EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting

George Onoufriou*, Marc Hanheide, Georgios Leontidis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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We present automatically parameterised Fully Homomorphic Encryption (FHE) for encrypted neural network inference and exemplify our inference over FHE-compatible neural networks with our own open-source framework and reproducible examples. We use the fourth generation Cheon, Kim, Kim, and Song (CKKS) FHE scheme over fixed points provided by the Microsoft Simple Encrypted Arithmetic Library (MS-SEAL). We significantly enhance the usability and applicability of FHE in deep learning contexts, with a focus on the constituent graphs, traversal, and optimisation. We find that FHE is not a panacea for all privacy-preserving machine learning (PPML) problems and that certain limitations still remain, such as model training. However, we also find that in certain contexts FHE is well-suited for computing completely private predictions with neural networks. The ability to privately compute sensitive problems more easily while lowering the barriers to entry can allow otherwise too-sensitive fields to begin advantaging themselves of performant third-party neural networks. Lastly, we show how encrypted deep learning can be applied to a sensitive real-world problem in agri-food, i.e., strawberry yield forecasting, demonstrating competitive performance. We argue that the adoption of encrypted deep learning methods at scale could allow for a greater adoption of deep learning methodologies where privacy concerns exist, hence having a large positive potential impact within the agri-food sector and its journey to net zero
Original languageEnglish
Article number8124
Number of pages24
Issue number22
Publication statusPublished - 24 Oct 2022

Bibliographical note

This research was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) studentship 2155898 grant: BB/S507453/1.

Data Availability Statement

There is partial availability of the datasets used in this paper. The first dataset we use called Fashion-MNIST can be found at the following address https:github. com/zalandoresearch/fashion-mnist. The second strawberry dataset is unfortunately currently not permitted to be shared.


  • fully homomorphic encryption
  • deep learning
  • machine learning
  • privacy-preserving technologies
  • agri-food
  • data sharing


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