Privacy Concerns Regarding Occupant Tracking in Smart Buildings

TL;DR

This paper compares occupant tracking methods in smart buildings, highlighting privacy concerns, and proposes a vibration data anonymization technique that preserves localization accuracy while protecting occupant identity.

Contribution

It introduces a privacy-preserving method for vibration-based occupant tracking that anonymizes identifiable information without sacrificing localization precision.

Findings

Vibration sensors can localize occupants with accuracy comparable to other methods.

The proposed anonymization method successfully hides occupant gender information.

Localization accuracy is minimally affected by the privacy-preserving transformation.

Abstract

Tracking of occupants within buildings has become a topic of interest in the past decade. Occupant tracking has been used in the public safety, energy conservation, and marketing fields. Various methods have been demonstrated which can track people outside of and inside buildings; including GPS, visual-based tracking using surveillance cameras, and vibration-based tracking using sensors such as accelerometers. In this work, those main systems for tracking occupants are compared and contrasted for the levels of detail they give about where occupants are, as well as their respective privacy concerns and how identifiable the tracking information collected is to a specific person. We discuss a case study using vibrations sensors mounted in Virginia Tech's Goodwin Hall that was recently conducted, demonstrating that similar levels of accuracy in occupant localization can be achieved to current methods, and highlighting the amount of identifying information in the vibration signals dataset. Finally, a method of transforming the vibration data to preserve occupant privacy was proposed and tested on the dataset. The results indicate that our proposed method has successfully resulted in anonymizing the occupant's gender information which was previously identifiable from the vibration data, while minimally impacting the localization accuracy achieved without anonymization.