Authors:
Damien Ligier
1
;
Sergiu Carpov
2
;
Caroline Fontaine
3
and
Renaud Sirdey
2
Affiliations:
1
CEAT LIST, CNRS/Lab-STICC and Telecom Bretagne and UBL, France
;
2
CEAT LIST, France
;
3
CNRS/Lab-STICC and Telecom Bretagne and UBL, France
Keyword(s):
Functional Encryption, Inner-Product Encryption, Classification, Linear Classification.
Related
Ontology
Subjects/Areas/Topics:
Information and Systems Security
;
Information Assurance
;
Information Hiding
Abstract:
In the context of data outsourcing more and more concerns raise about the privacy of user’s data. Simultaneously,
cryptographers are designing schemes enabling computation on ciphertexts (homomorphic encryption,
functional encryption, etc.). Their use in real world applications is difficult. In this work we focus on functional
encryption schemes enabling computation of inner-product on encrypted vectors and their use in real
world scenarios. We propose a protocol combining such type of functional encryption schemes with machine
learning algorithms. Indeed, we think that being able to perform classification over encrypted data is useful
in many scenarios, in particular when the owners of the data are not ready to share it. After explaining our
protocol, we detail the implemented handwritten digit recognition use case, and then, we study its security.