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Machine learning (ML) is increasingly being adopted in a wide variety of application domains.
Untraceable electronic mail, return addresses, and digital pseudonyms
David L Chaum · 1981
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Protocols for secure computations
Andrew C Yao · 1982
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The dining cryptographers problem: Unconditional sender and recipient untraceability
David Chaum · 1988
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Public-key cryptosystems based on composite degree residuosity classes
Pascal Paillier · 1999
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Privacy-preserving data mining
Rakesh Agrawal and Ramakrishnan Srikant · 2000
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Privacy preserving data mining
Yehuda Lindell and Benny Pinkas · 2000
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k-anonymity: A model for protecting privacy
Latanya Sweeney · 2002
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Privacy preserving regression modelling via distributed computation
Ashish P Sanil, Alan F Karr, Xiaodong Lin, and Jerome P Reiter · 2004
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Random-data perturbation techniques and privacy-preserving data mining
Hillol Kargupta, Souptik Datta, Qi Wang, and Krishnamoorthy Sivakumar · 2005
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Can machine learning be secure?
Marco Barreno, Blaine Nelson, Russell Sears, Anthony D Joseph, and J Doug Tygar · 2006
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Workload-aware anonymization
Kristen LeFevre, David J DeWitt, and Raghu Ramakrishnan · 2006
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Injecting utility into anonymized datasets
Daniel Kifer and Johannes Gehrke · 2006
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l-diversity: Privacy beyond k-anonymity
Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, and Muthuramakrishnan Venkitasubramaniam · 2007
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t-closeness: Privacy beyond k-anonymity and l-diversity
Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian · 2007
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On privacy-preservation of text and sparse binary data with sketches
Charu C Aggarwal and Philip S Yu · 2007
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Secure two-party k-means clustering
Paul Bunn and Rafail Ostrovsky · 2007
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Providing k-anonymity in data mining
Arik Friedman, Ran Wolff, and Assaf Schuster · 2008
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Understanding privacy
Daniel J Solove · 2008
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Distributed privacy preserving k-means clustering with additive secret sharing
Mahir Can Doganay, Thomas B Pedersen, Yücel Saygin, Erkay Savaş, and Albert Levi · 2008
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Fully homomorphic encryption using ideal lattices
Craig Gentry · 2009
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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Secure two-party computation is practical
Benny Pinkas, Thomas Schneider, Nigel P Smart, and Stephen C Williams · 2009
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The security of machine learning
Marco Barreno, Blaine Nelson, Anthony D Joseph, and J Doug Tygar · 2010
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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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A practical attack to de-anonymize social network users
Gilbert Wondracek, Thorsten Holz, Engin Kirda, and Christopher Kruegel · 2010
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Fully homomorphic encryption over the integers
Marten Van Dijk, Craig Gentry, Shai Halevi, and Vinod Vaikuntanathan · 2010
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Functional encryption for inner product: Achieving constant-size ciphertexts with adaptive security or support for negation
Nuttapong Attrapadung and Benoît Libert · 2010
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Tasty: tool for automating secure two-party computations
Wilko Henecka, Stefan K ögl, Ahmad-Reza Sadeghi, Thomas Schneider, and Immo Wehrenberg · 2010
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Functional encryption: Definitions and challenges
Dan Boneh, Amit Sahai, and Brent Waters · 2011
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Publishing set-valued data via differential privacy
Rui Chen, Noman Mohammed, Benjamin CM Fung, Bipin C Desai, and Li Xiong · 2011
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Secure multiple linear regression based on homomorphic encryption
Rob Hall, Stephen E Fienberg, and Yuval Nardi · 2011
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Functional encryption for inner product predicates from learning with errors
Shweta Agrawal, David Mandell Freeman, and Vinod Vaikuntanathan · 2011
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Homomorphic evaluation of the aes circuit
Craig Gentry, Shai Halevi, and Nigel P Smart · 2012
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On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption
Adriana López-Alt, Eran Tromer, and Vinod Vaikuntanathan · 2012
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Multiparty computation from somewhat homomorphic encryption
Ivan Damgård, Valerio Pastro, Nigel Smart, and Sarah Zakarias · 2012
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Publishing trajectories with differential privacy guarantees
Kaifeng Jiang, Dongxu Shao, Stéphane Bressan, Thomas Kister, and Kian-Lee Tan · 2013
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Privacy-preserving ridge regression on hundreds of millions of records
