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Differentially Private Federated Learning (DP-FL) has garnered attention as a collaborative machine learning approach that ensures formal privacy.
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Efficient and secure comparison for on-line auctions. In Information Security and Privacy: 12th Australasian Conference, ACISP 2007, Townsville, Australia, July 2-4, 2007. Proceedings 12 . Springer, 416–430
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Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data . 19–30
Frank D McSherry. 2009 · 2009
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security . 308–318
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Federated learning of deep networks using model averaging
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Practical secure aggregation for privacy-preserving machine learning. In proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . 1175–1191
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Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017 · 2017
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The EU General Data Protection Regulation (GDPR): European regulation that has a global impact
Michelle Goddard. 2017 · 2017
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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2017 · 2017
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Rényi differential privacy. In 2017 IEEE 30th computer security foundations symposium (CSF) . IEEE, 263–275
Ilya Mironov. 2017 · 2017
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Privacy-preserving record linkage for big data: Current approaches and research challenges
Dinusha Vatsalan, Ziad Sehili, Peter Christen, and Erhard Rahm. 2017 · 2017
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A general approach to adding differential privacy to iterative training procedures
H Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, and Peter Kairouz. 2018 · 2018
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Bounding user contributions: A bias-variance trade-off in differential privacy. In International Conference on Machine Learning . PMLR, 263–271
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Capacity Bounded Differential Privacy
Kamalika Chaudhuri, Jacob Imola, and Ashwin Machanavajjhala. 2019 · 2019
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Distributed differential privacy via shuffling. In Advances in Cryptology–EUROCRYPT 2019: 38th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Darmstadt, Germany, May 19–23, 2019, Proceedings, Part I 38 . Springer, 375–403
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev. 2019 · 2019
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R \ \backslash ’enyi differential privacy of the sampled gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang. 2019 · 2019
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Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. In 2019 IEEE symposium on security and privacy (SP) . IEEE, 739–753
Milad Nasr, Reza Shokri, and Amir Houmansadr. 2019 · 2019
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Federated learning for emoji prediction in a mobile keyboard
Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Françoise Beaufays. 2019 · 2019
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Subsampled rényi differential privacy and analytical moments accountant. In The 22nd International Conference on Artificial Intelligence and Statistics . PMLR, 1226–1235
Yu-Xiang Wang, Borja Balle, and Shiva Prasad Kasiviswanathan. 2019 · 2019
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Hypothesis testing interpretations and renyi differential privacy. In International Conference on Artificial Intelligence and Statistics . PMLR, 2496–2506
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, and Tetsuya Sato. 2020 · 2020
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Projected federated averaging with heterogeneous differential privacy
Junxu Liu, Jian Lou, Li Xiong, Jinfei Liu, and Xiaofeng Meng. 2021 · 2021
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PPFL: privacy-preserving federated learning with trusted execution environments. In Proceedings of the 19th annual international conference on mobile systems, applications, and services . 94–108
Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, and Nicolas Kourtellis. 2021 · 2021
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Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications
Matthias Paulik, Matt Seigel, Henry Mason, Dominic Telaar, Joris Kluivers, Rogier van Dalen, Chi Wai Lau, Luke Carlson, Filip Granqvist, Chris Vandevelde, et al · 2021
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Adaptive Federated Optimization
Sashank Reddi, Zachary Burr Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and Brendan McMahan (Eds.). 2021 · 2021
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User-level privacy-preserving federated learning: Analysis and performance optimization
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Secure single-server aggregation with (poly) logarithmic overhead. In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security . 1253–1269
James Henry Bell, Kallista A Bonawitz, Adrià Gascón, Tancrède Lepoint, and Mariana Raykova. 2020 · 2020
Cited alongside, same era.
Smoothly bounding user contributions in differential privacy
Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, and Lijie Ren. 2020 · 2020
Cited alongside, same era.
