Welcome to the resource topic for 2019/1129
Title:
Privacy-Enhanced Machine Learning with Functional Encryption
Authors: Tilen Marc, Miha Stopar, Jan Hartman, Manca Bizjak, Jolanda Modic
Abstract: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.
ePrint: https://eprint.iacr.org/2019/1129
See all topics related to this paper.
Feel free to post resources that are related to this paper below.
Example resources include: implementations, explanation materials, talks, slides, links to previous discussions on other websites.
For more information, see the rules for Resource Topics .