Fetching the paper…
Reading the bibliography…
Machine learning is promising, but it often needs to process vast amounts of sensitive data which raises concerns about privacy.
Secure multi-party computation
Oded Goldreich · 1998
Earlier work this paper cites.
Model selection and model averaging
Gerda Claeskens, Nils Lid Hjort, et al · 2008
Earlier work this paper cites.
Bitcoin: A peer-to-peer electronic cash system
Satoshi Nakamoto et al · 2008
Earlier work this paper cites.
The unreasonable effectiveness of data
Alon Halevy, Peter Norvig, and Fernando Pereira · 2009
Earlier work this paper cites.
A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
Earlier work this paper cites.
The ethics of artificial intelligence
Nick Bostrom and Eliezer Yudkowsky · 2014
Earlier work this paper cites.
Ethereum: A secure decentralised generalised transaction ledger
Gavin Wood et al · 2014
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Earlier work this paper cites.
Trusted execution environment: what it is, and what it is not
Mohamed Sabt, Mohammed Achemlal, and Abdelmadjid Bouabdallah · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
Cited alongside, same era.
Towards the science of security and privacy in machine learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman · 2016
Cited alongside, same era.
Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
Cited alongside, same era.
Hyperledger fabric: a distributed operating system for permissioned blockchains
Elli Androulaki, Artem Barger, Vita Bortnikov, Christian Cachin, Konstantinos Christidis, Angelo De Caro, David Enyeart, Christopher Ferris, Gennady Laventman, Yacov Manevich, et al · 2018
Cited alongside, same era.
Realizing private and practical pharmacological collaboration
Brian Hie, Hyunghoon Cho, and Bonnie Berger · 2018
https://arxiv.org/pdf/1811.04017.pdf
A generic framework for privacy preserving deep learning · 2019
Closest in time.
https://www.hyperledger.org/projects/fabric
Hyperledger fabric · 2019
Closest in time.
http://docs.oasiscloud.io/en/latest/overview/
Oasis labs platform overview · 2019
Closest in time.
https://oceanprotocol.com/tech-whitepaper.pdf
Ocean protocol technical whitepaper · 2019
Closest in time.
https://ec.europa.eu/justice/article-29/documentation/opinion-recommendation/files/2014/wp216_en.pdf
Opinion 05/2014 on anonymisation techniques · 2019
Closest in time.
https://www.tensorflow.org/federated
Tensor flow federated · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Cited alongside, same era.
https://developer.mozilla.org/en-US/docs/Web/Security/Information_Security_Basics/Confidentiality,_Integrity,_and_Availability
Confidentiality, integrity, and availability · 2019
Cited alongside, same era.
https://github.com/tf-encrypted/tf-encrypted
Dropout labs tf encrypted libraryw · 2019
Cited alongside, same era.
https://eur-lex.europa.eu/eli/reg/2016/679/oj
General data protection regulation · 2019
Cited alongside, same era.
Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart
Cited in the paper.
https://eu.udacity.com/course/secure-and-private-ai--ud185
Udacity ’secure and private ai’ course · 2019
Closest in time.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
Closest in time.
Keystone: An open framework for architecting tees, 2019
Dayeol Lee, David Kohlbrenner, Shweta Shinde, Dawn Song, and Krste Asanovic · 2019
Closest in time.