以隱私保護為目的之群聚方式研究

Abstract

Clustering is one of the most useful techniques to do some data analysis. But the conventional way to perform clustering usually offends one’s privacy. In the era of digital information, privacy is a very important concern in our daily life. To preserve one’s privacy, we exploit several privacy-preserving clustering schemes in this thesis. In the beginning, introduction to clustering, distributed clustering, and privacy-preserving clustering schemes will be given in order. And then, two major schemes, privacy-preserving k-means clustering and privacy-preserving hierarchical clustering, are illustrated in details. We implement these algorithms and perform experiments with both simulated ones and realistic data sets to learn the characteristics of each privacy-preserving clustering algorithm and evaluate the feasibility and usefulness of privacy-preserving clustering schemes.