Privacy Preserving Data Classification using Inner-product Functional Encryption

In the context of data outsourcing more and more concerns raise about the privacy of user's data. Simultaneously, cryptographers are designing schemes enabling computation on ciphertexts (homomorphic encryption, functional encryption, etc.). Their use in real world applications is difficult. In this work we focus on functional encryption schemes enabling computation of inner-product on encrypted vectors and their use in real world scenarios. We propose a protocol combining such type of functional encryption schemes with machine learning algorithms. Indeed, we think that being able to perform classification over encrypted data is useful in many scenarios, in particular when the owners of the data are not ready to share it. After explaining our protocol, we detail the implemented handwritten digit recognition use case, and then, we study its security.