Secure and Efficient k NN Classification for Industrial Internet of Things

Abstract

The ${k}$ -nearest neighbors ( ${k}$ NN) classification has been widely used for defective product identification and anomaly detection in the Industrial Internet of Things (IIoT). In this article, we propose a secure and efficient distributed ${k}$ NN classification algorithm (SEED- ${k}$ NN) to prevent information and control flow exposure while supporting large-scale data classification on distributed servers. Specifically, we first design a secure and efficient vector homomorphic encryption (VHE) scheme by constructing a key-switching matrix and a noise matrix for data encryption. Based on the designed VHE, SEED- ${k}$ NN is proposed to efficiently achieve the confidentiality of data flow, ${k}$ NN query, and class label, while enabling homomorphic operations on the encrypted data. Moreover, by leveraging the Map/Reduce architecture, SEED- ${k}$ NN enables the ${k}$ NN classification over the large-scale encrypted data on distributed servers for industrial control systems. Finally, we demonstrate that SEED- ${k}$ NN achieves semantic security and high classification accuracy, and is applicable in IIoT due to its high efficiency.