It’s rather important to secure cloud-based AI systems, especially when they they use sensitive data like photos or medical records. To date, though, that hasn’t been very practical — encrypting the data can render machine learning systems so slow as to be virtually unusable. MIT thankfully has a solution in the form of GAZELLE, a technology that promises to encrypt convolutional neural networks without a dramatic slowdown. The key was to meld two existing techniques in a way that avoids the usual bottlenecks those methods create. Read More

#cloud, #homomorphic-encryption