Big Data

Semi-Trusted Mixer Based Privacy Preserving Distributed Data Mining for Resource Constrained Devices

Download Now Date Added: Apr 2010
Format: PDF

In this paper a homomorphic privacy preserving association rule mining algorithm is proposed which can be deployed in Resource Constrained Devices (RCD). Privacy preserved exchange of counts of itemsets among distributed mining sites is a vital part in association rule mining process. Existing cryptography based privacy preserving solutions consume lot of computation due to complex mathematical equations involved. Therefore less computation involved privacy solutions are extremely necessary to deploy mining applications in RCD. In this algorithm, a semi-trusted mixer is used to unify the counts of itemsets encrypted by all mining sites without revealing individual values. The proposed algorithm is built on with a well known communication efficient association rule mining algorithm named Count Distribution (CD).