Fully Homomorphically Encrypted Deep Learning as a Service

George Onoufriou, Paul Mayfield, Georgios Leontidis*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
64 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)819-834
Number of pages16
JournalMachine Learning and Knowledge Extraction
Issue number4
Early online date13 Oct 2021
Publication statusFirst published - 13 Oct 2021


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


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