Tight Lower Bounds for Differentially Private Selection

10 April 2017

Papers citing "Tight Lower Bounds for Differentially Private Selection"

23 / 23 papers shown

Title
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning T. Tony Cai Yichen Wang Linjun Zhang 46 16 0 13 Mar 2023
Tight Data Access Bounds for Private Top- $k$ Selection Hao Wu O. Ohrimenko Anthony Wirth 26 0 0 31 Jan 2023
Answering Private Linear Queries Adaptively using the Common Mechanism Yingtai Xiao Guanhong Wang Danfeng Zhang Daniel Kifer 77 7 0 30 Nov 2022
Generalized Private Selection and Testing with High Confidence E. Cohen Xin Lyu Jelani Nelson Tamas Sarlos Uri Stemmer 28 6 0 22 Nov 2022
Private Online Prediction from Experts: Separations and Faster Rates Hilal Asi Vitaly Feldman Tomer Koren Kunal Talwar FedML 32 18 0 24 Oct 2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma Gautam Kamath Argyris Mouzakis Vikrant Singhal FedML 47 26 0 17 May 2022
The Price of Differential Privacy under Continual Observation Palak Jain Sofya Raskhodnikova Satchit Sivakumar Adam D. Smith 33 52 0 01 Dec 2021
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 135 120 0 07 Oct 2021
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation Gavin Brown Marco Gaboardi Adam D. Smith Jonathan R. Ullman Lydia Zakynthinou FedML 30 48 0 24 Jun 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
Permute-and-Flip: A new mechanism for differentially private selection Ryan McKenna Daniel Sheldon 112 47 0 23 Oct 2020
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians Ishaq Aden-Ali H. Ashtiani Gautam Kamath 48 42 0 19 Oct 2020
On Distributed Differential Privacy and Counting Distinct Elements Lijie Chen Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 33 29 0 21 Sep 2020
CoinPress: Practical Private Mean and Covariance Estimation Sourav Biswas Yihe Dong Gautam Kamath Jonathan R. Ullman 41 115 0 11 Jun 2020
A Primer on Private Statistics Gautam Kamath Jonathan R. Ullman 40 48 0 30 Apr 2020
Differentially Private Assouad, Fano, and Le Cam Jayadev Acharya Ziteng Sun Huanyu Zhang FedML 28 58 0 14 Apr 2020
The power of synergy in differential privacy: Combining a small curator with local randomizers A. Beimel Aleksandra Korolova Kobbi Nissim Or Sheffet Uri Stemmer 34 14 0 18 Dec 2019
Distributed Differential Privacy via Shuffling Albert Cheu Adam D. Smith Jonathan R. Ullman David Zeber M. Zhilyaev FedML 53 347 0 04 Aug 2018
The Limits of Post-Selection Generalization Kobbi Nissim Adam D. Smith Thomas Steinke Uri Stemmer Jonathan R. Ullman 39 26 0 15 Jun 2018
Privately Learning High-Dimensional Distributions Gautam Kamath Jerry Li Vikrant Singhal Jonathan R. Ullman FedML 72 149 0 01 May 2018
Scalable Private Learning with PATE Nicolas Papernot Shuang Song Ilya Mironov A. Raghunathan Kunal Talwar Ulfar Erlingsson 43 607 0 24 Feb 2018
The Price of Selection in Differential Privacy Mitali Bafna Jonathan R. Ullman 46 30 0 09 Feb 2017
The Power of Linear Reconstruction Attacks S. Kasiviswanathan M. Rudelson Adam D. Smith AAML 62 54 0 08 Oct 2012