Peng, Xiaobing; Li, Qishun; Wang, Lizhen; Zhu, Yuquan Research progress of privacy-preserving support vector machines. (Chinese. English summary) Zbl 1389.68026 J. Jiangsu Univ., Nat. Sci. 38, No. 1, 78-85 (2017). Summary: To realize information security for future support vector machines (SVM) data mining, the privacy-preserving support vector machines (PPSVM) was investigated to obtain effective result. The characteristics of SVM classifiers were analyzed to find the security hole. The latest literatures and related research were summarized. The recent progress of privacy-preserving support vector machines was presented based on data perturbation and data encryption. The future hot research directions of new privacy-preserving support vector machine technologies in distributed environment, more effective fully homomorphic encryption (FHE) schemes and privacy-preserving support vector machine technologies for big data mining were pointed out. MSC: 68P25 Data encryption (aspects in computer science) 68T05 Learning and adaptive systems in artificial intelligence Keywords:privacy preservation; SVM; secure multi-party computation; homomorphic encryption; big data PDFBibTeX XMLCite \textit{X. Peng} et al., J. Jiangsu Univ., Nat. Sci. 38, No. 1, 78--85 (2017; Zbl 1389.68026) Full Text: DOI