An Efficient Solution for Privacy-preserving Naïve Bayes Classification in Fully Distributed Data Model
Từ khóa:
khai phá dữ liệu và học máy đảm bảo tính riêng tư; tính toán bảo mật nhiều thành viên; phân lớp Naïve Bayes; mã hóa đồng cấu; tính riêng tư của dữ liệuTóm tắt
Abstract—Recently, privacy preservation has become one of the most important problems in data mining and machine learning. In this paper, we propose a novel privacy-preserving Naïve Bayes classifier for the fully distributed data scenario where each record is only kept by a unique owner. Our proposed solution is based on a secure multi-party computation protocol, so that it has the capability to securely protect each data owner’s privacy, as well as accurately guarantee the classification model. Furthermore, our experimental results show that the new solution is efficient enough for practical applications.
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