Articles in Press

The ability to store vast quantities of data and the emergence of high speed networking have led to intense interest in distributed data mining. However, privacy concerns, as well as regulations, often prevent the sharing of data between multiple parties. Privacy-preserving distributed data mining allows the cooperative computation of data mining algorithms without requiring the participating organizations to reveal their individual data items to each other.

This paper makes several contributions. First, we present a simple, deterministic, I/O-efficient kclustering algorithm that was designed with the goal of enabling an efficient privacy-preserving version of the algorithm. Our algorithm examines each item in the database only once and uses only sequential access to the data. Our experiments show that this algorithm produces cluster centers that are, on average, more accurate than the ones produced by the well known iterative k-means algorithm, and compares well against BIRCH.

Second, we present a distributed privacy-preserving protocol for k-clustering based on our new clustering algorithm. The protocol applies to databases that are horizontally partitioned between two parties. The participants of the protocol learn only the final cluster centers on completion of the protocol. Unlike most of the earlier results in privacy-preserving clustering, our protocol does not reveal intermediate candidate cluster centers. The protocol is also efficient in terms of communication and does not depend on the size of the database. Although there have been other clustering algorithms that improve on the k-means algorithm, ours is the first for which a communication efficient cryptographic privacy-preserving protocol has been demonstrated.

^* Corresponding author.