Outsourcing Two-Party Privacy Preserving K-Means Clustering Protocol in Wireless Sensor Networks

Nowadays wireless sensor network (WSN) is widely used in human-centric applications and environmental monitoring. Different institutes deploy their own WSNs for data collection and processing. It becomes a challenging problem when institutes collaborate to do data mining while intend to keep data privacy on each side. Privacy preserving data mining (PPDM) is used to solve the above problem, which enables multiple parties owning confidential data to run a data mining algorithm on their combined data, without revealing any unnecessary information to each other. However, due to the huge amount of data collected and the complexity of data mining algorithms, it is preferable to outsource most of the computations to the cloud. In this paper, we consider a scenario in which two parties with weak computational power need jointly run a k-means clustering protocol, at the same time outsource most of the computation of the protocol to the cloud. As a result, each party can have the correct result calculated by the data from both parties with most of the computation outsourced to the cloud. As for privacy, the data owned by one party should be kept confidential from both the other party and the cloud.