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Machine learning benefits from large training datasets, which may not always be possible to collect by any single entity, especially when using privacy-sensitive data.
A mathematical theory of communication
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Adi Shamir · 1979
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Andrew Chi-Chih Yao · 1986
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Active hidden markov models for information extraction
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Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
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Privacy-preserving learning via deep net pruning
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Extending oblivious transfers efficiently
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Fairplay—a secure two-party computation system
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Calibrating noise to sensitivity in private data analysis
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A fully homomorphic encryption scheme , volume 20
Craig Gentry · 2009
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Active learning literature survey
Burr Settles · 2009
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Texthide: Tackling data privacy in language understanding tasks
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The eu proposal for a general data protection regulation and the roots of the ‘right to be forgotten’
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(leveled) fully homomorphic encryption without bootstrapping
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Membership inference attacks against machine learning models
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Gazelle: A low latency framework for secure neural network inference
Chiraag Juvekar, Vinod Vaikuntanathan, and Anantha Chandrakasan · 2018
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Deep learning with differential privacy
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Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
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Equality of opportunity in supervised learning
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Federated learning: Strategies for improving communication efficiency
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EMP-toolkit: Efficient MultiParty computation toolkit
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Ngraph-he: A graph compiler for deep learning on homomorphically encrypted data
Fabian Boemer, Yixing Lao, Rosario Cammarota, and Casimir Wierzynski · 2019
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The secret sharer: Evaluating and testing unintended memorization in neural networks
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he-transformer
Fabian Boemer · 2020
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MP2ML: a mixed-protocol machine learning framework for private inference
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sar (sysstat)
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Delphi: A cryptographic inference service for neural networks
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Psi from paxos: Fast, malicious private set intersection
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Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Micah J. Sheller, Brandon Edwards, G. Anthony Reina, Jason Martin, Sarthak Pati, Aikaterini Kotrotsou, Mikhail Milchenko, Weilin Xu, Daniel Marcus, Rivka R. Colen, and Spyridon Bakas · 2020
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