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"

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Title
Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning Linda Scheu-Hachtel Jasmin Zalonis 48 0 0 09 May 2025
On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models Antti Koskela Mohamed Seif Andrea J. Goldsmith 31 1 0 09 May 2025
Conformal-DP: Differential Privacy on Riemannian Manifolds via Conformal Transformation Peilin He Liou Tang M. Amin Rahimian James Joshi 36 0 0 29 Apr 2025
Clustering-Based Evolutionary Federated Multiobjective Optimization and Learning Chengui Xiao Songbai Liu FedML 72 0 0 29 Apr 2025
General-Purpose $f$ -DP Estimation and Auditing in a Black-Box Setting Önder Askin Holger Dette Martin Dunsche T. Kutta Yun Lu Yu Wei Vassilis Zikas 52 0 0 10 Feb 2025
Differential Privacy with Higher Utility by Exploiting Coordinate-wise Disparity: Laplace Mechanism Can Beat Gaussian in High Dimensions Gokularam Muthukrishnan Sheetal Kalyani 87 0 0 28 Jan 2025
Balls-and-Bins Sampling for DP-SGD Lynn Chua Badih Ghazi Charlie Harrison Ethan Leeman Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 85 4 0 21 Dec 2024
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 38 6 0 08 Oct 2024
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang Zhiqi Bu Borja Balle Mingyi Hong Meisam Razaviyayn Vahab Mirrokni 78 2 0 04 Oct 2024
Rewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions Siqiao Mu Diego Klabjan MU 50 3 0 15 Sep 2024
Gaussian Differentially Private Human Faces Under a Face Radial Curve Representation Carlos Soto M. Reimherr Aleksandra B. Slavkovic Mark Shriver CVBM 31 1 0 10 Sep 2024
Better Gaussian Mechanism using Correlated Noise Christian Janos Lebeda 44 2 0 13 Aug 2024
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy Wei Huo Changxin Liu Kemi Ding Karl H. Johansson Ling Shi FedML 43 0 0 08 Aug 2024
Towards Certified Unlearning for Deep Neural Networks Binchi Zhang Yushun Dong Tianhao Wang Wenlin Yao MU 64 7 0 01 Aug 2024
Private Collaborative Edge Inference via Over-the-Air Computation Selim F. Yilmaz Burak Hasircioglu Li Qiao Deniz Gunduz FedML 64 1 0 30 Jul 2024
GCON: Differentially Private Graph Convolutional Network via Objective Perturbation Jianxin Wei Yizheng Zhu Xiaokui Xiao Ergute Bao Yin Yang Kuntai Cai Beng Chin Ooi AAML 29 0 0 06 Jul 2024
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition C. Lebeda Matthew Regehr Gautam Kamath Thomas Steinke 53 9 0 27 May 2024
FastLloyd: Federated, Accurate, Secure, and Tunable $k$ -Means Clustering with Differential Privacy Abdulrahman Diaa Thomas Humphries Florian Kerschbaum FedML 35 0 0 03 May 2024
Budget Recycling Differential Privacy Bo Jiang Jian Du Sagar Shamar Qiang Yan 26 1 0 18 Mar 2024
Visual Privacy Auditing with Diffusion Models Kristian Schwethelm Johannes Kaiser Moritz Knolle Daniel Rueckert Daniel Rueckert Alexander Ziller DiffM AAML 35 0 0 12 Mar 2024
Closed-Form Bounds for DP-SGD against Record-level Inference Giovanni Cherubin Boris Köpf Andrew J. Paverd Shruti Tople Lukas Wutschitz Santiago Zanella Béguelin 46 2 0 22 Feb 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization Xinyu Tang Ashwinee Panda Milad Nasr Saeed Mahloujifar Prateek Mittal 47 18 0 09 Jan 2024
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST) Xingyuan Zhao Fang Liu 30 0 0 20 Dec 2023
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation Dzung Pham Shreyas Kulkarni Amir Houmansadr 33 0 0 29 Oct 2023
