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With the rapid adoption of the Internet of Things (IoT), we face a new world, where we are never alone. At all times, a plethora of connected devices, from smartphones to home assistants to motion detectors continuously sense and monitor our activities. While these devices provide us convenience, they are often backed by powerful analytics to sift through large volume of personal data, at times collected without our awareness or consent. Such personal data can reveal a lot about ourselves like our habits and lifestyles, which not only has great commercial value to advertisers to serve targeted ads, but can also be misused by insurance companies, repressive governments and cybercriminals. The goal of this project is to build a foundation for understanding the different ways in which every day Internet of Things devices can leak sensitive information about our lifestyles. The proposed work will benefit the society as a whole as people will become more aware of the security and privacy risks associated with household Internet of Things devices. The proposal can also help identify vulnerable communication protocols and consequentially lead to the design of new privacy-enhancing protocols. Furthermore, this project will expose both undergraduate and graduate students to cutting-edge analytic tools and help develop a globally competitive science, technology, engineering and mathematics workforce.
This project proposes to build a testbed to analyze the extent to which household Internet of Things devices can leak sensitive information about ourselves. The proposed testbed will facilitate research along the following directions: i) help determine the feasibility of performing side-channel attacks to infer not only what devices reside inside a household, but also what higher order activities users may be performing, for example, inferring whether a user is making a call using Amazon Alexa or taking a nap, or working out on a treadmill; ii) help determine what data is collected and with whom it is shared; iii) help build an information dashboard to notify users about the potential privacy risks. Building a scalable testbed that can interact with a wide variety of devices will require addressing a host of systems related challenges. The data analysis will utilize a variety of advanced statistical, signal processing and machine learning techniques. This project will provide a much-needed insight into the security and privacy threats imposed by emerging Internet of Things devices. The proposed work can lead to not only identifying devices that leak sensitive information, but at the same time foster the design of new privacy-enhancing technologies.
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CRII: SaTC: Analyzing Information Leak in Smart Homes