Funding: This research was funded by UKRI-EPSRC grant “The Internet of Food Things” grant number EP/R045127/1.Peer reviewedPublisher PD

Leontidis, Georgios

Mayfield, Paul

Onoufriou, Georgios

Machine Learning and Knowledge Extraction

English

arXiv

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

Onoufriou, George

SRUC - Scotland's Rural College

Fully Homomorphically Encrypted Deep Learning as a Service

George Onoufriou

Paul Mayfield

Georgios Leontidis

Directory of Open Access Journals

Funding: This research was funded by UKRI-EPSRC grant “The Internet of Food Things” grant number EP/R045127/1.Peer reviewe

Aberdeen University Research

https://pure.sruc.ac.uk/files/43345185/make-03-00041.pdf

Fully Homomorphically Encrypted Deep Learning as a Service

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SRUC - Scotland's Rural College

Directory of Open Access Journals

Aberdeen University Research

SRUC - Scotland's Rural College