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Fully Homomorphically Encrypted Deep Learning as a Service

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School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
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Scotland’s Rural College, Craibstone Estate, Ferguson Building, Aberdeen AB21 9YA, UK
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Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Andreas Holzinger and Francesco Buccafurri
Mach. Learn. Knowl. Extr. 2021, 3(4), 819-834; https://doi.org/10.3390/make3040041
Received: 29 August 2021 / Revised: 5 October 2021 / Accepted: 9 October 2021 / Published: 13 October 2021
Fully Homomorphic Encryption (FHE) is a relatively recent advancement in the field of privacy-preserving technologies. FHE allows for the arbitrary depth computation of both addition and multiplication, and thus the application of abelian/polynomial equations, like those found in deep learning algorithms. This project investigates how FHE with deep learning can be used at scale toward accurate sequence prediction, with a relatively low time complexity, the problems that such a system incurs, and mitigations/solutions for such problems. In addition, we discuss how this could have an impact on the future of data privacy and how it can enable data sharing across various actors in the agri-food supply chain, hence allowing the development of machine learning-based systems. Finally, we find that although FHE incurs a high spatial complexity cost, the run time is within expected reasonable bounds, while allowing for absolutely private predictions to be made, in our case for milk yield prediction with a Mean Absolute Percentage Error (MAPE) of 12.4% and an accuracy of 87.6% on average. View Full-Text
Keywords: deep learning; fully homomorphic encryption; convolutional neural network; privacy-preserving technologies; agri-food; data sharing deep learning; fully homomorphic encryption; convolutional neural network; privacy-preserving technologies; agri-food; data sharing
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MDPI and ACS Style

Onoufriou, G.; Mayfield, P.; Leontidis, G. Fully Homomorphically Encrypted Deep Learning as a Service. Mach. Learn. Knowl. Extr. 2021, 3, 819-834. https://doi.org/10.3390/make3040041

AMA Style

Onoufriou G, Mayfield P, Leontidis G. Fully Homomorphically Encrypted Deep Learning as a Service. Machine Learning and Knowledge Extraction. 2021; 3(4):819-834. https://doi.org/10.3390/make3040041

Chicago/Turabian Style

Onoufriou, George, Paul Mayfield, and Georgios Leontidis. 2021. "Fully Homomorphically Encrypted Deep Learning as a Service" Machine Learning and Knowledge Extraction 3, no. 4: 819-834. https://doi.org/10.3390/make3040041

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