A recent THaW paper was nominated for Best Paper at the IoT conference:
With the rapid growth in the number of Internet of Things (IoT) devices with wireless communication capabilities, and sensitive information collection capabilities, it is becoming increasingly necessary to ensure that these devices communicate securely with only authorized devices. A major requirement of this secure communication is to ensure that both the devices share a secret, which can be used for secure pairing and encrypted communication. Manually imparting this secret to these devices becomes an unnecessary overhead, especially when the device interaction is transient. In this work, we empirically investigate the possibility of using an out-of-band communication channel – vibration, generated by a custom smartRing – to share a secret with a compatible IoT device. Through a user study with 12 participants we show that in the best case we can exchange 85.9% messages successfully. Our technique demonstrates the possibility of sharing messages accurately, quickly, and securely as compared to several existing techniques.
To learn more, check out the video presentation here.
Sougata Sen and David Kotz. VibeRing: Using vibrations from a smart ring as an out-of-band channel for sharing secret keys. In Proceedings of the International Conference on the Internet of Things (IoT), page Article#13 (8 pages), October 2020. ACM. DOI: 10.1145/3341162.3343818
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.
When filling out privacy-related forms in public places such as hospitals or clinics, people usually are not aware that the sound of their handwriting can leak personal information. In this paper, we explore the possibility of eavesdropping on handwriting via nearby mobile devices based on audio signal processing and machine learning. By presenting a proof-of-concept system, WritingHacker, we show the usage of mobile devices to collect the sound of victims’ handwriting, and to extract handwriting-specific features for machine learning based analysis. An attacker can keep a mobile device, such as a common smartphone, touching the desk used by the victim to record the audio signals of handwriting. Then, the system can provide a word-level estimate for the content of the handwriting. Moreover, if the relative position between the device and the handwriting is known, a hand motion tracking method can be further applied to enhance the system’s performance. Our prototype system’s experimental results show that the accuracy of word recognition reaches around 70 – 80 percent under certain conditions, which reveals the danger of privacy leakage through the sound of handwriting.
July 2020: Tuo Yu, Haiming Jin, and Klara Nahrstedt. Mobile devices based eavesdropping of handwriting. IEEE Transactions on Mobile Computing 19(7), pages 1649–1663, July 2020. IEEE. DOI: 10.1109/TMC.2019.2912747
May 2020: Chen Yan, Hocheol Shin, Connor Bolton, Wenyuan Xu, Yongdae Kim, and Kevin Fu. SoK: A Minimalist Approach to Formalizing Analog Sensor Security. pages 233–248, May 2020. IEEE. DOI: 10.1109/sp40000.2020.00026
Over the last six years, several papers demonstrated how intentional analog interference based on acoustics, RF, lasers, and other physical modalities could induce faults, influence, or even control the output of sensors. Damage to the availability and integrity of sensor output carries significant risks to safety-critical systems that make automated decisions based on trusted sensor measurement. This IEEE S&P conference ‘Systematization of Knowledge’ paper provides a framework for assessing the security of analog sensors without sensor engineers needing to learn significantly new notation. The primary goals of the systematization are (1) to enable more meaningful quantification of risk for the design and evaluation of past and future sensors, (2) to better predict new attack vectors, and (3) to establish defensive design patterns that make sensors more resistant to analog attacks.