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We present Syft 0.5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems.
Privacy preserving neural network inference on encrypted data with gpus
Daniel Takabi, Robert Podschwadt, Jeff Druce, Curt Wu, and Kevin Procopio · 1911
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Privacy and freedom
Alan F Westin · 1968
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New directions in cryptography
Whitfield Diffie and Martin Hellman · 1976
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Privacy and the limits of law
Ruth Gavison · 1980
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Efficient multiparty protocols using circuit randomization
Donald Beaver · 1991
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An efficient system for non-transferable anonymous credentials with optional anonymity revocation
Jan Camenisch and Anna Lysyanskaya · 2001
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Privacy as contextual integrity
Helen Nissenbaum · 2004
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Fully homomorphic encryption using ideal lattices
Craig Gentry · 2009
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Privacy in context: Technology, policy, and the integrity of social life
Helen Nissenbaum · 2009
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Secure multiparty computation
Ronald Cramer, Ivan Bjerre Damgård, et al · 2015
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URL https://www.legislation.gov.uk/eur/2016/679/article/5
Regulation (eu) 2016/679 of the european parliament and of the council · 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
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Performance measures and a data set for multi-target, multi-camera tracking
Ergys Ristani, Francesco Solera, Roger S. Zou, R. Cucchiara, and Carlo Tomasi · 2016
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Homomorphic encryption for arithmetic of approximate numbers
Jung Hee Cheon, Andrey Kim, Miran Kim, and Yongsoo Song · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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LEAF: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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Eggroll, 2018
Max Wong, H Moster, and Lin, Bryce · 2018
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FATE Documentation, 2018
Wenbin Wei · 2018
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Introducing tensorflow federated., 2019
Alex Ingerman and Krzys Ostrowski · 2019
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Clara, 2019
Nvidia · 2019
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Paddlefl, 2019
Qinghe Jing, Dong Daxiang, and contributors · 2019
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We Research and Build Artificial Intelligence Technology and Services, 2019
Sherpa · 2019
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Reducing leakage in distributed deep learning for sensitive health data
Ibm federated learning: an enterprise framework white paper v0.1, 2020
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, Mark Purcell, Ambrish Rawat, Tran Minh, Naoise Holohan, Supriyo Chakraborty, Shalisha Whitherspoon, Dean Steuer, Laura Wynter, Hifaz Hassan, Sean Laguna, Mikhail Yurochkin, Mayank Agarwal, Ebube Chuba, and Annie Abay · 2020
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Microsoft SEAL (release 3.6)
Microsoft SEAL · 2020
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Shredder: Learning noise distributions to protect inference privacy
Fatemehsadat Mireshghallah, Mohammadkazem Taram, Prakash Ramrakhyani, Ali Jalali, Dean Tullsen, and Hadi Esmaeilzadeh · 2020
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Facial recognition datasets are being widely used despite being taken down due to ethical concerns. here’s how., 2020
Kenny Peng · 2020
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Towards general-purpose infrastructure for protecting scientific data under study, 2020
Andrew Trask and Kritika Prakash · 2020
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Praneeth Vepakomma, Otkrist Gupta, Abhimanyu Dubey, and Ramesh Raskar · 2019
Cited alongside, same era.
THE MNIST DATABASE of handwritten digits, 2019
Yann LeCun, Corinna Cortes, Christopher J.C. Burges · 2019
Cited alongside, same era.
A distributed trust framework for privacy-preserving machine learning
Will Abramson, Adam James Hall, Pavlos Papadopoulos, Nikolaos Pitropakis, and William J. Buchanan · 2020
Cited alongside, same era.
Asymmetric private set intersection with applications to contact tracing and private vertical federated machine learning, 2020
Nick Angelou, Ayoub Benaissa, Bogdan Cebere, William Clark, Adam James Hall, Michael A. Hoeh, Daniel Liu, Pavlos Papadopoulos, Robin Roehm, Robert Sandmann, Phillipp Schoppmann, and Tom Titcombe · 2020
Cited alongside, same era.
Flower: A friendly federated learning research framework, 2020
Daniel J. Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D. Lane · 2020
Cited alongside, same era.
Improved techniques for model inversion attacks, 2020
Si Chen, Ruoxi Jia, and Guo-Jun Qi · 2020
Cited alongside, same era.
Beyond privacy trade-offs with structured transparency, 2020
Andrew Trask, Emma Bluemke, Ben Garfinkel, Claudia Ghezzou Cuervas-Mons, and Allan Dafoe · 2020
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Optimizing federated learning on non-iid data with reinforcement learning
H. Wang, Z. Kaplan, D. Niu, and B. Li · 2020
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The secret revealer: Generative model-inversion attacks against deep neural networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song · 2020
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Syfertext, 2021
Alan Aboudib · 2021
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Exposing.ai, 2021
Jules. Harvey, Adam. LaPlace · 2021
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PyGrid, 2021
Ionesio Junior, Patrick Cason · 2021
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Crypten: Secure multi-party computation meets machine learning, 2021
Brian Knott, Shobha Venkataraman, Shubho Sengupta Awni Hannun, Mark Ibrahim, and Laurens van der Maaten · 2021
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Cybersecurity: Risk management framework and investment cost analysis
In Lee · 2021
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TenSEAL (release 0.3.0)
TenSEAL · 2021
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PyDentity, 2021
Will Abramson, Adam Hall, Lohan Spies, Tom Titcombe · 2021
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