Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity

29 November 2018

Papers citing "Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity"

50 / 74 papers shown

Title
Differential Privacy for Network Assortativity Fei Ma Jinzhi Ouyang Xincheng Hu 37 0 0 06 May 2025
Towards Trustworthy Federated Learning with Untrusted Participants Youssef Allouah R. Guerraoui John Stephan FedML 46 0 0 03 May 2025
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 48 0 0 21 Feb 2025
Differentially Private Empirical Cumulative Distribution Functions Antoine Barczewski Amal Mawass Jan Ramon FedML 37 0 0 10 Feb 2025
Distributed Differentially Private Data Analytics via Secure Sketching Jakob Burkhardt Hannah Keller Claudio Orlandi Chris Schwiegelshohn FedML 82 0 0 30 Nov 2024
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 36 5 0 08 Oct 2024
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates Puning Zhao Jiafei Wu Zhe Liu Chong Wang Rongfei Fan Qingming Li 42 1 0 19 Aug 2024
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence Shuya Feng Meisam Mohammady Hanbin Hong Shenao Yan Ashish Kundu Binghui Wang Yuan Hong FedML 36 3 0 20 Jul 2024
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling Shaowei Wang Changyu Dong Xiangfu Song Jin Li Zhili Zhou Di Wang Han Wu 41 0 0 26 Jun 2024
Universal Exact Compression of Differentially Private Mechanisms Yanxiao Liu Wei-Ning Chen Ayfer Özgür Cheuk Ting Li 33 2 0 28 May 2024
Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions T.-H. Hubert Chan Hao Xie Mengshi Zhao 32 1 0 14 Dec 2023
DP-NMT: Scalable Differentially-Private Machine Translation Timour Igamberdiev Doan Nam Long Vu Felix Künnecke Zhuo Yu Jannik Holmer Ivan Habernal 29 7 0 24 Nov 2023
Differentially Private Aggregation via Imperfect Shuffling Badih Ghazi Ravi Kumar Pasin Manurangsi Jelani Nelson Samson Zhou FedML 30 1 0 28 Aug 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 30 13 0 27 Jul 2023
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 28 2 0 28 May 2023
Amplification by Shuffling without Shuffling Borja Balle James Bell Adria Gascon FedML 32 2 0 18 May 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 23 9 0 11 Apr 2023
Label Inference Attack against Split Learning under Regression Setting Shangyu Xie Xin Yang Yuanshun Yao Tianyi Liu Taiqing Wang Jiankai Sun FedML 16 9 0 18 Jan 2023
Differentially Private Federated Clustering over Non-IID Data Yiwei Li Shuai Wang Chong-Yung Chi Tony Q. S. Quek FedML 22 12 0 03 Jan 2023
Stateful Switch: Optimized Time Series Release with Local Differential Privacy Qingqing Ye Haibo Hu Kai Huang M. Au Qiao Xue 26 10 0 17 Dec 2022
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization Ashwinee Panda Xinyu Tang Saeed Mahloujifar Vikash Sehwag Prateek Mittal 31 11 0 08 Dec 2022
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy Ergute Bao Yizheng Zhu X. Xiao Y. Yang Beng Chin Ooi B. Tan Khin Mi Mi Aung FedML 25 18 0 08 Dec 2022
Straggler-Resilient Differentially-Private Decentralized Learning Yauhen Yakimenka Chung-Wei Weng Hsuan-Yin Lin E. Rosnes J. Kliewer 21 6 0 06 Dec 2022
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 46 7 0 24 Nov 2022
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning Christopher A. Choquette-Choo H. B. McMahan Keith Rush Abhradeep Thakurta 24 42 0 12 Nov 2022
An Easy-to-use and Robust Approach for the Differentially Private De-Identification of Clinical Textual Documents Yakini Tchouka Jean-François Couchot David Laiymani OOD 26 1 0 02 Nov 2022
Contraction of Locally Differentially Private Mechanisms S. Asoodeh Huanyu Zhang 10 10 0 24 Oct 2022
Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search Yannis Cattan Christopher A. Choquette-Choo Nicolas Papernot Abhradeep Thakurta 13 20 0 05 Oct 2022
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 56 49 0 02 Oct 2022
Private Read Update Write (PRUW) in Federated Submodel Learning (FSL): Communication Efficient Schemes With and Without Sparsification Sajani Vithana S. Ulukus FedML 13 19 0 09 Sep 2022
Algorithms with More Granular Differential Privacy Guarantees Badih Ghazi Ravi Kumar Pasin Manurangsi Thomas Steinke 52 6 0 08 Sep 2022
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy Vitaly Feldman Audra McMillan Kunal Talwar FedML 18 47 0 09 Aug 2022
Differentially Private Linear Bandits with Partial Distributed Feedback Fengjiao Li Xingyu Zhou Bo Ji FedML 20 13 0 12 Jul 2022
Differentially Private Stochastic Linear Bandits: (Almost) for Free Osama A. Hanna Antonious M. Girgis Christina Fragouli Suhas Diggavi FedML 21 18 0 07 Jul 2022
DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware H. Hashemi Yongqin Wang M. Annavaram FedML 18 58 0 30 Jun 2022
"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning Marc Juárez Aleksandra Korolova FedML 32 9 0 24 Jun 2022
On Privacy and Personalization in Cross-Silo Federated Learning Ziyu Liu Shengyuan Hu Zhiwei Steven Wu Virginia Smith FedML 20 51 0 16 Jun 2022
Distributed Differential Privacy in Multi-Armed Bandits Sayak Ray Chowdhury Xingyu Zhou 25 12 0 12 Jun 2022
Impact of Sampling on Locally Differentially Private Data Collection Sayan Biswas Graham Cormode Carsten Maple FedML 22 0 0 02 Jun 2022
Private Federated Submodel Learning with Sparsification Sajani Vithana S. Ulukus FedML 24 10 0 31 May 2022
Improved Utility Analysis of Private CountSketch Rasmus Pagh M. Thorup FedML 34 20 0 17 May 2022
Optimal Algorithms for Mean Estimation under Local Differential Privacy Hilal Asi Vitaly Feldman Kunal Talwar 22 41 0 05 May 2022
Label Leakage and Protection from Forward Embedding in Vertical Federated Learning Jiankai Sun Xin Yang Yuanshun Yao Chong-Jun Wang FedML 31 37 0 02 Mar 2022
Differentially Private Speaker Anonymization Ali Shahin Shamsabadi B. M. L. Srivastava A. Bellet Nathalie Vauquier Emmanuel Vincent Mohamed Maouche Marc Tommasi Nicolas Papernot MIACV 38 32 0 23 Feb 2022
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation Kunal Talwar FedML 23 10 0 22 Feb 2022
Toward Training at ImageNet Scale with Differential Privacy Alexey Kurakin Shuang Song Steve Chien Roxana Geambasu Andreas Terzis Abhradeep Thakurta 25 99 0 28 Jan 2022
Privacy Amplification via Shuffling for Linear Contextual Bandits Evrard Garcelon Kamalika Chaudhuri Vianney Perchet Matteo Pirotta FedML 22 18 0 11 Dec 2021
Tight Bounds for Differentially Private Anonymized Histograms Pasin Manurangsi PICV 19 6 0 05 Nov 2021
User-Level Private Learning via Correlated Sampling Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 35 13 0 21 Oct 2021
Label differential privacy via clustering Hossein Esfandiari Vahab Mirrokni Umar Syed Sergei Vassilvitskii FedML 21 26 0 05 Oct 2021