Exploiting Air Quality Monitors to Perform Indoor Surveillance: Academic Setting

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.

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Publication Details

Published
2024-09-21
DOI
https://doi.org/10.1145/3640471.3680243
Citations
1
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.21
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article

Exploiting Air Quality Monitors to Perform Indoor Surveillance: Academic Setting

Sandip Chakraborty, Prasenjit Karmakar, Swadhin Pradhan
1 citations
Air Quality Monitoring and Forecasting
0.21
article

Exploiting Air Quality Monitors to Perform Indoor Surveillance: Academic Setting

Sandip Chakraborty, Prasenjit Karmakar, Swadhin Pradhan
article en
1 citations

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.

Cisco Systems (United States) (US), Indian Institute of Technology Kharagpur (IN)
Climate action
Openalex Percentile: Top 49%
Air Quality Monitoring and Forecasting
0.21
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