Efficient Privacy Preserving Nearest Neighboring Classification from Tree Structures and Secret Sharing
Jhe-Kai Yang, Kuan-Chun Huang, Cheng‐Yang Chung, Yu‐Chi Chen, Ting-Wei Wu · 2022
The k-nearest neighbor (kNN) algorithm is a very simple manner in the area of machine learning. It is a supervised method to classify according to the distance between different instances and is also widely used in solving some classification problems. It is expected to obtain better training with a larger dataset. However, how to perform kNN algorithm efficiently is an issue with privacy-preserving. In this paper, we proposed a privacy-preserving k -nearest neighboring scheme by secret sharing and improve the kNN classification by preprocessing with tree structures. Finally, the applicability of our method is shown by experiments with real datasets.