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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
Differentially Private Sparse Linear Regression with Heavy-tailed Responses Xizhi Tian Meng Ding Touming Tao Zihang Xiang Di Wang 18 0 0 07 Jun 2025
Private Geometric Median in Nearly-Linear Time Syamantak Kumar Daogao Liu Kevin Tian Chutong Yang FedML 35 0 0 26 May 2025
Optimal Piecewise-based Mechanism for Collecting Bounded Numerical Data under Local Differential Privacy Ye Zheng Sumita Mishra Yidan Hu 36 0 0 21 May 2025
How Private is Your Attention? Bridging Privacy with In-Context Learning Soham Bonnerjee Zhen Wei Yeon Anna Asch Sagnik Nandy Promit Ghosal 105 0 0 22 Apr 2025
Optimal Differentially Private Sampling of Unbounded Gaussians Valentio Iverson Gautam Kamath Argyris Mouzakis 81 0 0 03 Mar 2025
Learning with Differentially Private (Sliced) Wasserstein Gradients David Rodríguez-Vítores Clément Lalanne Jean-Michel Loubes FedML 111 0 0 03 Feb 2025
A Statistical Hypothesis Testing Framework for Data Misappropriation Detection in Large Language Models Yinpeng Cai Lexin Li Linjun Zhang 473 1 0 05 Jan 2025
Distributed Differentially Private Data Analytics via Secure Sketching Jakob Burkhardt Hannah Keller Claudio Orlandi Chris Schwiegelshohn FedML 162 0 0 30 Nov 2024
Privately Learning Smooth Distributions on the Hypercube by Projections Clément Lalanne Sébastien Gadat 72 1 0 16 Sep 2024
Learning with Shared Representations: Statistical Rates and Efficient Algorithms Xiaochun Niu Lili Su Jiaming Xu Pengkun Yang FedML 68 2 0 07 Sep 2024
Private Means and the Curious Incident of the Free Lunch Jack Fitzsimons James Honaker Michael Shoemate Vikrant Singhal 85 2 0 19 Aug 2024
Better Locally Private Sparse Estimation Given Multiple Samples Per User Yuheng Ma Ke Jia Hanfang Yang FedML 85 1 0 08 Aug 2024
On Differentially Private U Statistics Kamalika Chaudhuri Po-Ling Loh Shourya Pandey Purnamrita Sarkar FedML 92 1 0 06 Jul 2024
Distribution Learnability and Robustness Shai Ben-David Alex Bie Gautam Kamath Tosca Lechner 94 2 0 25 Jun 2024
Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints T. T. Cai Abhinav Chakraborty Lasse Vuursteen FedML 83 3 0 10 Jun 2024
Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests T. T. Cai Abhinav Chakraborty Lasse Vuursteen FedML 77 2 0 10 Jun 2024
PriME: Privacy-aware Membership profile Estimation in networks Abhinav Chakraborty Sayak Chatterjee Sagnik Nandy 119 1 0 04 Jun 2024
FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits Sunrit Chakraborty Saptarshi Roy Debabrota Basu FedML 93 1 0 22 May 2024
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares Gavin Brown J. Hayase Samuel B. Hopkins Weihao Kong Xiyang Liu Sewoong Oh Juan C. Perdomo Adam D. Smith 46 2 0 23 Apr 2024
Minimax density estimation in the adversarial framework under local differential privacy Mélisande Albert Juliette Chevallier Béatrice Laurent Ousmane Sacko 50 0 0 27 Mar 2024
Near-Optimal differentially private low-rank trace regression with guaranteed private initialization Mengyue Zha 67 0 0 24 Mar 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations Enayat Ullah Michael Menart Raef Bassily Cristóbal Guzmán Raman Arora 74 2 0 06 Mar 2024
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation Gavin Brown Krishnamurthy Dvijotham Georgina Evans Daogao Liu Adam D. Smith Abhradeep Thakurta 91 6 0 21 Feb 2024
