Partially Encrypted Machine Learning using Functional Encryption

Understand

Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation.

• It allows outsourcing computation to untrusted servers without sacrificing privacy of sensitive data.
• We propose a practical framework to perform partially encrypted and privacy-preserving predictions which combines adversarial training and functional encryption.
• We first present a new functional encryption scheme to efficiently compute quadratic functions so that the data owner controls what can be computed but is not involved in the calculation: it provides a decryption key which allows one to learn a specific function evaluation of some encrypted data.