Fetching the paper…
Reading the bibliography…
AI algorithms, and machine learning (ML) techniques in particular, are increasingly important to individuals' lives, but have caused a range of privacy concerns addressed by, e.g., the European GDPR.
Leslie G Valiant, ‘Universal circuits (preliminary report)’, in STOC
1976
Earlier work this paper cites.
Andrew Yao, ‘How to generate and exchange secrets’, in FOCS
1986
Earlier work this paper cites.
Oded Goldreich, Silvio Micali, and Avi Wigderson, ‘How to play any mental game’, in STOC
1987
Earlier work this paper cites.
MNIST Dataset
Yann LeCun, Corinna Cortes, and Christopher Burges · 1998
Earlier work this paper cites.
2002
Earlier work this paper cites.
Yuval Ishai, Joe Kilian, Kobbi Nissim, and Erez Petrank, ‘Extending oblivious transfers efficiently’, in CRYPTO
2003
Earlier work this paper cites.
Justin Brickell, Donald E Porter, Vitaly Shmatikov, and Emmett Witchel, ‘Privacy-preserving remote diagnostics’, in ACM Conference on Computer and Communications Security (CCS)
2007
Earlier work this paper cites.
Claudio Orlandi, Alessandro Piva, and Mauro Barni, ‘Oblivious neural network computing via homomorphic encryption’, EURASIP Journal on Information Security
2007
Earlier work this paper cites.
Vladimir Kolesnikov and Thomas Schneider, ‘Improved garbled circuit: Free XOR gates and applications’, in International Colloquium on Automata, Languages, and Programming (ICALP)
2008
Earlier work this paper cites.
Vladimir Kolesnikov and Thomas Schneider, ‘A practical universal circuit construction and secure evaluation of private functions’, in Financial Cryptography and Data Security (FC)
2008
Earlier work this paper cites.
Ahmad-Reza Sadeghi and Thomas Schneider, ‘Generalized universal circuits for secure evaluation of private functions with application to data classification’, in International Conference on Information Security and Cryptology (ICISC)
2008
Earlier work this paper cites.
Daphne Koller and Nir Friedman, Probabilistic Graphical Models: Principles and Techniques
2009
Earlier work this paper cites.
Yehuda Lindell and Benny Pinkas, ‘A proof of security of Yao’s protocol for two-party computation’, Journal of Cryptology
2009
Earlier work this paper cites.
Mauro Barni, Pierluigi Failla, Riccardo Lazzeretti, Ahmad-Reza Sadeghi, and Thomas Schneider, ‘Privacy-preserving ECG classification with branching programs and neural networks’, IEEE Transactions on Information Forensics and Security (TIFS)
2011
Earlier work this paper cites.
Hoifung Poon and Pedro M Domingos, ‘Sum-product networks: A new deep architecture’, in UAI
2011
Earlier work this paper cites.
Robert Gens and Pedro M Domingos, ‘Learning the structure of sum-product networks’, in ICML
2013
Earlier work this paper cites.
Tahrima Rahman, Prasanna Kothalkar, and Vibhav Gogate, ‘Cutset networks: A simple, tractable, and scalable approach for improving the accuracy of Chow-Liu trees’, in ECML PKDD
2014
Cited alongside, same era.
Raphael Bost, Raluca Ada Popa, Stephen Tu, and Shafi Goldwasser, ‘Machine learning classification over encrypted data’, in Network and Distributed System Security Symposium (NDSS)
2015
Cited alongside, same era.
Daniel Demmler, Ghada Dessouky, Farinaz Koushanfar, Ahmad-Reza Sadeghi, Thomas Schneider, and Shaza Zeitouni, ‘Automated synthesis of optimized circuits for secure computation’, in ACM Conference on Computer and Communications Security (CCS)
2015
Cited alongside, same era.
Daniel Demmler, Thomas Schneider, and Michael Zohner, ‘ABY-A framework for efficient mixed-protocol secure two-party computation’, in Network and Distributed System Security Symposium (NDSS)
2015
Cited alongside, same era.
TensorFlow privacy
Galen Andrew, Steve Chien, and Nicolas Papernot · 2019
Later among the works it cites.
