Exposing privacy risks in indoor air pollution monitoring systems

International Institute of Information Technology, Hyderabad, India

^* Corresponding author: krishna.singh@research.iiit.ac.in

Abstract

Indoor air pollution monitoring has been a region of interest in recent times. Multiple Internet of Things (IoT) enabled devices are available for this purpose. With the growing number of sensors in our daily environment, huge amounts of data are being collected and pushed to the servers through the Internet. This study aims to reveal that seemingly trivial indoor air pollution data containing particulate matter, carbon dioxide, and temperature can reveal complex insights about an individual’s lifestyle. Data was collected over a period of four months in a real-world environment. The study demonstrates the inference of cooking activities by using machine learning and deep learning techniques. The study further demonstrates that different food items and culinary practices have different air pollution signatures, which can be identified and distinguished with great accuracy (>90%). In the practice of inferential analysis, it is not necessary to rely on data characterised by high frequency or granularity. Less detailed data like hourly averages, can be used to make meaningful conclusions that might intrude on an individual’s privacy. With the rapid advancement in machine learning and deep learning, a proactive approach to privacy is needed to ensure that the collected data and its usage do not intentionally or unintentionally breach individual privacy.