Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
Knowledge-Based Systems
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Privacy preservation is an important area of research in recent years. Due to the advancement of technology, enormous digital data is being generated at various locations. There are many applications such as market basket analysis, medical research etc where the global results computation places a significant role. The collaborating parties are generally interested in finding the global results for their integrated data without revealing the personal details to the other party. There are few proposals which talk about privacy preservation of vertical partitioned distributed database. Our proposed novel approach preserves the privacy of the distributed databases, using Na茂ve Bayes Classification along with the trusted third party and secure multiparty computation.