. D. Kolaczyk Adam D. Smith 83 6 0 19 Jan 2023
Continual Mean Estimation Under User-Level Privacy Anand George Lekshmi Ramesh A. V. Singh Himanshu Tyagi FedML 65 9 0 20 Dec 2022
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 110 8 0 24 Nov 2022
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation Kristian Georgiev Samuel B. Hopkins FedML 90 24 0 01 Nov 2022
Differentially private multivariate medians Kelly Ramsay Aukosh Jagannath Shojaéddin Chenouri 60 4 0 12 Oct 2022
Shuffled linear regression through graduated convex relaxation Efe Onaran Soledad Villar 49 4 0 30 Sep 2022
On the Impossible Safety of Large AI Models El-Mahdi El-Mhamdi Sadegh Farhadkhani R. Guerraoui Nirupam Gupta L. Hoang Rafael Pinot Sébastien Rouault John Stephan 106 33 0 30 Sep 2022
Majority Vote for Distributed Differentially Private Sign Selection Weidong Liu Jiyuan Tu Xiaojun Mao Xinyu Chen FedML 61 1 0 08 Sep 2022
Easy Differentially Private Linear Regression Kareem Amin Matthew Joseph Mónica Ribero Sergei Vassilvitskii FedML 57 17 0 15 Aug 2022
Analyzing the Differentially Private Theil-Sen Estimator for Simple Linear Regression Jayshree Sarathy Salil P. Vadhan 64 7 0 27 Jul 2022
(Nearly) Optimal Private Linear Regression via Adaptive Clipping Prateeksha Varshney Abhradeep Thakurta Prateek Jain 76 8 0 11 Jul 2022
Data Augmentation MCMC for Bayesian Inference from Privatized Data Nianqiao P. Ju Jordan Awan Ruobin Gong Vinayak A. Rao 77 25 0 01 Jun 2022
A Blessing of Dimensionality in Membership Inference through Regularization Jasper Tan Daniel LeJeune Blake Mason Hamid Javadi Richard G. Baraniuk 64 20 0 27 May 2022
DP-PCA: Statistically Optimal and Differentially Private PCA Xiyang Liu Weihao Kong Prateek Jain Sewoong Oh 127 24 0 27 May 2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma Gautam Kamath Argyris Mouzakis Vikrant Singhal FedML 88 30 0 17 May 2022
Differentially Private Generalized Linear Models Revisited R. Arora Raef Bassily Cristóbal Guzmán Michael Menart Enayat Ullah FedML 83 18 0 06 May 2022
Private High-Dimensional Hypothesis Testing Shyam Narayanan FedML 80 12 0 03 Mar 2022
Robust Estimation of Discrete Distributions under Local Differential Privacy J. Chhor Flore Sentenac FedML 57 13 0 14 Feb 2022
On robustness and local differential privacy Mengchu Li Thomas B. Berrett Yi Yu 70 26 0 03 Jan 2022
Differentially-Private Clustering of Easy Instances E. Cohen Haim Kaplan Yishay Mansour Uri Stemmer Eliad Tsfadia 71 25 0 29 Dec 2021
Private Robust Estimation by Stabilizing Convex Relaxations Pravesh Kothari Pasin Manurangsi A. Velingker 71 47 0 07 Dec 2021
Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism Samuel B. Hopkins Gautam Kamath Mahbod Majid FedML 80 62 0 25 Nov 2021
Differentially Private Nonparametric Regression Under a Growth Condition Noah Golowich 53 6 0 24 Nov 2021
Distribution-Invariant Differential Privacy Xuan Bi Xiaotong Shen 44 14 0 08 Nov 2021
A Private and Computationally-Efficient Estimator for Unbounded Gaussians Gautam Kamath Argyris Mouzakis Vikrant Singhal Thomas Steinke Jonathan R. Ullman 95 40 0 08 Nov 2021
Universal Private Estimators Wei Dong K. Yi 81 20 0 04 Nov 2021
Private sampling: a noiseless approach for generating differentially private synthetic data M. Boedihardjo Thomas Strohmer Roman Vershynin SyDa 68 14 0 30 Sep 2021
Privacy of synthetic data: a statistical framework M. Boedihardjo Thomas Strohmer Roman Vershynin 46 15 0 03 Sep 2021
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data Lijie Hu Shuo Ni Hanshen Xiao Di Wang 145 53 0 23 Jul 2021
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation Gavin Brown Marco Gaboardi Adam D. Smith Jonathan R. Ullman Lydia Zakynthinou FedML 94 50 0 24 Jun 2021
Large Scale Private Learning via Low-rank Reparametrization Da Yu Huishuai Zhang Wei Chen Jian Yin Tie-Yan Liu 76 106 0 17 Jun 2021
Privately Learning Mixtures of Axis-Aligned Gaussians Ishaq Aden-Ali H. Ashtiani Christopher Liaw FedML 85 12 0 03 Jun 2021
High-Dimensional Differentially-Private EM Algorithm: Methods and Near-Optimal Statistical Guarantees Zhe Zhang Linjun Zhang FedML 70 3 0 01 Apr 2021
Differentially private inference via noisy optimization Marco Avella-Medina Casey Bradshaw Po-Ling Loh FedML 85 31 0 19 Mar 2021
A Central Limit Theorem for Differentially Private Query Answering Jinshuo Dong Weijie J. Su Linjun Zhang 83 15 0 15 Mar 2021
Wide Network Learning with Differential Privacy Huanyu Zhang Ilya Mironov Meisam Hejazinia 83 27 0 01 Mar 2021
Learning with User-Level Privacy Daniel Levy Ziteng Sun Kareem Amin Satyen Kale Alex Kulesza M. Mohri A. Suresh FedML 108 91 0 23 Feb 2021
Robust and Differentially Private Mean Estimation Xiyang Liu Weihao Kong Sham Kakade Sewoong Oh OOD FedML 90 77 0 18 Feb 2021
Differentially private depth functions and their associated medians Kelly Ramsay Shojaéddin Chenouri FedML 48 8 0 07 Jan 2021
The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds T. Tony Cai Yichen Wang Linjun Zhang FedML 109 21 0 08 Nov 2020
Strongly universally consistent nonparametric regression and classification with privatised data Thomas B. Berrett László Gyorfi Harro Walk 50 16 0 31 Oct 2020
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians Ishaq Aden-Ali H. Ashtiani Gautam Kamath 121 44 0 19 Oct 2020
Privacy Preserving Set-Based Estimation Using Partially Homomorphic Encryption Amr Alanwar Victor Gaßmann Xingkang He Hazem Said H. Sandberg Karl H. Johansson Matthias Althoff 45 13 0 19 Oct 2020
Differentially Private Simple Linear Regression Daniel Alabi Audra McMillan Jayshree Sarathy Adam D. Smith Salil P. Vadhan 53 55 0 10 Jul 2020
Improved Complexities for Stochastic Conditional Gradient Methods under Interpolation-like Conditions Tesi Xiao Krishnakumar Balasubramanian Saeed Ghadimi 60 2 0 15 Jun 2020
CoinPress: Practical Private Mean and Covariance Estimation Sourav Biswas Yihe Dong Gautam Kamath Jonathan R. Ullman 87 117 0 11 Jun 2020