Sample Complexity Bounds on Differentially Private Learning via Communication Complexity

25 February 2014

Papers citing "Sample Complexity Bounds on Differentially Private Learning via Communication Complexity"

19 / 19 papers shown

Title
$An \tilde{O}ptimal Differentially Private Learner for Concept Classes with VC Dimension 1$ An \tilde{O}ptimal Differentially Private Learner for Concept Classes with VC Dimension 1 Chao Yan 21 0 0 10 May 2025
Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy Bo Li Wei Wang Peng Ye FedML 36 0 0 30 Jul 2024
PILLAR: How to make semi-private learning more effective Francesco Pinto Yaxian Hu Fanny Yang Amartya Sanyal 46 11 0 06 Jun 2023
A Unified Characterization of Private Learnability via Graph Theory N. Alon Shay Moran Hilla Schefler Amir Yehudayoff 29 2 0 08 Apr 2023
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension Yuval Filmus Steve Hanneke Idan Mehalel Shay Moran 20 11 0 27 Feb 2023
Certified private data release for sparse Lipschitz functions Konstantin Donhauser J. Lokna Amartya Sanyal M. Boedihardjo R. Honig Fanny Yang 46 3 0 19 Feb 2023
Private Isotonic Regression Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi 21 0 0 27 Oct 2022
Algorithms with More Granular Differential Privacy Guarantees Badih Ghazi Ravi Kumar Pasin Manurangsi Thomas Steinke 59 6 0 08 Sep 2022
Memory Bounds for Continual Learning Xi Chen Christos H. Papadimitriou Binghui Peng CLL LRM 29 22 0 22 Apr 2022
Provably Private Distributed Averaging Consensus: An Information-Theoretic Approach Mohammad Fereydounian Aryan Mokhtari Ramtin Pedarsani Hamed Hassani FedML 27 2 0 18 Feb 2022
Reproducibility in Learning R. Impagliazzo Rex Lei T. Pitassi Jessica Sorrell 21 43 0 20 Jan 2022
An Equivalence Between Private Classification and Online Prediction Mark Bun Roi Livni Shay Moran 21 75 0 01 Mar 2020
Quantum statistical query learning Srinivasan Arunachalam A. Grilo Henry Yuen 17 32 0 19 Feb 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 26 14 0 18 Dec 2019
Privately Learning Thresholds: Closing the Exponential Gap Haim Kaplan Katrina Ligett Yishay Mansour M. Naor Uri Stemmer 23 55 0 22 Nov 2019
Private PAC learning implies finite Littlestone dimension N. Alon Roi Livni M. Malliaris Shay Moran 20 109 0 04 Jun 2018
Privacy-preserving Prediction Cynthia Dwork Vitaly Feldman 22 90 0 27 Mar 2018
Order-Revealing Encryption and the Hardness of Private Learning Mark Bun Mark Zhandry FedML 30 34 0 03 May 2015
Learning Privately with Labeled and Unlabeled Examples A. Beimel Kobbi Nissim Uri Stemmer 35 23 0 10 Jul 2014