Privacy-preserving Naive Bayes classification using trusted third party and different offset computation over distributed databases

Privacy-preservation in distributed databases is an important area of research in recent years. In a typical scenario, multiple parties may be wish to collaborate to extract interesting global information such as class labels without revealing their respective data to each other. This may be particularly useful in applications such as car selling units, medical research etc. In the proposed work, we aim to develop a global classification model based on the Naïve Bayes classification scheme. The Naïve Bayes classification has been used because of its applicability in case of car-evaluation dataset. For privacy-preservation of the data, the concept of trusted third party with different offset has been used. The data is first anonymized at local party end and then the aggregation and global classification is done at the trusted third party. We have proposed algorithms and tested dataset for different distributed database scenarios such as horizontal, vertical and arbitrary partitions.