Fully Homomorphically Encrypted Deep Learning as a Service

Georgios Onoufriou, Paul Mayfield, Georgios Leontidis

Research output: Contribution to journalArticlepeer-review

Abstract

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, derives, and proves how FHE with deep learning can be used at scale, 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 time complexity is within expected reasonable bounds, while allowing for absolutely private predictions to be made, in our case for milk yield prediction
Original languageEnglish
Pages (from-to)819-834
Number of pages16
JournalMachine Learning and Knowledge Extraction
Volume3
Issue number4
Early online date13 Oct 2021
DOIs
Publication statusE-pub ahead of print - 13 Oct 2021

Keywords

  • deep learning
  • fully homomorphic encryption
  • convolutional neural network
  • privacy-preserving technologies
  • agri-food
  • data sharing

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