High Performance Privacy Preserving AI

Homomorphic EncryptionHomomorphic encryption (HE) is one technique that enables privacy-preserving computation.Operations (or circuit evaluations) are performed directly on homomorphically encrypted data.The result can then be decrypted.Since its introduction 46 years ago, homomorphic encryption has evolved significantly with a plethora of schemes to choose from, each with their own advantages and disadvantages.Following the invention of fully homomorphic encryption (FHE) in 2009, there has been a revolution in the field.FHE theoretically allows for evaluation of arbitrary circuits of unbounded depth.HE can be used with a variety of models, ranging from simple support vector machines (SVMs) and random forests to computationally expensive deep neural networks.