Privacy-Preserving KNN Classification Algorithm for Smart Grid

Abstract

With the development of Internet of Things (IOT), outsourcing data and tasks to a cloud server has become a popular and economical way for small devices with restricted ability. The k-nearest neighbors (KNNs) classification algorithm have been commonly applied to medical image classification, abnormal detection, defective product identification, and so on. The previous privacy-preserving KNN algorithms are based on two cloud servers, which have high computational and communication costs. In this paper, we design a privacy-preserving KNN classification (PPKC) algorithm with single cloud server for smart grid. Specifically, each smart meter and control center encrypted their data with Paillier cryptosystem and the cloud server does some calculation on the encrypted data. We prove that PPKC can protect the privacy of both smart meters and control centers, and the classification results are also private for the server. Besides, both smart meters and control centers can stay offline after uploading their data. The experiment results demonstrate that the proposed PPKC algorithm is more efficient than the previous algorithms and it can obtain almost the same accuracy as the original KNN, which means the PPKC algorithm is more applicable for small devices in IOT.

A Review on Privacy Preserving Using Machine learning and Deep Learning Techniques

“…ML, a branch of AI, uses mathematical and scientific methods to learn from the data and derive new insights, making healthcare apps smarter. Additionally, cognitive psychology is a synthesis of numerous scientific fields that employ AI, ML, and many other mathematical & scientific techniques to learn and obtain new insights [32]. It has a significant impact on how intelligent applications become.…”