Bounding Privacy Leakage in Smart Buildings

TL;DR

This paper investigates privacy leakage in smart buildings caused by sensor access and proposes adding Gaussian noise to measurements to obscure occupancy changes, deriving bounds on estimator variance and validating results through simulation.

Contribution

It introduces a method to mitigate privacy leaks in smart buildings by adding Gaussian noise and derives theoretical bounds on estimator variance related to system parameters.

Findings

Signal-to-noise ratio significantly impacts privacy protection.

System dynamics influence the effectiveness of noise-based privacy measures.

Simulation results align well with theoretical bounds.

Abstract

Smart building management systems rely on sensors to optimize the operation of buildings. If an unauthorized user gains access to these sensors, a privacy leak may occur. This paper considers such a potential leak of privacy in a smart residential building, and how it may be mitigated through corrupting the measurements with additive Gaussian noise. This corruption is done in order to hide the occupancy change in an apartment. A lower bound on the variance of any estimator that estimates the change time is derived. The bound is then used to analyze how different model parameters affect the variance. It is shown that the signal to noise ratio and the system dynamics are the main factors that affect the bound. These results are then verified on a simulator of the KTH Live-In Lab Testbed, showing good correspondence with theoretical results.