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1902.04495
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The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy
Annals of Statistics (Ann. Stat.), 2019
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"
50 / 121 papers shown
Embedding-Space Data Augmentation to Prevent Membership Inference Attacks in Clinical Time Series Forecasting
Marius Fracarolli
Michael Staniek
Stefan Riezler
198
0
0
07 Nov 2025
Differentially Private High-dimensional Variable Selection via Integer Programming
Petros Prastakos
Kayhan Behdin
Rahul Mazumder
137
0
0
24 Oct 2025
On the Sample Complexity of Differentially Private Policy Optimization
Yi He
Xingyu Zhou
163
0
0
24 Oct 2025
Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees
Shurong Lin
Aleksandra Slavković
Deekshith Reddy Bhoomireddy
206
0
0
19 Oct 2025
Federated Learning of Quantile Inference under Local Differential Privacy
Leheng Cai
Qirui Hu
Shuyuan Wu
FedML
167
0
0
26 Sep 2025
Enhancing Differentially Private Linear Regression via Public Second-Moment
Zilong Cao
Hai Zhang
122
0
0
25 Aug 2025
High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference
Ziliang Shen
Caixing Wang
Shaoli Wang
Yibo Yan
198
0
0
07 Aug 2025
Statistical Inference for Differentially Private Stochastic Gradient Descent
Xintao Xia
Linjun Zhang
Zhanrui Cai
282
0
0
28 Jul 2025
Lower Bounds for Public-Private Learning under Distribution Shift
Amrith Rajagopal Setlur
Pratiksha Thaker
Jonathan Ullman
FedML
216
0
0
23 Jul 2025
Differentially Private Sparse Linear Regression with Heavy-tailed Responses
Xizhi Tian
Meng Ding
Touming Tao
Zihang Xiang
Di Wang
231
2
0
07 Jun 2025
Sample-optimal learning of quantum states using gentle measurements
Cristina Butucea
Jan Johannes
Henning Stein
182
3
0
30 May 2025
Private Geometric Median in Nearly-Linear Time
Syamantak Kumar
Daogao Liu
Kevin Tian
Chutong Yang
FedML
354
0
0
26 May 2025
Optimal Piecewise-based Mechanism for Collecting Bounded Numerical Data under Local Differential Privacy
Proceedings on Privacy Enhancing Technologies (PoPETs), 2025
Ye Zheng
Sumita Mishra
Yidan Hu
324
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
354
1
0
22 Apr 2025
Optimal Differentially Private Sampling of Unbounded Gaussians
Annual Conference Computational Learning Theory (COLT), 2025
Valentio Iverson
Gautam Kamath
Argyris Mouzakis
462
1
0
03 Mar 2025
Learning with Differentially Private (Sliced) Wasserstein Gradients
David Rodríguez-Vítores
Clément Lalanne
Jean-Michel Loubes
FedML
564
0
0
03 Feb 2025
A Statistical Hypothesis Testing Framework for Data Misappropriation Detection in Large Language Models
Yinpeng Cai
Lexin Li
Linjun Zhang
1.0K
3
0
05 Jan 2025
Distributed Differentially Private Data Analytics via Secure Sketching
IACR Cryptology ePrint Archive (IACR ePrint), 2024
Jakob Burkhardt
Hannah Keller
Claudio Orlandi
Chris Schwiegelshohn
FedML
472
1
0
30 Nov 2024
Privately Learning Smooth Distributions on the Hypercube by Projections
International Conference on Machine Learning (ICML), 2024
Clément Lalanne
Sébastien Gadat
409
1
0
16 Sep 2024
Learning with Shared Representations: Statistical Rates and Efficient Algorithms
Xiaochun Niu
Lili Su
Jiaming Xu
Pengkun Yang
FedML
494
2
0
07 Sep 2024
Private Means and the Curious Incident of the Free Lunch
Jack Fitzsimons
James Honaker
Michael Shoemate
Vikrant Singhal
448
3
0
19 Aug 2024
Better Locally Private Sparse Estimation Given Multiple Samples Per User
International Conference on Machine Learning (ICML), 2024
Hanfang Yang
Ke Jia
Yuheng Ma
FedML
376
2
0
08 Aug 2024
On Differentially Private U Statistics
Kamalika Chaudhuri
Po-Ling Loh
Shourya Pandey
Purnamrita Sarkar
