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

12 February 2019

T. Tony Cai

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

37 / 37 papers shown

Title
How Private is Your Attention? Bridging Privacy with In-Context Learning Soham Bonnerjee Zhen Wei Yeon Anna Asch Sagnik Nandy Promit Ghosal 51 0 0 22 Apr 2025
Learning with Differentially Private (Sliced) Wasserstein Gradients David Rodríguez-Vítores Clément Lalanne Jean-Michel Loubes FedML 46 0 0 03 Feb 2025
Distributed Differentially Private Data Analytics via Secure Sketching Jakob Burkhardt Hannah Keller Claudio Orlandi Chris Schwiegelshohn FedML 82 0 0 30 Nov 2024
Private Means and the Curious Incident of the Free Lunch Jack Fitzsimons James Honaker Michael Shoemate Vikrant Singhal 49 2 0 19 Aug 2024
Better Locally Private Sparse Estimation Given Multiple Samples Per User Yuheng Ma Ke Jia Hanfang Yang FedML 38 1 0 08 Aug 2024
On Differentially Private U Statistics Kamalika Chaudhuri Po-Ling Loh Shourya Pandey Purnamrita Sarkar FedML 64 0 0 06 Jul 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations Enayat Ullah Michael Menart Raef Bassily Cristóbal Guzmán Raman Arora 30 1 0 06 Mar 2024
General Inferential Limits Under Differential and Pufferfish Privacy J. Bailie Ruobin Gong 35 1 0 27 Jan 2024
Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm Xintao Xia Linjun Zhang Zhanrui Cai 44 0 0 16 Jan 2024
Differentially Private Non-convex Learning for Multi-layer Neural Networks Hanpu Shen Cheng-Long Wang Zihang Xiang Yiming Ying Di Wang 49 7 0 12 Oct 2023
On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence Achraf Azize Marc Jourdan Aymen Al Marjani D. Basu 42 3 0 05 Sep 2023
Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free Syomantak Chaudhuri T. Courtade 30 4 0 27 Apr 2023
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning T. Tony Cai Yichen Wang Linjun Zhang 46 16 0 13 Mar 2023
Differentially Private Distributed Bayesian Linear Regression with MCMC Barics Alparslan S. Yıldırım cS. .Ilker Birbil FedML 25 1 0 31 Jan 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 41 9 0 30 Jan 2023
Differentially Private Confidence Intervals for Proportions under Stratified Random Sampling Shurong Lin Mark Bun Marco Gaboardi E. D. Kolaczyk Adam D. Smith 18 5 0 19 Jan 2023
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 52 7 0 24 Nov 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 32 31 0 30 Sep 2022
Analyzing the Differentially Private Theil-Sen Estimator for Simple Linear Regression Jayshree Sarathy Salil P. Vadhan 31 7 0 27 Jul 2022
Data Augmentation MCMC for Bayesian Inference from Privatized Data Nianqiao P. Ju Jordan Awan Ruobin Gong Vinayak A. Rao 32 23 0 01 Jun 2022
A Blessing of Dimensionality in Membership Inference through Regularization Jasper Tan Daniel LeJeune Blake Mason Hamid Javadi Richard G. Baraniuk 32 18 0 27 May 2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma Gautam Kamath Argyris Mouzakis Vikrant Singhal FedML 42 26 0 17 May 2022
Private High-Dimensional Hypothesis Testing Shyam Narayanan FedML 40 11 0 03 Mar 2022
Differentially-Private Clustering of Easy Instances E. Cohen Haim Kaplan Yishay Mansour Uri Stemmer Eliad Tsfadia 12 22 0 29 Dec 2021
Private Robust Estimation by Stabilizing Convex Relaxations Pravesh Kothari Pasin Manurangsi A. Velingker 35 46 0 07 Dec 2021
Distribution-Invariant Differential Privacy Xuan Bi Xiaotong Shen 24 13 0 08 Nov 2021
Private sampling: a noiseless approach for generating differentially private synthetic data M. Boedihardjo Thomas Strohmer Roman Vershynin SyDa 29 14 0 30 Sep 2021
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation Gavin Brown Marco Gaboardi Adam D. Smith Jonathan R. Ullman Lydia Zakynthinou FedML 28 48 0 24 Jun 2021
Differentially private inference via noisy optimization Marco Avella-Medina Casey Bradshaw Po-Ling Loh FedML 40 29 0 19 Mar 2021
The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds T. Tony Cai Yichen Wang Linjun Zhang FedML 43 20 0 08 Nov 2020
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians Ishaq Aden-Ali H. Ashtiani Gautam Kamath 40 41 0 19 Oct 2020
CoinPress: Practical Private Mean and Covariance Estimation Sourav Biswas Yihe Dong Gautam Kamath Jonathan R. Ullman 39 115 0 11 Jun 2020
A Primer on Private Statistics Gautam Kamath Jonathan R. Ullman 38 48 0 30 Apr 2020
Differentially Private Assouad, Fano, and Le Cam Jayadev Acharya Ziteng Sun Huanyu Zhang FedML 23 57 0 14 Apr 2020
Private Identity Testing for High-Dimensional Distributions C. Canonne Gautam Kamath Audra McMillan Jonathan R. Ullman Lydia Zakynthinou 37 36 0 28 May 2019
Geometrizing rates of convergence under local differential privacy constraints Angelika Rohde Lukas Steinberger 27 10 0 03 May 2018
Privately Learning High-Dimensional Distributions Gautam Kamath Jerry Li Vikrant Singhal Jonathan R. Ullman FedML 72 148 0 01 May 2018