Privacy-Enhanced Machine Learning with Functional Encryption

Thursday, January 10, 2019
ESORICS 2019 The European Symposium on Research in Computer, Luxembourg Security

Authors: Tilen Marc, Miha Stopar, Jan Hartman, Manca Bizjak and Jolanda Modic​

Functional ENcryption is a generalization of public-key encryption in which possessing a secret functional key allows one to learn a function of what the ciphertext is encrypting. This paper introduces the first fully-fledged open source cryptographic libraries for functional encryption. It also presents how Functional ENcryption can be used to build efficient privacy-enhanced machine learning models and it provides an implementation of three prediction services that can be applied on the encrypted data.

Finally, the paper discusses the advantages and disadvantages of the alternative approach for building privacy-enhanced machine learning models by using homomorphic encryption.