Cryptology ePrint Archive: Report 2019/1129

Privacy-Enhanced Machine Learning with Functional Encryption

Tilen Marc and Miha Stopar and Jan Hartman and Manca Bizjak and 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.

Category / Keywords: implementation / functional encryption, machine learning

Original Publication (with minor differences): ESORICS 2019
DOI:
https://doi.org/10.1007/978-3-030-29959-0_1

Date: received 1 Oct 2019

Contact author: tilen marc at xlab si

Available format(s): PDF | BibTeX Citation

Version: 20191002:075932 (All versions of this report)

Short URL: ia.cr/2019/1129


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