Towards Privacy Preserving Mining over Distributed Cloud Databases

Due to great advances in computing and Internet technologies, organizations have been enabled to collect and generate a large amount of data. Most of these organizations tend to analyze their data to discover new patterns. Usually, analyzing such amount of data requires huge computational power and storage facilities that may not be available to these organizations. Cloud computing offers the best way to solve this problem. Storing the private data of different organizations in the same cloud server enhances the mining process, but at the same time, raises privacy concerns. Therefore, it is highly recommended to support privacy preserving data mining algorithms in the cloud environment. This paper introduces an efficient and accurate cryptography-based scheme for mining the cloud data in a secure way without loss of accuracy. Specifically, we address the problem of K-nearest neighbor (KNN) classification over horizontally distributed databases without revealing any unnecessary information. We have utilized the recently developed cryptography primitive, order preserving symmetric encryption (OPSE), to integrate securely the local classifications at a lower cost than the previously presented privacy preserving data mining schemes. Empirical results on real datasets demonstrate that the proposed scheme has similar performance with the naive mining systems in terms of classification accuracy.