Privacy-Preserving Classification and Clustering Using Secure Multi-Party Computation

Nowadays, data mining and machine learning techniques are widely used in electronic applications in different areas such as e-government, e-health, e-business, and so on. One major and very crucial issue in these type of systems, which are normally distributed among two or more parties and are dealing with sensitive data, is preserving the privacy of individual’s sensitive information. Each party wants to keep its own raw data private while getting useful knowledge from the whole data owned by all the parties. Privacy-preserving Data Mining is dealing with this problem and many protocols have been introduced for various standard data mining algorithms so far. In this paper, we propose some new protocols for two popular techniques, classification and clustering, when data is horizontally or vertically partitioned among two or more parties. In classification we use Gini Index applying on ID3 algorithm to create decision tree from distributed data. We also propose two protocols for k-means Clustering which is a prominent clustering algorithm in data mining techniques. Some secure two and multi-party building blocks such as secure comparison, secure multi-party addition and multiplication are also proposed to use as sub-protocols in our algorithms.