Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising

16 May 2018

Papers citing "Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising"

36 / 86 papers shown

Title
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints Justin Whitehouse Zhiwei Steven Wu Aaditya Ramdas Ryan M. Rogers 11 9 0 15 Jun 2022
Analytical Composition of Differential Privacy via the Edgeworth Accountant Hua Wang Sheng-yang Gao Huanyu Zhang Milan Shen Weijie J. Su FedML 36 21 0 09 Jun 2022
Noise-Aware Statistical Inference with Differentially Private Synthetic Data Ossi Raisa Joonas Jälkö Samuel Kaski Antti Honkela SyDa 43 10 0 28 May 2022
Auditing Differential Privacy in High Dimensions with the Kernel Quantum Rényi Divergence Carles Domingo-Enrich Youssef Mroueh 27 5 0 27 May 2022
Distributed non-disclosive validation of predictive models by a modified ROC-GLM Daniel Schalk V. Hoffmann B. Bischl U. Mansmann 14 3 0 21 Mar 2022
Bounding Membership Inference Anvith Thudi Ilia Shumailov Franziska Boenisch Nicolas Papernot 33 18 0 24 Feb 2022
Over-the-Air Ensemble Inference with Model Privacy Selim F. Yilmaz Burak Hasircioglu Deniz Gunduz FedML 35 23 0 07 Feb 2022
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning A. Mondal Harpreet Virk Debayan Gupta 40 15 0 06 Feb 2022
Exact Privacy Analysis of the Gaussian Sparse Histogram Mechanism Brian Karrer Daniel Kifer Arjun S. Wilkins Danfeng Zhang 20 4 0 02 Feb 2022
Privately Publishable Per-instance Privacy Rachel Redberg Yu-Xiang Wang 32 17 0 03 Nov 2021
The Skellam Mechanism for Differentially Private Federated Learning Naman Agarwal Peter Kairouz Ziyu Liu FedML 22 122 0 11 Oct 2021
Differentially Private n-gram Extraction Kunho Kim Sivakanth Gopi Janardhan Kulkarni Sergey Yekhanin 23 15 0 05 Aug 2021
When Differential Privacy Meets Interpretability: A Case Study Rakshit Naidu Aman Priyanshu Aadith Kumar Sasikanth Kotti Haofan Wang Fatemehsadat Mireshghallah 27 9 0 24 Jun 2021
A Vertical Federated Learning Framework for Graph Convolutional Network Xiang Ni Xiaolong Xu Lingjuan Lyu Changhua Meng Weiqiang Wang FedML 19 36 0 22 Jun 2021
Optimal Accounting of Differential Privacy via Characteristic Function Yuqing Zhu Jinshuo Dong Yu-Xiang Wang 18 98 0 16 Jun 2021
DP-SIGNSGD: When Efficiency Meets Privacy and Robustness Lingjuan Lyu FedML AAML 27 19 0 11 May 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 44 232 0 12 Feb 2021
A bounded-noise mechanism for differential privacy Y. Dagan Gil Kur 25 22 0 07 Dec 2020
A Distributed Privacy-Preserving Learning Dynamics in General Social Networks Youming Tao Shuzhen Chen Feng Li Dongxiao Yu Jiguo Yu Hao Sheng FedML 19 3 0 15 Nov 2020
Differentially Private Bayesian Inference for Generalized Linear Models Tejas D. Kulkarni Joonas Jälkö A. Koskela Samuel Kaski Antti Honkela 35 31 0 01 Nov 2020
Permute-and-Flip: A new mechanism for differentially private selection Ryan McKenna Daniel Sheldon 112 47 0 23 Oct 2020
Differentially private partition selection Damien Desfontaines James R. Voss Bryant Gipson Chinmoy Mandayam FedML 22 15 0 05 Jun 2020
Revisiting Membership Inference Under Realistic Assumptions Bargav Jayaraman Lingxiao Wang Katherine Knipmeyer Quanquan Gu David Evans 24 147 0 21 May 2020
Differentially Private Set Union Sivakanth Gopi P. Gulhane Janardhan Kulkarni J. Shen Milad Shokouhi Sergey Yekhanin FedML 27 32 0 22 Feb 2020
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$ -Divergences S. Asoodeh Jiachun Liao Flavio du Pin Calmon O. Kosut Lalitha Sankar FedML 22 38 0 16 Jan 2020
An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning Zhiying Xu Shuyu Shi A. Liu Jun Zhao Lin Chen FedML 26 36 0 19 Dec 2019
Reviewing and Improving the Gaussian Mechanism for Differential Privacy Jun Zhao Teng Wang Tao Bai Kwok-Yan Lam Zhiying Xu Shuyu Shi Xuebin Ren Xinyu Yang Yang Liu Han Yu 44 30 0 27 Nov 2019
Diffprivlib: The IBM Differential Privacy Library N. Holohan S. Braghin Pól Mac Aonghusa Killian Levacher SyDa 23 129 0 04 Jul 2019
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM Bao Wang Quanquan Gu M. Boedihardjo Farzin Barekat Stanley J. Osher 16 25 0 28 Jun 2019
Locally Differentially Private Data Collection and Analysis Teng Wang Jun Zhao Xinyu Yang Xuebin Ren 27 13 0 05 Jun 2019
The Privacy Blanket of the Shuffle Model Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 42 236 0 07 Mar 2019
Privacy and Utility Tradeoff in Approximate Differential Privacy Quan Geng Wei Ding Ruiqi Guo Sanjiv Kumar 21 23 0 01 Oct 2018
Optimal Noise-Adding Mechanism in Additive Differential Privacy Quan Geng Wei Ding Ruiqi Guo Sanjiv Kumar 19 34 0 26 Sep 2018
Subsampled Rényi Differential Privacy and Analytical Moments Accountant Yu-Xiang Wang Borja Balle S. Kasiviswanathan 14 397 0 31 Jul 2018
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle Gilles Barthe Marco Gaboardi 27 378 0 04 Jul 2018
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm Christopher DeCarolis Mukul Ram Seyed-Alireza Esmaeili Yu-Xiang Wang Furong Huang FedML 11 12 0 25 May 2018