Privacy Amplification via Random Check-Ins

13 July 2020

Papers citing "Privacy Amplification via Random Check-Ins"

20 / 20 papers shown

Title
Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security Yiwei Zhang R. Behnia A. Yavuz Reza Ebrahimi E. Bertino FedML 31 0 0 09 May 2025
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 50 0 0 21 Feb 2025
Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy Khaoula Chehbouni Martine De Cock Gilles Caporossi Afaf Taik Reihaneh Rabbany G. Farnadi 73 0 0 21 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 80 3 0 21 Dec 2024
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 38 5 0 08 Oct 2024
Differentially Private Aggregation via Imperfect Shuffling Badih Ghazi Ravi Kumar Pasin Manurangsi Jelani Nelson Samson Zhou FedML 30 1 0 28 Aug 2023
Amplification by Shuffling without Shuffling Borja Balle James Bell Adria Gascon FedML 32 2 0 18 May 2023
Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy R. Saha Mohamed Seif M. Yemini Andrea J. Goldsmith H. Vincent Poor FedML 19 2 0 28 Feb 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 15 18 0 22 Jan 2023
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers Moritz Kirschte Sebastian Meiser Saman Ardalan Esfandiar Mohammadi FedML 23 0 0 03 Nov 2022
Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy Zhifeng Jiang Wei Wang Ruichuan Chen 40 6 0 26 Sep 2022
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy Vitaly Feldman Audra McMillan Kunal Talwar FedML 20 47 0 09 Aug 2022
What Do We Mean by Generalization in Federated Learning? Honglin Yuan Warren Morningstar Lin Ning K. Singhal OOD FedML 24 71 0 27 Oct 2021
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges S. Ulukus Salman Avestimehr Michael C. Gastpar S. Jafar Ravi Tandon Chao Tian FedML 22 64 0 30 Jul 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 181 411 0 14 Jul 2021
Differentially private inference via noisy optimization Marco Avella-Medina Casey Bradshaw Po-Ling Loh FedML 25 29 0 19 Mar 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections Zhiqi Bu Sivakanth Gopi Janardhan Kulkarni Y. Lee J. Shen U. Tantipongpipat FedML 24 41 0 05 Feb 2021
Privacy Amplification by Decentralization Edwige Cyffers A. Bellet FedML 42 39 0 09 Dec 2020
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 141 420 0 29 Nov 2018
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 99 570 0 08 Dec 2012