How to curtail oversensing in the home

Recent THaW paper:

Future homes are an IoT hotspot that will be particularly at risk. Sensitive information such as passwords, identification, and financial transactions are abundant in the home—as are sensor systems such as digital assistants, smartphones, and interactive home appliances that may unintentionally capture this sensitive information. For example, how motion sensors can capture nearby sounds, including words and keystrokes. We call this oversensing: where authorized access to sensor data provides an application with superfluous and potentially sensitive information. Manufacturers and system designers must employ the principle of least privilege at a more fine-grained level and with awareness of how often different sensors overlap in the sensitive information they leak. We project that directing technical efforts toward a more holistic conception of sensor data in system design and permissioning will reduce risks of oversensing.

Connor Bolton, Kevin Fu, Josiah Hester, and Jun Han. How to curtail oversensing in the homeCommunications of the ACM 63(6), pages 20–24, June 2020. ACM. DOI: 10.1145/3396261

New THaW Patent

The THaW team is pleased to announce one new patent derived from THaW research. For the complete list of patents, visit our Tech Transfer page.

Abstract: Systems and methods are disclosed for providing a trusted computing environment that provides data security in commodity computing systems. Such systems and methods deploy a flexible architecture comprised of distributed trusted platform modules (TPMs) configured to establish a root-of-trust within a heterogeneous network environment comprised of non-TPM enabled IoT devices and legacy computing devices. A data traffic module is positioned between a local area network and one or more non-TPM enabled IoT devices and legacy computing devices, and is configured to control and monitor data communication among such IoT devices and legacy computing devices and from such IoT devices and legacy computing devices to external computers. The data traffic module supports attestation of the IoT devices and legacy computing devices, supports secure boot operations of the IoT devices and legacy computing devices, and provides tamper resistance to such IoT devices and legacy computing devices.

Kevin Kornegay and Willie Lee Thompson II. Decentralized Root-of-Trust Framework for Heterogeneous Networks, November 2020. Morgan State University; USPTO. Download from https://patents.google.com/patent/US20180196945A1/en

VibeRing: Using vibrations from a smart ring as an out-of-band channel for sharing secret keys

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.

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

Two faces of Mobile Sensing

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 SensingPhD thesis, May 2020. University of Illinois at Urbana-Champaign. Download from http://hdl.handle.net/2142/107938

Mobile devices based eavesdropping of handwriting

Recent THaW paper:

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 handwritingIEEE Transactions on Mobile Computing 19(7), pages 1649–1663, July 2020. IEEE. DOI: 10.1109/TMC.2019.2912747 

SoK: A Minimalist Approach to Formalizing Analog Sensor Security

Recent THaW paper:

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.

New THaW dissertations

The THaW team recently released the dissertations of two of its newest PhDs.

Tuo Yu, University of Illinois: The two faces of mobile sensing

https://www.ideals.illinois.edu/handle/2142/107938

Abstract: 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 analyze the signals generated by human activities and environment changes, and thus get a better understanding of the environment and human behaviors. Nowadays, researchers have developed diverse mobile sensing applications, which benefit people’s living, such as gesture recognition, vital sign monitoring, localization, and identification. Mobile sensing has two faces. While benefiting people’s lives, its growing capability would also spawn new threats to security and privacy. Exploring the dual character of mobile sensing is challenging. 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 less 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. It is challenging to find the potential leakages, because mobile sensing attacks usually use side channels and target the information hidden in non-textual data. To target the above challenges, 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 are connected. MSAA reflects our principle of designing effective mobile sensing systems, i.e., we reduce the cost and improve the performance of current systems by exploring different sensors, various requirements for user/environment contexts, and different sensing algorithms. MSAA also shows our principle of exploring information leakages, i.e., we break a sensing system into basic components, and for each component we consider what user information could be extracted if data are leaked. I take handwriting input and indoor walking path tracking as examples, and show how we design effective mobile sensing techniques and also investigate their potential threats following MSAA. I design an audio-based handwriting input method for tiny mobile devices, which allows users to input words by writing on tables with fingers. Then, I explore the attacker’s capability of recognizing a victim’s handwriting content based on the handwriting sound. I also present an in-shoe force sensor-based indoor walking path tracking system, which enables smart shoes to locate users. Meanwhile, I show how likely a victim can be located if the foot force data are leaked to attackers. 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.

