Reviews: Partially Encrypted Deep Learning using Functional Encryption

Summary of the work: This paper proposes a methodology to perform inference on encrypted data using functional evaluation. Authors develop a specific model consisting of private and public execution; the private (cypher-text) execution takes place a 2-layer perceptron with square activation functions in the hidden layer. The output of this 2-layer perceptron is revealed to the server, which runs another ML model to classify the input. Authors provide Functional Encryption tools to efficiently run the private part of the protocol. They also propose a strong points: - Authors clearly distinguish their work from other private inference scenarios: their target is applications where the client might not be "online" and cannot communicate in an SFE protocol.