A PhD dissertation from a recent ThaW graduate.

The recent popularization of mobile devices equipped with high-performance sensors has given rise to the fast development of mobile sensing technology. Mobile sensing applications, such as gesture recognition, vital sign monitoring, localization, and identification analyze the signals generated by human activities and environment changes, and thus get a better understanding of the environment and human behaviors. While benefiting people’s lives, the growing capability of Mobile Sensing would also spawn new threats to security and privacy. On one hand, while the commercialization of new mobile devices enlarges the design space, it is challenging to design effective mobile sensing systems, which use fewer or cheaper sensors and achieve better performance or more functionalities. On the other hand, attackers can utilize the sensing strategies to track victims’ activities and cause privacy leakages. Mobile sensing attacks usually use side channels and target the information hidden in non-textual data. I present the Mobile Sensing Application-Attack (MSAA) framework, a general model showing the structures of mobile sensing applications and attacks, and how the two faces — the benefits and threats — are connected. MSAA reflects our principles of designing effective mobile sensing systems and exploring information leakages. Our experiment results show that our applications can achieve satisfactory performance, and also confirm the threats of privacy leakage if they are maliciously used, which reveals the two faces of mobile sensing.

Tuo Yu. Two faces of Mobile Sensing, PhD thesis, May 2020. University of Illinois at Urbana-Champaign. Download from http://hdl.handle.net/2142/107938