Privacy preserving data classification using inner product encryption - CEA - Commissariat à l’énergie atomique et aux énergies alternatives Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Privacy preserving data classification using inner product encryption

Résumé

In the context of data outsourcing more and more concerns raise about the privacy of user’s data. One solution is to outsource the data in encrypted form. Meanwhile obtaining a service based on machine learning predictions on user data remains very important in real-life situations. This paper presents ways to combine machine learning algorithms and IPE in order to perform classification on encrypted data. The proposed privacy preserving classification schemes allow to keep user’s data encrypted but at the same time revealing to a server classification results on this data. We study the performance of such classification schemes and their information leakage.
Fichier non déposé

Dates et versions

cea-01832758 , version 1 (09-07-2018)

Identifiants

Citer

D. Ligier, S. Carpov, C. Fontaine, R. Sirdey. Privacy preserving data classification using inner product encryption. Security and Privacy in Communication Networks. SecureComm 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Oct 2016, Guangzhou, China. pp.755-757, ⟨10.1007/978-3-319-59608-2_44⟩. ⟨cea-01832758⟩
55 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More