Privacy-Preserving SVM Computing in the Encrypted Domain
Takahiro Maekawa, Ayana Kawamura, Yuma Kinoshita, Hitoshi Kiya · 2018
Privacy-preserving Support Vector Machine (SVM) computing scheme is proposed in this paper. Cloud computing has been spreading in many fields. However, the cloud computing has some serious issues for end users, such as unauthorized use and leak of data, and privacy compromise. We focus on templates protected by a block scrambling-based encryption scheme, and consider some properties of the protected templates for secure SVM computing, where templates mean features extracted from data. The proposed scheme enables us not only to protect templates, but also to have the same performance as that of unprotected templates under some useful kernel functions. Moreover, it can be directly carried out by using well-known SVM algorithms, without preparing any algorithms specialized for secure SVM computing. In an experiment, the pfroposed scheme is applied to a face-based authentication algorithm with SVM classifiers to confirm the effectiveness.