Fully Homomorphically Encrypted Deep Learning as a Service
This work addresses data privacy concerns for stakeholders in domains like the agri-food supply chain by enabling secure machine learning predictions, though it is incremental in optimizing FHE for practical use.
The paper tackles the challenge of scaling Fully Homomorphic Encryption (FHE) for deep learning to enable private predictions, finding that while it incurs high spatial complexity, the time complexity remains within reasonable bounds, as demonstrated in a milk yield prediction case.
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 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.