Valeria Nikolaenko, Udi Weinsberg, Stratis Ioannidis, Marc Joye, Dan Boneh, and Nina Taft · 2013
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On the relationship between functional encryption, obfuscation, and fully homomorphic encryption
Joël Alwen, Manuel Barbosa, Pooya Farshim, Rosario Gennaro, S Dov Gordon, Stefano Tessaro, and David A Wilson · 2013
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Innovative instructions and software model for isolated execution
Frank McKeen, Ilya Alexandrovich, Alex Berenzon, Carlos V Rozas, Hisham Shafi, Vedvyas Shanbhogue, and Uday R Savagaonkar · 2013
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Path oram: an extremely simple oblivious ram protocol
Emil Stefanov, Marten Van Dijk, Elaine Shi, Christopher Fletcher, Ling Ren, Xiangyao Yu, and Srinivas Devadas · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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(leveled) fully homomorphic encryption without bootstrapping
Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan · 2014
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Multi-input functional encryption
Shafi Goldwasser, S Dov Gordon, Vipul Goyal, Abhishek Jain, Jonathan Katz, Feng-Hao Liu, Amit Sahai, Elaine Shi, and Hong-Sheng Zhou · 2014
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Algorithms in helib
Shai Halevi and Victor Shoup · 2014
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Fully homomorphic simd operations
Nigel P Smart and Frederik Vercauteren · 2014
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Oblivious data structures
Xiao Shaun Wang, Kartik Nayak, Chang Liu, TH Hubert Chan, Elaine Shi, Emil Stefanov, and Yan Huang · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Simple functional encryption schemes for inner products
Michel Abdalla, Florian Bourse, Angelo De Caro, and David Pointcheval · 2015
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Fully homomorphic encryption without bootstrapping
Masahiro Yagisawa · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Efficient private statistics with succinct sketches
Luca Melis, George Danezis, and Emiliano De Cristofaro · 2015
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Fast and secure three-party computation: The garbled circuit approach
Payman Mohassel, Mike Rosulek, and Ye Zhang · 2015
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Secure multiparty computation
Ronald Cramer, Ivan Bjerre Damgård, and Jesper Buus Nielsen · 2015
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Aby-a framework for efficient mixed-protocol secure two-party computation
Daniel Demmler, Thomas Schneider, and Michael Zohner · 2015
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Fast, privacy preserving linear regression over distributed datasets based on pre-distributed data
Martine de Cock, Rafael Dowsley, Anderson CA Nascimento, and Stacey C Newman · 2015
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Semantically secure order-revealing encryption: Multi-input functional encryption without obfuscation
Dan Boneh, Kevin Lewi, Mariana Raykova, Amit Sahai, Mark Zhandry, and Joe Zimmerman · 2015
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A punctured programming approach to adaptively secure functional encryption
Brent Waters · 2015
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Function-hiding inner product encryption
Allison Bishop, Abhishek Jain, and Lucas Kowalczyk · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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Regression model fitting under differential privacy and model inversion attack
Yue Wang, Cheng Si, and Xintao Wu · 2015
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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A methodology for formalizing model-inversion attacks
Xi Wu, Matthew Fredrikson, Somesh Jha, and Jeffrey F Naughton · 2016
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
De-anonymizing social networks and inferring private attributes using knowledge graphs
Jianwei Qian, Xiang-Yang Li, Chunhong Zhang, and Linlin Chen · 2016
Cited alongside, same era.
Secure linear regression on vertically partitioned datasets
Adrià Gascón, Phillipp Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahur, and David Evans · 2016
Cited alongside, same era.
Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing · 2016
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
Cited alongside, same era.
The tradeoff between privacy and accuracy in anomaly detection using federated xgboost
Mengwei Yang, Linqi Song, Jie Xu, Congduan Li, and Guozhen Tan · 2019
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Privacy for free: Communication-efficient learning with differential privacy using sketches
Tian Li, Zaoxing Liu, Vyas Sekar, and Virginia Smith · 2019
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Differentially private meta-learning
Jeffrey Li, Mikhail Khodak, Sebastian Caldas, and Ameet Talwalkar · 2019
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A hybrid approach to privacy-preserving federated learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou · 2019
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Cryptonn:training neural networks over encrypted data
Runhua Xu, James Joshi, and Chao Li · 2019
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Cited alongside, same era.
Cryptoml: Secure outsourcing of big data machine learning applications
Azalia Mirhoseini, Ahmad-Reza Sadeghi, and Farinaz Koushanfar · 2016
Cited alongside, same era.
Differentially private matrix factorization using sketching techniques
Raghavendran Balu and Teddy Furon · 2016
Cited alongside, same era.
Optimizing semi-honest secure multiparty computation for the internet
Aner Ben-Efraim, Yehuda Lindell, and Eran Omri · 2016
Cited alongside, same era.
