Rényi Differential Privacy of the Sampled Gaussian Mechanism

28 August 2019

Papers citing "Rényi Differential Privacy of the Sampled Gaussian Mechanism"

32 / 182 papers shown

Title
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 318 370 0 25 Sep 2021
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index Georgios Kaissis Moritz Knolle F. Jungmann Alexander Ziller Dmitrii Usynin Daniel Rueckert 59 1 0 22 Sep 2021
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning Antonious M. Girgis Deepesh Data Suhas Diggavi FedML 74 22 0 19 Jul 2021
Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability Qiyiwen Zhang Zhiqi Bu Kan Chen Qi Long BDL UQCV 72 11 0 18 Jul 2021
Differentially private federated deep learning for multi-site medical image segmentation Alexander Ziller Dmitrii Usynin Nicolas W. Remerscheid Moritz Knolle Marcus R. Makowski R. Braren Daniel Rueckert Georgios Kaissis FedML 75 23 0 06 Jul 2021
Large Scale Private Learning via Low-rank Reparametrization Da Yu Huishuai Zhang Wei Chen Jian Yin Tie-Yan Liu 76 106 0 17 Jun 2021
A Privacy-Preserving and Trustable Multi-agent Learning Framework Anudit Nagar Cuong Tran Ferdinando Fioretto 27 1 0 02 Jun 2021
Privacy Amplification Via Bernoulli Sampling Jacob Imola Kamalika Chaudhuri FedML 80 7 0 21 May 2021
On the Renyi Differential Privacy of the Shuffle Model Antonious M. Girgis Deepesh Data Suhas Diggavi A. Suresh Peter Kairouz 104 44 0 11 May 2021
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation Wei Ping Fan Wu Yunhui Long Luka Rimanic Ce Zhang Yue Liu FedML 127 65 0 20 Mar 2021
Wide Network Learning with Differential Privacy Huanyu Zhang Ilya Mironov Meisam Hejazinia 86 27 0 01 Mar 2021
Constrained Differentially Private Federated Learning for Low-bandwidth Devices Raouf Kerkouche G. Ács C. Castelluccia P. Genevès 60 7 0 27 Feb 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 169 116 0 25 Feb 2021
Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT A. Koskela Antti Honkela 69 20 0 24 Feb 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 116 243 0 12 Feb 2021
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning Milad Nasr Shuang Song Abhradeep Thakurta Nicolas Papernot Nicholas Carlini MIACV FedML 173 226 0 11 Jan 2021
Differentially Private Federated Learning for Cancer Prediction C. Béguier Jean Ogier du Terrail I. Meah M. Andreux Eric W. Tramel FedML 63 21 0 08 Jan 2021
Kamino: Constraint-Aware Differentially Private Data Synthesis Chang Ge Shubhankar Mohapatra Xi He Ihab F. Ilyas SyDa 86 47 0 31 Dec 2020
Differentially Private Synthetic Medical Data Generation using Convolutional GANs A. Torfi Edward A. Fox Chandan K. Reddy SyDa MedIm 88 126 0 22 Dec 2020
Private Wireless Federated Learning with Anonymous Over-the-Air Computation Burak Hasircioglu Deniz Gunduz FedML 78 33 0 17 Nov 2020
Compression Boosts Differentially Private Federated Learning Raouf Kerkouche G. Ács C. Castelluccia P. Genevès FedML 75 29 0 10 Nov 2020
Federated Learning in Adversarial Settings Raouf Kerkouche G. Ács C. Castelluccia FedML 48 16 0 15 Oct 2020
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach Cuong Tran Ferdinando Fioretto Pascal Van Hentenryck FedML 82 80 0 26 Sep 2020
Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising Milad Nasr Reza Shokri Amir Houmansadr 72 41 0 22 Jul 2020
The Trade-Offs of Private Prediction Laurens van der Maaten Awni Y. Hannun 103 24 0 09 Jul 2020
Auditing Differentially Private Machine Learning: How Private is Private SGD? Matthew Jagielski Jonathan R. Ullman Alina Oprea FedML 78 251 0 13 Jun 2020
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT A. Koskela Hibiki Ito Lukas Prediger Antti Honkela 70 59 0 12 Jun 2020
Differentially Private Generation of Small Images J. Schwabedal Pascal Michel Mario S. Riontino 8 2 0 02 May 2020
Differentially Private Federated Learning with Laplacian Smoothing Zhicong Liang Bao Wang Quanquan Gu Stanley Osher Yuan Yao FedML 81 7 0 01 May 2020
Private Knowledge Transfer via Model Distillation with Generative Adversarial Networks Di Gao Cheng Zhuo 21 4 0 05 Apr 2020
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 294 6,335 0 10 Dec 2019
Efficient Privacy-Preserving Stochastic Nonconvex Optimization Lingxiao Wang Bargav Jayaraman David Evans Quanquan Gu 61 28 0 30 Oct 2019