SOM-based recommendations with privacy on multi-party vertically distributed data

Data collected for providing recommendations can be partitioned among different parties. Offering distributed data-based predictions is popular due to mutual advantages. It is almost impossible to present trustworthy referrals with decent accuracy from split data only. Meaningful outcomes can be drawn from adequate data. Those companies with distributed data might want to collaborate to produce accurate and dependable recommendations to their customers. However, they hesitate to work together or refuse to collaborate because of privacy, financial concerns, and legal issues. If privacy-preserving measures are provided, such data holders might decide to collaborate for better predictions. In this study, we investigate how to provide predictions based on vertically distributed data (VDD) among multiple parties without deeply jeopardizing their confidentiality. Users are first grouped into various clusters off-line using self-organizing map clustering while protecting the online vendors’ privacy. With privacy concerns, recommendations are produced based on partitioned data using a nearest neighbour prediction algorithm. We analyse our privacy-preserving scheme in terms of confidentiality and supplementary costs. Our analysis shows that our method offers recommendations without greatly exposing data holders’ privacy and causes negligible superfluous costs because of privacy concerns. To evaluate the scheme in terms of accuracy, we perform real-data-based experiments. Our experiment results demonstrate that the scheme is still able to provide truthful predictions.