A bounded-noise mechanism for differential privacy

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A bounded-noise mechanism for differential privacy

7 December 2020

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Papers citing "A bounded-noise mechanism for differential privacy"

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Title
Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data AnalysisSymposium on the Theory of Computing (STOC), 2024 Xin Lyu Kunal Talwar 198 1 0 18 Dec 2024
Over-the-Air Federated Adaptive Data Analysis: Preserving Accuracy via Opportunistic Differential Privacy A. H. Hadavi M. M. Mojahedian M. R. Aref 332 1 0 24 Nov 2024
Differentially Private Federated Learning without Noise Addition: When is it Possible? Jiang Zhang Konstantinos Psounis FedML 279 0 0 06 May 2024
Privacy Amplification for the Gaussian Mechanism via Bounded Support Shengyuan Hu Saeed Mahloujifar Virginia Smith Kamalika Chaudhuri Chuan Guo FedML 169 1 0 07 Mar 2024
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of InteractivityAnnual Conference Computational Learning Theory (COLT), 2023 A. F. Pour Hassan Ashtiani S. Asoodeh 183 2 0 09 Dec 2023
AnoFel: Supporting Anonymity for Privacy-Preserving Federated LearningProceedings on Privacy Enhancing Technologies (PoPETs), 2023 Ghada Almashaqbeh Zahra Ghodsi FedML 144 3 0 12 Jun 2023
Adaptive Data Analysis in a Balanced Adversarial ModelNeural Information Processing Systems (NeurIPS), 2023 Kobbi Nissim Uri Stemmer Eliad Tsfadia FedML 151 3 0 24 May 2023
Certified private data release for sparse Lipschitz functionsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023 Konstantin Donhauser J. Lokna Amartya Sanyal M. Boedihardjo R. Honig Fanny Yang 221 4 0 19 Feb 2023
Subsampling Suffices for Adaptive Data AnalysisSymposium on the Theory of Computing (STOC), 2023 Guy Blanc 204 12 0 17 Feb 2023
Private Multi-Winner Voting for Machine LearningProceedings on Privacy Enhancing Technologies (PoPETs), 2022 Adam Dziedzic Christopher A. Choquette-Choo Natalie Dullerud Vinith Suriyakumar Ali Shahin Shamsabadi Muhammad Ahmad Kaleem S. Jha Nicolas Papernot Xiao Wang 180 1 0 23 Nov 2022
Generalized Private Selection and Testing with High ConfidenceInformation Technology Convergence and Services (ITCS), 2022 E. Cohen Xin Lyu Jelani Nelson Tamas Sarlos Uri Stemmer 204 7 0 22 Nov 2022
Accuracy Gains from Privacy Amplification Through Sampling for Differential Privacy Jingchen Hu Joerg Drechsler Hang J Kim FedML 178 2 0 17 Mar 2021
A Central Limit Theorem for Differentially Private Query AnsweringNeural Information Processing Systems (NeurIPS), 2021 Jinshuo Dong Weijie J. Su Linjun Zhang 159 17 0 15 Mar 2021
On Avoiding the Union Bound When Answering Multiple Differentially Private QueriesAnnual Conference Computational Learning Theory (COLT), 2020 Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 140 11 0 16 Dec 2020
Local Differential Privacy for Regret Minimization in Reinforcement Learning Evrard Garcelon Vianney Perchet Ciara Pike-Burke Matteo Pirotta 288 40 0 15 Oct 2020