Exploiting Air Quality Monitors to Perform Indoor Surveillance: Academic Setting

TL;DR

This paper demonstrates that low-cost air quality monitors can be used to accurately infer specific indoor activities, raising privacy concerns in indoor environments.

Contribution

It introduces a method to identify indoor activities using air quality data from a low-cost monitor with high accuracy, highlighting privacy implications.

Findings

97.7% classification accuracy in activity detection

Effective identification of 8 distinct indoor activities

Data collected over three months supports robustness

Abstract

Changing public perceptions and government regulations have led to the widespread use of low-cost air quality monitors in modern indoor spaces. Typically, these monitors detect air pollutants to augment the end user's understanding of her indoor environment. Studies have shown that having access to one's air quality context reinforces the user's urge to take necessary actions to improve the air over time. Thus, user's activities significantly influence the indoor air quality. Such correlation can be exploited to get hold of sensitive indoor activities from the side-channel air quality fluctuations. This study explores the odds of identifying eight indoor activities (i.e., enter, exit, fan on, fan off, AC on, AC off, gathering, eating) in a research lab with an in-house low-cost air quality monitoring platform named DALTON. Our extensive data collection and analysis over three months shows 97.7% classification accuracy in our dataset.