Tommy Flowers: nothing like the machine he proposed had ever been contemplated. Tommy Flowers: nothing like the machine he proposed had ever been contemplated. Move over, Alan Turing: meet the working-class hero of Bletchley Park you didn't see in the movies The Oxbridge-educated boffin is feted as the codebreaking genius who helped Britain win the war. But should a little-known Post Office engineer named Tommy Flowers be seen as the real father of computing? T his is a story you know, right? It's early in the war and western Europe has fallen. Only the Channel stands between Britain and the fascist yoke; only Atlantic shipping lanes offer hope of the population continuing to be fed, clothed and armed. But hunting "wolf packs" of Nazi U-boats pick off merchant shipping at will, coordinated by radio instructions the Brits can intercept but can't read, thanks to the fiendish Enigma encryption machine.

In this paper, we propose a homomorphic matrix completion algorithm for privacy-preserving purpose.

We use the 4th 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 exists, hence having a large positive potential impact within the agri-food sector and its journey to net zero.