Candidate indistinguishability obfuscation and functional encryption for all circuits
Sanjam Garg, Craig Gentry, Shai Halevi, Mariana Raykova, Amit Sahai, and Brent Waters · 2016
Cited alongside, same era.
5gen: A framework for prototyping applications using multilinear maps and matrix branching programs
Kevin Lewi, Alex J Malozemoff, Daniel Apon, Brent Carmer, Adam Foltzer, Daniel Wagner, David W Archer, Dan Boneh, Jonathan Katz, and Mariana Raykova · 2016
Cited alongside, same era.
Amd memory encryption
David Kaplan, Jeremy Powell, and Tom Woller · 2016
Cited alongside, same era.
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Secureboost: A lossless federated learning framework
Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, and Qiang Yang · 2019
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Towards deep neural network training on encrypted data
Karthik Nandakumar, Nalini Ratha, Sharath Pankanti, and Shai Halevi · 2019
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Optimal bounded-collusion secure functional encryption
Prabhanjan Ananth and Vinod Vaikuntanathan · 2019
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From single-input to multi-client inner-product functional encryption
Michel Abdalla, Fabrice Benhamouda, and Romain Gay · 2019
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Decentralizing inner-product functional encryption
Michel Abdalla, Fabrice Benhamouda, Markulf Kohlweiss, and Hendrik Waldner · 2019
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Private model compression via knowledge distillation
Ji Wang, Weidong Bao, Lichao Sun, Xiaomin Zhu, Bokai Cao, and S Yu Philip · 2019
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Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela van der Schaar · 2019
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Sok: General purpose compilers for secure multi-party computation
Marcella Hastings, Brett Hemenway, Daniel Noble, and Steve Zdancewic · 2019
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Ezpc: Programmable and efficient secure two-party computation for machine learning
Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, and Shardul Tripathi · 2019
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Quotient: two-party secure neural network training and prediction
Nitin Agrawal, Ali Shahin Shamsabadi, Matt J Kusner, and Adrià Gascón · 2019
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Glyph: Fast and accurately training deep neural networks on encrypted data
Qian Lou, Bo Feng, Geoffrey C Fox, and Lei Jiang · 2019
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Helen: Maliciously secure coopetitive learning for linear models
Wenting Zheng, Raluca Ada Popa, Joseph E Gonzalez, and Ion Stoica · 2019
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Efficient multi-key homomorphic encryption with packed ciphertexts with application to oblivious neural network inference
Hao Chen, Wei Dai, Miran Kim, and Yongsoo Song · 2019
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Deep neural networks classification over encrypted data
Ehsan Hesamifard, Hassan Takabi, and Mehdi Ghasemi · 2019
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Partially encrypted machine learning using functional encryption
Théo Ryffel, Edouard Dufour Sans, Romain Gay, Francis Bach, and David Pointcheval · 2019
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Privpy: General and scalable privacy-preserving data mining
Yi Li and Wei Xu · 2019
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Enhancing the privacy of federated learning with sketching
Zaoxing Liu, Tian Li, Virginia Smith, and Vyas Sekar · 2019
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Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
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Differentially private model publishing for deep learning
Lei Yu, Ling Liu, Calton Pu, Mehmet Emre Gursoy, and Stacey Truex · 2019
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On the compatibility of privacy and fairness
Rachel Cummings, Varun Gupta, Dhamma Kimpara, and Jamie Morgenstern · 2019
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Data poisoning against differentially-private learners: Attacks and defenses
Yuzhe Ma, Xiaojin Zhu, and Justin Hsu · 2019
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Membership inference attacks and defenses in supervised learning via generalization gap
Jiacheng Li, Ninghui Li, and Bruno Ribeiro · 2020
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idlg: Improved deep leakage from gradients
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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Threats to federated learning: A survey
Lingjuan Lyu, Han Yu, and Qiang Yang · 2020
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Fedsketch: Communication-efficient and private federated learning via sketching
Farzin Haddadpour, Belhal Karimi, Ping Li, and Xiaoyun Li · 2020
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Revisiting model-agnostic private learning: Faster rates and active learning
Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, and Yu-Xiang Wang · 2020
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Adaptive histogram-based gradient boosted trees for federated learning
Yuya Jeremy Ong, Yi Zhou, Nathalie Baracaldo, and Heiko Ludwig · 2020
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A survey of differentially private generative adversarial networks
Liyue Fan · 2020
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Fastsecagg: Scalable secure aggregation for privacy-preserving federated learning
