Exploring Privacy Breaches and Mitigation Strategies of Occupancy Sensors in Smart Buildings
Year: 2019
Type: conference-paper
Abstract: Occupancy sensors are an integral part of many smart building applications, including energy and space optimizations. However, as more occupancy data is collected at an increasingly fine-grained level, possibilities of potential privacy breaches also increase. In this paper, we propose an attack in which the adversary associates an individual with the occupancy sensor by combining the occupancy data with other public information that could easily be obtained online. We conduct an experiment using real-world data to demonstrate that the proposed attack is not only feasible, but the adversary can efficiently identify a small set of sensor IDs that contains the ID of interest given a relatively small amount of additional information. We also summarize a number of mitigation strategies against the proposed attack.
Authors: Phillip Lee, Eun-Jeong Shin, Valerie Guralnik, Sharad Mehrotra, Nalini Venkatasubramanian, Kevin T. Smith
Institutions: Honeywell (United States), University of California, Irvine
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: G4713059963, G5552018505
OpenAlex ID: W2998519508