Travis Peters, Dartmouth College: Trustworthy Wireless Personal Area Networks.

https://www.cs.dartmouth.edu/~trdata/reports/abstracts/TR2020-878/

Abstract: In the Internet of Things (IoT), everyday objects are equipped with the ability to compute and communicate. These smart things have invaded the lives of everyday people, being constantly carried or worn on our bodies, and entering into our homes, our healthcare, and beyond. This has given rise to wireless networks of smart, connected, always-on, personal things that are constantly around us, and have unfettered access to our most personal data as well as all of the other devices that we own and encounter throughout our day. It should, therefore, come as no surprise that our personal devices and data are frequent targets of ever-present threats. Securing these devices and networks, however, is challenging. In this dissertation, we outline three critical problems in the context of Wireless Personal Area Networks (WPANs) and present our solutions to these problems.

First, I present our Trusted I/O solution (BASTION-SGX) for protecting sensitive user data transferred between wirelessly connected (Bluetooth) devices. This work shows how in-transit data can be protected from privileged threats, such as a compromised OS, on commodity systems. I present insights into the Bluetooth architecture, Intel’s Software Guard Extensions (SGX), and how a Trusted I/O solution can be engineered on commodity devices equipped with SGX.

Second, I present our work on AMULET and how we successfully built a wearable health hub that can run multiple health applications, provide strong security properties, and operate on a single charge for weeks or even months at a time. I present the design and evaluation of our highly efficient event-driven programming model, the design of our low-power operating system, and developer tools for profiling ultra-low-power applications at compile time.

Third, I present a new approach (VIA) that helps devices at the center of WPANs (e.g., smartphones) to verify the authenticity of interactions with other devices. This work builds on past work in anomaly detection techniques and shows how these techniques can be applied to Bluetooth network traffic. Specifically, we show how to create normality models based on fine- and course-grained insights from network traffic, which can be used to verify the authenticity of future interactions.

Light Commands: Laser-Based Audio Injection Attacks on Voice-Controllable Systems

A new THaW paper was published at USENIX Security last week. It describes using a laser at a distance of 110 meters to stimulate audio sensors on smart speakers and thereby insert audio commands that are accepted as coming from a legitimate user. Techniques for dealing with this vulnerability are proposed.

Takeshi Sugawara, Benjamin Cyr, Sara Rampazzi, Daniel Genkin, and Kevin Fu. Light Commands: Laser-Based Audio Injection Attacks on Voice-Controllable Systems. In Proceedings of the USENIX Security Symposium (USENIX Security), pages 2631–2648, August 2020. USENIX Association.

Paper and video presentation at https://www.usenix.org/conference/usenixsecurity20/presentation/sugawara 

THaW graduates: where are they now?

As the THaW project draws to a close, we are proud to recognize the many students and postdocs who were involved in THaW research over the years. As noted below, they have moved on to positions in academia or industry. Unless otherwise noted, each is a PhD. (Please send any corrections or additions to David Kotz at info@thaw.org.)

Cybersecurity and Privacy Implications of Contact Tracing

Two THaW researchers participated as panelists in a recent online panel discussion about contact tracing, with an emphasis on the security and privacy aspects. The video is now available.

“The coronavirus pandemic has highlighted the need for contact tracing, an effort to retroactively discover and inform all the persons who had recent contact with an infected person. Traditional methods are labor-intensive and inherently limited by human memory. Smartphone apps have been proposed to proactively record contacts, for retrospective notifications to those who may have been proximate to someone later discovered to be infected. There are, however, inherent privacy and cybersecurity risks posed by such technologies, and the same technologies could be abused for purposes other than public health. It is thus essential for contact tracing technologies to be designed and deployed with the utmost care and transparency.”