Swanand Kadhe, Nived Rajaraman, O Ozan Koyluoglu, and Kannan Ramchandran · 2020
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Microsoft SEAL (release 3.5), 2020
Redmond Microsoft Research · 2020
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Dynamic decentralized functional encryption
Jérémy Chotard, Edouard Dufour-Sans, Romain Gay, Duong Hieu Phan, and David Pointcheval · 2020
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Revisiting secure computation using functional encryption: Opportunities and research directions
Runhua Xu and James Joshi · 2020
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Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning
Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu · 2020
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Crypten: Secure multi-party computation meets machine learning
Brian Knott, Shobha Venkataraman, Awni Hannun, Shubho Sengupta, Mark Ibrahim, and Laurens van der Maaten · 2020
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Falcon: Honest-majority maliciously secure framework for private deep learning
Sameer Wagh, Shruti Tople, Fabrice Benhamouda, Eyal Kushilevitz, Prateek Mittal, and Tal Rabin · 2020
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Mitigating leakage in federated learning with trusted hardware
Javad Ghareh Chamani and Dimitrios Papadopoulos · 2020
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Secure collaborative training and inference for xgboost
Andrew Law, Chester Leung, Rishabh Poddar, Raluca Ada Popa, Chenyu Shi, Octavian Sima, Chaofan Yu, Xingmeng Zhang, and Wenting Zheng · 2020
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
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Privacy-preserving learning via deep net pruning
Yangsibo Huang, Yushan Su, Sachin Ravi, Zhao Song, Sanjeev Arora, and Kai Li · 2020
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Deep leakage from gradients
Ligeng Zhu and Song Han · 2020
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Inverting gradients–how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller · 2020
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Mohammad Naseri, Jamie Hayes, and Emiliano De Cristofaro · 2020
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Auditing differentially private machine learning: How private is private sgd?
Matthew Jagielski, Jonathan Ullman, and Alina Oprea · 2020
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Mathias PM Parisot, Balazs Pejo, and Dayana Spagnuelo · 2021
Closest in time.
A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions
Xuefei Yin, Yanming Zhu, and Jiankun Hu · 2021
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Nn-emd: Efficiently training neural networks using encrypted multi-sourced datasets
Runhua Xu, James Joshi, and Chao Li · 2021
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Sirnn: A math library for secure rnn inference
Deevashwer Rathee, Mayank Rathee, Rahul Kranti Kiran Goli, Divya Gupta, Rahul Sharma, Nishanth Chandran, and Aseem Rastogi · 2021
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Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning
Jinhyun So, Başak Güler, and A Salman Avestimehr · 2021
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Encrypted distributed lasso for sparse data predictive control
Andreea B Alexandru, Anastasios Tsiamis, and George J Pappas · 2021
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Ppca: Privacy-preserving principal component analysis using secure multiparty computation (mpc)
Xiaoyu Fan, Guosai Wang, Kun Chen, Xu He, and Wei Xu · 2021
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Cryptgpu: Fast privacy-preserving machine learning on the gpu
Sijun Tan, Brian Knott, Yuan Tian, and David J Wu · 2021
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Separation of powers in federated learning
Pau-Chen Cheng, Kevin Eykholt, Zhongshu Gu, Hani Jamjoom, KR Jayaram, Enriquillo Valdez, and Ashish Verma · 2021
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Citadel: Protecting data privacy and model confidentiality for collaborative learning with sgx
Chengliang Zhang, Junzhe Xia, Baichen Yang, Huancheng Puyang, Wei Wang, Ruichuan Chen, Istemi Ekin Akkus, Paarijaat Aditya, and Feng Yan · 2021
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Ppfl: Privacy-preserving federated learning with trusted execution environments
Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, and Nicolas Kourtellis · 2021
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Datalens: Scalable privacy preserving training via gradient compression and aggregation
Boxin Wang, Fan Wu, Yunhui Long, Luka Rimanic, Ce Zhang, and Bo Li · 2021
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Privacy-preserving federated learning based on multi-key homomorphic encryption
Jing Ma, Si-Ahmed Naas, Stephan Sigg, and Xixiang Lyu · 2021
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Dp-cryptography: marrying differential privacy and cryptography in emerging applications
Sameer Wagh, Xi He, Ashwin Machanavajjhala, and Prateek Mittal · 2021
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Byzantine-robust and privacy-preserving framework for fedml
Hanieh Hashemi, Yongqin Wang, Chuan Guo, and Murali Annavaram · 2021
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Shufflefl: gradient-preserving federated learning using trusted execution environment
Yuhui Zhang, Zhiwei Wang, Jiangfeng Cao, Rui Hou, and Dan Meng · 2021
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