Hypothesis Testing Interpretations and Renyi Differential Privacy

24 May 2019

Gilles Barthe

Papers citing "Hypothesis Testing Interpretations and Renyi Differential Privacy"

30 / 30 papers shown

Title
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation Chengkun Wei Weixian Li Chen Gong Wenzhi Chen 58 0 0 29 Mar 2025
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators Tejumade Afonja Hui-Po Wang Raouf Kerkouche Mario Fritz SyDa 118 2 0 03 Dec 2024
Preempting Text Sanitization Utility in Resource-Constrained Privacy-Preserving LLM Interactions Robin Carpentier B. Zhao Hassan Jameel Asghar Dali Kaafar 85 1 0 18 Nov 2024
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning Fengyu Gao Ruida Zhou T. Wang Cong Shen Jing Yang 41 2 0 15 Oct 2024
The 2020 United States Decennial Census Is More Private Than You (Might) Think Buxin Su Weijie J. Su Chendi Wang 39 3 0 11 Oct 2024
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy Georgios Kaissis Stefan Kolek Borja Balle Jamie Hayes Daniel Rueckert 47 4 0 13 Jun 2024
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation Jie Xu Karthikeyan P. Saravanan Rogier van Dalen Haaris Mehmood David Tuckey Mete Ozay 56 6 0 10 May 2024
Preserving Node-level Privacy in Graph Neural Networks Zihang Xiang Tianhao Wang Di Wang 27 6 0 12 Nov 2023
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks Shijie Liu Andrew C. Cullen Paul Montague S. Erfani Benjamin I. P. Rubinstein AAML 26 3 0 15 Aug 2023
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy Georgios Kaissis Jamie Hayes Alexander Ziller Daniel Rueckert AAML 43 11 0 08 Jul 2023
Training Differentially Private Graph Neural Networks with Random Walk Sampling Morgane Ayle Jan Schuchardt Lukas Gosch Daniel Zügner Stephan Günnemann FedML 29 6 0 02 Jan 2023
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing Jie Fu Zhili Chen Xinpeng Ling 27 0 0 14 Nov 2022
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design Chuan Guo Kamalika Chaudhuri Pierre Stock Michael G. Rabbat FedML 33 7 0 08 Nov 2022
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning Samuel Maddock Alexandre Sablayrolles Pierre Stock FedML 20 22 0 06 Oct 2022
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks Chulin Xie Yunhui Long Pin-Yu Chen Qinbin Li Arash Nourian Sanmi Koyejo Bo Li FedML 48 13 0 08 Sep 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
Accelerating Vertical Federated Learning Dongqi Cai Tao Fan Yan Kang Lixin Fan Mengwei Xu Shangguang Wang Qiang Yang FedML 19 7 0 23 Jul 2022
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM Chulin Xie Pin-Yu Chen Qinbin Li Arash Nourian Ce Zhang Bo Li FedML 38 16 0 20 Jul 2022
Confidentiality Protection in the 2020 US Census of Population and Housing John M. Abowd Michael B. Hawes 19 26 0 07 Jun 2022
Quantum Differential Privacy: An Information Theory Perspective C. Hirche Cambyse Rouzé Daniel Stilck França 33 60 0 22 Feb 2022
Reconstructing Training Data with Informed Adversaries Borja Balle Giovanni Cherubin Jamie Hayes MIACV AAML 43 158 0 13 Jan 2022
Improving Differentially Private SGD via Randomly Sparsified Gradients Junyi Zhu Matthew B. Blaschko 30 5 0 01 Dec 2021
Optimal Accounting of Differential Privacy via Characteristic Function Yuqing Zhu Jinshuo Dong Yu-Xiang Wang 18 98 0 16 Jun 2021
On the Renyi Differential Privacy of the Shuffle Model Antonious M. Girgis Deepesh Data Suhas Diggavi A. Suresh Peter Kairouz 22 44 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
Revisiting Membership Inference Under Realistic Assumptions Bargav Jayaraman Lingxiao Wang Katherine Knipmeyer Quanquan Gu David Evans 24 147 0 21 May 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
Robust Aggregation for Federated Learning Krishna Pillutla Sham Kakade Zaïd Harchaoui FedML 37 629 0 31 Dec 2019
SoK: Differential Privacies Damien Desfontaines Balázs Pejó 33 122 0 04 Jun 2019