Federated KNN-Based Privacy-Preserving Position Recommendation for Indoor Consumer Applications

Abstract

Indoor positioning (IP) has attracted significant demand in diverse smart indoor consumer electronics applications like domotics appliances, automated energy management, patient tracking in hospitals, indoor navigation industries, etc. Most of these applications use Wi-Fi access points and a centralized server to create an IP network. The location of target nodes in these systems is disclosed, compromising user privacy. To overcome this, the current work proposes a federated KNN-based privacy-enforcing technique where the location coordinates of each target node are secured through discrete coordinate encryption. Hence the exact location coordinates are known only to the target node. However, real time consumer applications show that the privacy preserving technique increases localization error. But, efficient deployment of access points can improve localization accuracy. Therefore, we use Hausdorff distance to deploy dynamic access points based on the movements of the target nodes within convex hull regions. Experiments reveal that the proposed Hausdorff distance-based deployment model incorporated with a federated KNN results in better localization accuracy. Moreover, the proposed deployment technique does not require any additional hardware, making the system cost efficient.