Encode, shuffle, analyze privacy revisited: Formalizations and empirical evaluation
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, and Abhradeep Thakurta. 2020 · 2020
Cited alongside, same era.
An efficient framework for clustered federated learning
Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran. 2020 · 2020
Cited alongside, same era.
Learning discrete distributions: user vs item-level privacy
Yuhan Liu, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Michael Riley. 2020 · 2020
Cited alongside, same era.
LDP-Fed: Federated learning with local differential privacy. In Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking . 61–66
Stacey Truex, Ling Liu, Ka-Ho Chow, Mehmet Emre Gursoy, and Wenqi Wei. 2020 · 2020
Cited alongside, same era.
Differentially private SQL with bounded user contribution
Royce J Wilson, Celia Yuxin Zhang, William Lam, Damien Desfontaines, Daniel Simmons-Marengo, and Bryant Gipson. 2020 · 2020
Cited alongside, same era.
FPGA-based hardware accelerator of homomorphic encryption for efficient federated learning
Zhaoxiong Yang, Shuihai Hu, and Kai Chen. 2020 · 2020
Cited alongside, same era.
Kang Wei, Jun Li, Ming Ding, Chuan Ma, Hang Su, Bo Zhang, and H Vincent Poor. 2021 · 2021
Later among the works it cites.
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
Haibo Yang, Minghong Fang, and Jia Liu. 2021 · 2021
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Opacus: User-friendly differential privacy library in PyTorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, et al · 2021
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Network shuffling: Privacy amplification via random walks. In Proceedings of the 2022 International Conference on Management of Data . 773–787
Seng Pei Liew, Tsubasa Takahashi, Shun Takagi, Fumiyuki Kato, Yang Cao, and Masatoshi Yoshikawa. 2022 · 2022
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On privacy and personalization in cross-silo federated learning
Ken Liu, Shengyuan Hu, Steven Z Wu, and Virginia Smith. 2022 · 2022
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FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. In Advances in Neural Information Processing Systems , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 5315–5334
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Teleńczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, and Mathieu Andreux. 2022 · 2022
Later among the works it cites.
Safeguarding cross-silo federated learning with local differential privacy
Chen Wang, Xinkui Wu, Gaoyang Liu, Tianping Deng, Kai Peng, and Shaohua Wan. 2022 · 2022
Later among the works it cites.
Understanding clipping for federated learning: Convergence and client-level differential privacy. In International Conference on Machine Learning, ICML 2022
Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, and Jinfeng Yi. 2022 · 2022
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Credit Card Fraud Detection dataset
Kaggle. 2018 · 2023
Closest in time.
CS 860 : Algorithms for Private Data Analysis Fall 2020 Lecture 5 — Approximate Differential Privacy
Gautam Kamath. 2020 · 2023
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Olive: Oblivious Federated Learning on Trusted Execution Environment against the Risk of Sparsification
Fumiyuki Kato, Yang Cao, and Masatoshi Yoshikawa. 2023 · 2023
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Private non-convex federated learning without a trusted server. In International Conference on Artificial Intelligence and Statistics . PMLR, 5749–5786
Andrew Lowy, Ali Ghafelebashi, and Meisam Razaviyayn. 2023 · 2023
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Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. In The Eleventh International Conference on Learning Representations
Andrew Lowy and Meisam Razaviyayn. 2023 · 2023
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Securing secure aggregation: Mitigating multi-round privacy leakage in federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 37. 9864–9873
Jinhyun So, Ramy E Ali, Başak Güler, Jiantao Jiao, and A Salman Avestimehr. 2023 · 2023
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From Bounded to Unbounded: Privacy Amplification via Shuffling with Dummies. In 2023 IEEE 36th Computer Security Foundations Symposium (CSF) . IEEE, 457–472
Shun Takagi, Fumiyuki Kato, Yang Cao, and Masatoshi Yoshikawa. 2023 · 2023
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