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We present automatically parameterised Fully Homomorphic Encryption (FHE) forencrypted neural network inference and exemplify our inference over FHEcompatible neural networks with our own open-source framework and reproducibleexamples. We use the 4th generation Cheon, Kim, Kim and Song (CKKS) FHE schemeover fixed points provided by the Microsoft Simple Encrypted Arithmetic Library(MS-SEAL). We significantly enhance the usability and applicability of FHE indeep learning contexts, with a focus on the constituent graphs, traversal, andoptimisation. We find that FHE is not a panacea for all privacy preservingmachine learning (PPML) problems, and that certain limitations still remain,such as model training. However we also find that in certain contexts FHE iswell suited for computing completely private predictions with neural networks.The ability to privately compute sensitive problems more easily, while loweringthe barriers to entry, can allow otherwise too-sensitive fields to beginadvantaging themselves of performant third-party neural networks. Lastly weshow how encrypted deep learning can be applied to a sensitive real worldproblem in agri-food, i.e. strawberry yield forecasting, demonstratingcompetitive performance. We argue that the adoption of encrypted deep learningmethods at scale could allow for a greater adoption of deep learningmethodologies where privacy concerns exists, hence having a large positivepotential impact within the agri-food sector and its journey to net zero.

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