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We introduce PrivPy, a practical privacy-preserving collaborative computation framework, especially optimized for machine learning tasks.
How to share a secret
1979
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Introduction to numerical analysis
1980
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How to exchange secrets by oblivious transfer
1981
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Protocols for secure computations
1982
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Historical development of the newton-raphson method
1995
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Handwritten digits recognition
2000
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Pipeline vectorization
2001
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Understanding source code evolution using abstract syntax tree matching
2005
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Privacy preserving data mining research: Current status and key issues
2007
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FairplayMP: a system for secure multi-party computation
2008
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Sharemind: A framework for fast privacy-preserving computations
2008
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Improved garbled circuit building blocks and applications to auctions and computing minima
2009
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Improved primitives for secure multiparty integer computation
2010
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Secure computation with fixed-point numbers
2010
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P4P: Practical Large-scale Privacy-preserving Distributed Computation Robust Against Malicious Users
2010
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Tasty: tool for automating secure two-party computations
2010
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The mnist database of handwritten digits
2010
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numpy.ndarray
2011
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numpy.ndarray
2011
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Scikit-learn: Machine learning in Python
2011
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L1-an intermediate language for mixed-protocol secure computation
2011
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The numpy array: A structure for efficient numerical computation
2011
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Multiparty computation from somewhat homomorphic encryption
2012
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Billion-gate secure computation with malicious adversaries
2012
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
2015
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Secure floating point arithmetic and private satellite collision analysis
2015
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Lenet-5, convolutional neural networks
2015
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Oblivm: A programming framework for secure computation
2015
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Fast and secure three-party computation:the garbled circuit approach
2015
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Combining differential privacy and secure multiparty computation
2015
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Practical covertly secure mpc for dishonest majority–or: breaking the spdz limits
2013
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Picco: a general-purpose compiler for private distributed computation
2013
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Domain-polymorphic programming of privacy-preserving applications
2014
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Algorithms in helib
2014
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Hybrid model of fixed and floating point numbers in secure multiparty computations
2014
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2015
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High-throughput semi-honest secure three-party computation with an honest majority
2016
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The movielens datasets: History and context
2016
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Using fully homomorphic encryption for statistical analysis of categorical, ordinal and numerical data
2016
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emp-sh2pc
2016
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Pem: Practical differentially private system for large-scale cross-institutional data mining
2017
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Oblivious neural network predictions via minionn transformations
2017
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Secureml: A system for scalable privacy-preserving machine learning
2017
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Generalizing the spdz compiler for other protocols
2018
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Aby 3: a mixed protocol framework for machine learning
2018
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