Personalized Differential Privacy for Ridge Regression Krishna Acharya Franziska Boenisch Rakshit Naidu Juba Ziani 40 2 0 30 Jan 2024
PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model Abhinav Chakraborty Anirban Chatterjee Abhinandan Dalal 50 0 0 29 Jan 2024
General Inferential Limits Under Differential and Pufferfish Privacy J. Bailie Ruobin Gong 72 1 0 27 Jan 2024
Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm Xintao Xia Linjun Zhang Zhanrui Cai 78 0 0 16 Jan 2024
Efficient Sparse Least Absolute Deviation Regression with Differential Privacy Weidong Liu Xiaojun Mao Xiaofei Zhang Xin Zhang 55 2 0 02 Jan 2024
On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift Pratiksha Thaker Amrith Rajagopal Setlur Zhiwei Steven Wu Virginia Smith 88 2 0 24 Dec 2023
Differentially private projection-depth-based medians Kelly Ramsay Dylan Spicker 45 2 0 12 Dec 2023
Mean Estimation Under Heterogeneous Privacy Demands Syomantak Chaudhuri Konstantin Miagkov T. Courtade 72 1 0 19 Oct 2023
Differentially Private Non-convex Learning for Multi-layer Neural Networks Hanpu Shen Cheng-Long Wang Zihang Xiang Yiming Ying Di Wang 81 8 0 12 Oct 2023
On the Computational Complexity of Private High-dimensional Model Selection Saptarshi Roy Zehua Wang Ambuj Tewari 63 0 0 11 Oct 2023
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model Liyang Zhu Meng Ding Vaneet Aggarwal Jinhui Xu Di Wang 40 5 0 11 Oct 2023
Better and Simpler Lower Bounds for Differentially Private Statistical Estimation Shyam Narayanan FedML 68 11 0 10 Oct 2023
On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence Achraf Azize Marc Jourdan Aymen Al Marjani D. Basu 84 4 0 05 Sep 2023
The Normal Distributions Indistinguishability Spectrum and its Application to Privacy-Preserving Machine Learning Yun Lu Malik Magdon-Ismail Yu Wei Vassilis Zikas 71 0 0 03 Sep 2023
Differentially Private Linear Regression with Linked Data Shurong Lin Elliot Paquette E. D. Kolaczyk 66 1 0 01 Aug 2023
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes Naty Peter Eliad Tsfadia Jonathan R. Ullman 96 5 0 14 Jul 2023
PLAN: Variance-Aware Private Mean Estimation Martin Aumüller C. Lebeda Boel Nelson Rasmus Pagh FedML 63 4 0 14 Jun 2023
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training Alyssa Huang Peihan Liu Ryumei Nakada Linjun Zhang Wanrong Zhang VLM 129 6 0 13 Jun 2023
Differentially private sliced inverse regression in the federated paradigm Shuai He Jiawei Zhang Xin Chen FedML 71 1 0 10 Jun 2023
Adaptive False Discovery Rate Control with Privacy Guarantee Xintao Xia Zhanrui Cai 31 1 0 31 May 2023
Discover and Cure: Concept-aware Mitigation of Spurious Correlation Shirley Wu Mert Yuksekgonul Linjun Zhang James Zou 149 62 0 01 May 2023
Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free Syomantak Chaudhuri T. Courtade 47 4 0 27 Apr 2023
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions Vikrant Singhal 91 9 0 13 Apr 2023
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation Wei-Ning Chen Danni Song Ayfer Özgür Peter Kairouz FedML 71 28 0 04 Apr 2023
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning T. Tony Cai Yichen Wang Linjun Zhang 81 19 0 13 Mar 2023
Improved Differentially Private Regression via Gradient Boosting Shuai Tang Sergul Aydore Michael Kearns Saeyoung Rho Aaron Roth Yichen Wang Yu Wang Zhiwei Steven Wu FedML 67 4 0 06 Mar 2023
Subset-Based Instance Optimality in Private Estimation Travis Dick Alex Kulesza Ziteng Sun A. Suresh 101 9 0 01 Mar 2023