FedML
232
2
0
06 Jul 2024
Distribution Learnability and Robustness
Shai Ben-David
Alex Bie
Gautam Kamath
Tosca Lechner
375
5
0
25 Jun 2024
Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints
T. T. Cai
Abhinav Chakraborty
Lasse Vuursteen
FedML
352
9
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
323
6
0
10 Jun 2024
PriME: Privacy-aware Membership profile Estimation in networks
Abhinav Chakraborty
Sayak Chatterjee
Sagnik Nandy
362
2
0
04 Jun 2024
FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits
Sunrit Chakraborty
Saptarshi Roy
Debabrota Basu
FedML
509
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
334
8
0
23 Apr 2024
Minimax density estimation in the adversarial framework under local differential privacy
Mélisande Albert
Juliette Chevallier
Béatrice Laurent
Ousmane Sacko
255
0
0
27 Mar 2024
Near-Optimal differentially private low-rank trace regression with guaranteed private initialization
Mengyue Zha
290
0
0
24 Mar 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations
Enayat Ullah
Michael Menart
Raef Bassily
Cristóbal Guzmán
Raman Arora
412
3
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
375
11
0
21 Feb 2024
Personalized Differential Privacy for Ridge Regression
Krishna Acharya
Franziska Boenisch
Rakshit Naidu
Juba Ziani
196
8
0
30 Jan 2024
PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model
Abhinav Chakraborty
Anirban Chatterjee
Abhinandan Dalal
259
0
0
29 Jan 2024
General Inferential Limits Under Differential and Pufferfish Privacy
International Journal of Approximate Reasoning (IJAR), 2024
J. Bailie
Ruobin Gong
382
3
0
27 Jan 2024
Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm
Journal of the American Statistical Association (JASA), 2024
Xintao Xia
Linjun Zhang
Zhanrui Cai
329
0
0
16 Jan 2024
Efficient Sparse Least Absolute Deviation Regression with Differential Privacy
IEEE Transactions on Information Forensics and Security (IEEE TIFS), 2024
Weidong Liu
Xiaojun Mao
Xiaofei Zhang
Xin Zhang
307
5
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
511
7
0
24 Dec 2023
Differentially private projection-depth-based medians
Kelly Ramsay
Dylan Spicker
342
2
0
12 Dec 2023
Mean Estimation Under Heterogeneous Privacy Demands
Syomantak Chaudhuri
Konstantin Miagkov
T. Courtade
333
5
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
405
9
0
12 Oct 2023
On the Computational Complexity of Private High-dimensional Model Selection
Neural Information Processing Systems (NeurIPS), 2023
Saptarshi Roy
Zehua Wang
Ambuj Tewari
570
1
0
11 Oct 2023
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
International Conference on Learning Representations (ICLR), 2023
Liyang Zhu
Meng Ding
Vaneet Aggarwal
Jinhui Xu
Haiyan Zhao
234
5
0
11 Oct 2023
Better and Simpler Lower Bounds for Differentially Private Statistical Estimation
IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2023
Shyam Narayanan
FedML
358
15
0
10 Oct 2023
On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence
Neural Information Processing Systems (NeurIPS), 2023
Achraf Azize
Marc Jourdan
Aymen Al Marjani
D. Basu
409
8
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
497
0
0
03 Sep 2023
Differentially Private Linear Regression with Linked Data
Shurong Lin
Elliot Paquette
E. D. Kolaczyk
256
2
0
01 Aug 2023
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes
Annual Conference Computational Learning Theory (COLT), 2023
Naty Peter
Eliad Tsfadia
Jonathan R. Ullman
545
9
0
14 Jul 2023
PLAN: Variance-Aware Private Mean Estimation
Proceedings on Privacy Enhancing Technologies (PoPETs), 2023
Martin Aumüller
C. Lebeda
Boel Nelson
Rasmus Pagh
FedML
293
6
0
14 Jun 2023
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