Fabian Boemer, Anamaria Costache, Rosario Cammarota, and Casimir Wierzynski, ‘nGraph-HE2: A high-throughput framework for neural network inference on encrypted data’, in Workshop on Encrypted Computing & Applied Homomorphic Cryptography (WAHC)
2019
Later among the works it cites.
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song, ‘The secret sharer: Evaluating and testing unintended memorization in neural networks’, in USENIX Security
2019
Later among the works it cites.
Guoxing Chen, Sanchuan Chen, Yuan Xiao, Yinqian Zhang, Zhiqiang Lin, and Ten-Hwang Lai, ‘SgxPectre: Stealing Intel secrets from SGX enclaves via speculative execution’, in IEEE European Symposium on Security and Privacy (EuroS&P)
2019
Later among the works it cites.
CrypTen: A new research tool for secure machine learning with PyTorch
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zoubin Ghahramani, ‘Probabilistic machine learning and artificial intelligence’, Nature
2015
Cited alongside, same era.
Samee Zahur, Mike Rosulek, and David Evans, ‘Two halves make a whole’, in EUROCRYPT
2015
Cited alongside, same era.
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E Lauter, Michael Naehrig, and John Wernsing, ‘Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy’, in ICML
2016
Cited alongside, same era.
Gilad Asharov, Yehuda Lindell, Thomas Schneider, and Michael Zohner, ‘More efficient oblivious transfer extensions’, Journal of Cryptology
2017
Cited alongside, same era.
Arthur Choi and Adnan Darwiche, ‘On relaxing determinism in arithmetic circuits’, in ICML
2017
Cited alongside, same era.
Bryce Goodman and Seth Flaxman, ‘European Union regulations on algorithmic decision-making and a “right to explanation”’, AI Magazine
2017
Cited alongside, same era.
Jian Liu, Mika Juuti, Yao Lu, and N Asokan, ‘Oblivious neural network predictions via MiniONN transformations’, in ACM Conference on Computer & Communications Security (CCS)
2017
Cited alongside, same era.
Payman Mohassel and Yupeng Zhang, ‘SecureML: A system for scalable privacy-preserving machine learning’, in IEEE Symposium on Security and Privacy (S&P)
2017
Cited alongside, same era.
David Gunning, Awni Hannun, Mark Ibrahim, Brian Knott, Laurens van der Maaten, Vinicius Reis, Shubho Sengupta, Shobha Venkataraman, and Xing Zhou · 2019
Later among the works it cites.
Ágnes Kiss, Masoud Naderpour, Jian Liu, N Asokan, and Thomas Schneider, ‘SoK: Modular and efficient private decision tree evaluation’, Privacy Enhancing Technologies (PETs)
2019
Later among the works it cites.
2019
Later among the works it cites.
How AT&T insiders were bribed to ‘unlock’ millions of phones
Louise Matsakis · 2019
Later among the works it cites.
2019
Later among the works it cites.
Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Xiaoting Shao, Martin Trapp, Kristian Kersting, and Zoubin Ghahramani, ‘Random sum-product networks: A simple and effective approach to probabilistic deep learning’, in UAI
2019
Later among the works it cites.
M Sadegh Riazi, Bita Darvish Rouhani, and Farinaz Koushanfar, ‘Deep learning on private data’, IEEE Security and Privacy Magazine
2019
Later among the works it cites.
M Sadegh Riazi, Mohammad Samragh, Hao Chen, Kim Laine, Kristin Lauter, and Farinaz Koushanfar, ‘XONN: XNOR-based oblivious deep neural network inference’, in USENIX Security
2019
Later among the works it cites.
Luc Rocher, Julien M Hendrickx, and Yves-Alexandre de Montjoye, ‘Estimating the success of re-identifications in incomplete datasets using generative models’, Nature Communications
2019
Later among the works it cites.
2019
Later among the works it cites.
Microsoft admits Outlook.com hackers were able to access emails
Tom Warren · 2019
Later among the works it cites.
Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca Ada Popa, ‘DELPHI: A cryptographic inference service for neural networks’, in USENIX Security
2020
Closest in time.