Privacy-Aware Data Sharing in a Tree-Based Categorical Clustering Algorithm

Abstract

Despite being one of the most common approaches in unsupervised data analysis, a very small literature exists in applying formal methods to address data mining problems. This paper applies an abstract representation of a hierarchical categorical clustering algorithm (CCTree) to solve the problem of privacy-aware data clustering in distributed agents. The proposed methodology is based on rewriting systems, and automatically generates a global structure of the clusters. We prove that the proposed approach improves the time complexity. Moreover a metric is provided to measure the privacy gain after revealing the CCTree result. Furthermore, we discuss under what condition the CCTree clustering in distributed framework produces the comparable result to the centralized one.