Privacy preserving approximate K-means clustering

Abstract

Privacy preserving computation is of utmost importance in a cloud computing environment where a client often requires to send sensitive data to servers offering computing services over untrusted networks. Eavesdropping over the network or malware at the server may lead to leaking sensitive information from the data. To prevent this, we propose to encode the input data in such a way that, firstly, it should be difficult to decode it back to the true data, and secondly, the computational results obtained with the encoded data should not be substantially different from those obtained with the true data. Specifically, the computational activity that we focus on is the K-means clustering, which is widely used for many data mining tasks. Our proposed variant of the K-means algorithm is capable of privacy preservation in the sense that it requires as input only binary encoded data, and is not allowed to access the true data vectors at any stage of the computation. During intermediate stages of K-means computation, our algorithm is able to effectively process the inputs with incomplete information seeking to yield outputs relatively close to the complete information (non-encoded) case. Evaluation on real datasets show that the proposed methods yields comparable clustering effectiveness in comparison to the standard K-means algorithm on image clustering (MNIST-8M dataset), and in fact outperforms the standard K-means on text clustering (ODPtweets dataset).