Privately Aligning Language Models with Reinforcement Learning Fan Wu Huseyin A. Inan A. Backurs Varun Chandrasekaran Janardhan Kulkarni Robert Sim 29 6 0 25 Oct 2023
Communication Efficient Private Federated Learning Using Dithering Burak Hasircioglu Deniz Gunduz FedML 45 7 0 14 Sep 2023
Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective Héber H. Arcolezi Sébastien Gambs 40 1 0 04 Sep 2023
Samplable Anonymous Aggregation for Private Federated Data Analysis Kunal Talwar Shan Wang Audra McMillan Vojta Jina Vitaly Feldman ... Congzheng Song Karl Tarbe Sebastian Vogt L. Winstrom Shundong Zhou FedML 38 13 0 27 Jul 2023
Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning Tamas Madl Weijie Xu Olivia Choudhury Matthew Howard 34 5 0 04 Jul 2023
Differential Privacy for Class-based Data: A Practical Gaussian Mechanism Raksha Ramakrishna Anna Scaglione Tong Wu Nikhil Ravi S. Peisert 22 4 0 08 Jun 2023
Differentially Private Image Classification by Learning Priors from Random Processes Xinyu Tang Ashwinee Panda Vikash Sehwag Prateek Mittal 23 20 0 08 Jun 2023
On Consistent Bayesian Inference from Synthetic Data Ossi Raisa Joonas Jälkö Antti Honkela SyDa 31 2 0 26 May 2023
Differentially Private Synthetic Data via Foundation Model APIs 1: Images Zinan Lin Sivakanth Gopi Janardhan Kulkarni Harsha Nori Sergey Yekhanin 41 37 0 24 May 2023
TPMDP: Threshold Personalized Multi-party Differential Privacy via Optimal Gaussian Mechanism Jiandong Liu Lan Zhang Chaojie Lv Ting Yu N. Freris Xiang-Yang Li 29 0 0 18 May 2023
An Optimal and Scalable Matrix Mechanism for Noisy Marginals under Convex Loss Functions Yingtai Xiao Guanlin He Danfeng Zhang Daniel Kifer 34 4 0 14 May 2023
On the connection between the ABS perturbation methodology and differential privacy Parastoo Sadeghi Chien-Hung Chien 29 2 0 24 Mar 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 96 167 0 01 Mar 2023
Efficient Privacy-Preserved Processing of Multimodal Data for Vehicular Traffic Analysis Meisam Mohammady Reza Arablouei 32 0 0 20 Feb 2023
DP-BART for Privatized Text Rewriting under Local Differential Privacy Timour Igamberdiev Ivan Habernal 23 17 0 15 Feb 2023
OPORP: One Permutation + One Random Projection Ping Li Xiaoyun Li 39 8 0 07 Feb 2023
Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds Anastasia Borovykh N. Kantas P. Parpas G. Pavliotis 19 1 0 01 Feb 2023
Differentially Private Distributed Bayesian Linear Regression with MCMC Barics Alparslan S. Yıldırım cS. .Ilker Birbil FedML 25 1 0 31 Jan 2023
The Bounded Gaussian Mechanism for Differential Privacy Bo Chen Matthew T. Hale 16 4 0 30 Nov 2022
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies Zhanyu Wang Guang Cheng Jordan Awan 34 9 0 12 Oct 2022
Federated Boosted Decision Trees with Differential Privacy Samuel Maddock Graham Cormode Tianhao Wang Carsten Maple S. Jha FedML 34 29 0 06 Oct 2022
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 66 50 0 02 Oct 2022
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census Daniel Kifer John M. Abowd Robert Ashmead Ryan Cumings-Menon Philip Leclerc Ashwin Machanavajjhala William Sexton Pavel I Zhuravlev 48 26 0 07 Sep 2022
The Saddle-Point Accountant for Differential Privacy Wael Alghamdi S. Asoodeh Flavio du Pin Calmon Juan Felipe Gomez O. Kosut Lalitha Sankar Fei Wei 25 7 0 20 Aug 2022
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