Paper 2019/1129

Privacy-Enhanced Machine Learning with Functional Encryption

Tilen Marc, Miha Stopar, Jan Hartman, 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.

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
Published elsewhere. Minor revision. ESORICS 2019
DOI
10.1007/978-3-030-29959-0_1
Keywords
functional encryptionmachine learning
Contact author(s)
tilen marc @ xlab si
History
2019-10-02: received
Short URL
https://ia.cr/2019/1129
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2019/1129,
      author = {Tilen Marc and Miha Stopar and Jan Hartman and Manca Bizjak and Jolanda Modic},
      title = {Privacy-Enhanced Machine Learning with Functional Encryption},
      howpublished = {Cryptology {ePrint} Archive, Paper 2019/1129},
      year = {2019},
      doi = {10.1007/978-3-030-29959-0_1},
      url = {https://eprint.iacr.org/2019/1129}
}
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