Tim Pierson’s dissertation work resulted in an innovative method for single-antenna Wi-Fi devices (like many mHealth devices, medical devices, or those in the IoT) to determine with strong confidence whether a Wi-Fi transmitter is close by (within a few centimeters). This proximity detector can be the basis for trustworthy relationships between devices. A poster paper about this idea just won the best-poster award at MobiCom 2018, and the full paper was just accepted for presentation at MobiCom 2019. See below for the abstract, or check out the corresponding three-page paper.
At the Joint Conference on Pervasive and Ubiquitous Computing conference, Ubicomp, David Kotz presented THaW’s work to develop a novel biometric approach to identifying and verifying who is wearing a device – an important consideration for a medical device that may be collecting diagnostic information that is fed into an electronic health record. Their novel approach is to use vocal resonance, i.e., the sound of your voice as it passes through bones and tissues, for a device to recognize its wearer and verify that it is physically in contact with the wearer… not just nearby. They implemented the method on a wearable-class computing device and showed high accuracy and low energy consumption.
Rui Liu, Cory Cornelius, Reza Rawassizadeh, Ron Peterson, and David Kotz. Vocal Resonance: Using Internal Body Voice for Wearable Authentication. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) (UbiComp), 2(1), March 2018. DOI 10.1145/3191751.
Abstract: We observe the advent of body-area networks of pervasive wearable devices, whether for health monitoring, personal assistance, entertainment, or home automation. For many devices, it is critical to identify the wearer, allowing sensor data to be properly labeled or personalized behavior to be properly achieved. In this paper we propose the use of vocal resonance, that is, the sound of the person’s voice as it travels through the person’s body – a method we anticipate would be suitable for devices worn on the head, neck, or chest. In this regard, we go well beyond the simple challenge of speaker recognition: we want to know who is wearing the device. We explore two machine-learning approaches that analyze voice samples from a small throat-mounted microphone and allow the device to determine whether (a) the speaker is indeed the expected person, and (b) the microphone-enabled device is physically on the speaker’s body. We collected data from 29 subjects, demonstrate the feasibility of a prototype, and show that our DNN method achieved balanced accuracy 0.914 for identification and 0.961 for verification by using an LSTM-based deep-learning model, while our efficient GMM method achieved balanced accuracy 0.875 for identification and 0.942 for verification.
In a recent Viewpoint article in JAMA, THaW member Kevin Fu explored a recent pacemaker vulnerability, and its ramifications for medical device security in general. In the post, he discusses both the full extent of the vulnerabilities, as well as the practical considerations to be taken as a result. To read the full text of the article, click the link below.
THaW researchers are showing off some cool research at this week’s MobiSys conference in Niagara Falls, with three papers at MobiSys workshops and a poster in the poster session.
- Aarathi Prasad and David Kotz. ENACT: Encounter-based Architecture for Contact Tracing. In ACM Workshop on Physical Analytics (WPA), pages 37-42, June 2017. ACM Press. DOI 10.1145/3092305.3092310.
- Rui Liu, Reza Rawassizadeh, and David Kotz. Toward Accurate and Efficient Feature Selection for Speaker Recognition on Wearables. InProceedings of the ACM Workshop on Wearable Systems and Applications (WearSys), pages 41-46, 2017. ACM Press. DOI 10.1145/3089351.3089352.
- Rui Liu, Cory Cornelius, Reza Rawassizadeh, Ron Peterson, and David Kotz. Poster: Vocal Resonance as a Passive Biometric. In Proceedings of the ACM International Conference on Mobile Systems, Applications, and Services (MobiSys), pages 160, 2017. ACM Press. DOI 10.1145/3081333.3089304.
- Xiaohui Liang and David Kotz. AuthoRing: Wearable User-presence Authentication. In Proceedings of the ACM Workshop on Wearable Systems and Applications (WearSys), pages 5-10, 2017. ACM Press. DOI 10.1145/3089351.3089357.
THaW Researchers Xiaohui Liang, Tianlong Yun, Ronald Peterson, and David Kotz have been researching new methods for connecting wearables to external screens. Their paper, LightTouch: Securely Connecting Wearables to Ambient Displays with User Intent, has been accepted to INFOCOM 2017. In it, they explore a security system that uses a screen’s brightness level to ensure secure connection between screen and device. Moreover, they also address additional screen-based counter measures that can be taken to further secure the protocol. For more information and to read the paper, click the link below.
THaW researcher Kevin Fu’s work on acoustic device hacking has recently been featured in the New York Times. The article discusses the team’s work on using acoustic signals to fool sensors in mobile device, and create the potential for security violations. For more information beyond the article, click here for a quick video, or read the complete paper below.
THaW researchers Haiming Jin and Klara Nahrstedt of UIUIC, in collaboration with Lu Su of SUNY Buffalo, recently had a paper accepted to IEEE INFOCOM 2017. Entitled CENTURION, the research explores the incentivization of participants in crowd sourced data collection. Notably, CENTURION rethinks the existing model of crowd sourced data collection (one consumer, one set of incentives), and instead takes the novel approach of applying a double auction model with multiple consumers and multiple incentives. The result is a system that can guarantee non-negative social welfare impact, among other benefits. To explore CENTURION further, click below.
THaW PhD student, Tim Pierson, along with the Wanda team have built a ‘magic wand’ that simplifies the integration of new medical devices into existing wireless networks. A detailed description of their work is found below in the abstract to their recently accepted IEEE INFOCOM paper.
Abstract: Nearly every setting is increasingly populated with wireless and mobile devices – whether appliances in a home, medical devices in a health clinic, sensors in an industrial setting, or devices in an office or school. There are three fundamental operations when bringing a new device into any of these settings: (1) to configure the device to join the wireless local-area network, (2) to partner the device with other nearby devices so they can work together, and (3) to configure the device so it connects to the relevant individual or organizational account in the cloud. The challenge is to accomplish all three goals simply, securely, and consistent with user intent. We present a novel approach we call Wanda – a `magic wand’ that accomplishes all three of the above goals – and evaluate a prototype implementation.
A prepublication version is available here.
Enormous numbers of mobile health applications (mHealth apps) developed recently on mobile devices (e.g. smart-phones, tablets, etc.) have enabled health status (e.g. sleep quality, heart rate, etc.) monitoring that is readily accessible to average mobile device users. Typically, such mHealth apps involve active usage of mobile device resources, such as on-board sensors, network bandwidth, etc. The rapid increase of these applications prompted the US FDA agency to put in place regulations on mHealth app risk assessment. But these existing and upcoming regulations have not yet been accompanied by a mobile auditing framework, which provides real-time monitoring of mHealth apps’ resource usage and triggers alerts to users if abnormal resource usage patterns are detected.
In this project, we develop a mobile auditing framework shown in the figure to the left (mAuditor Framework). The mAuditor runs as a separate process along with mHealth apps and other general purpose apps (e.g. Facebook, Gmail, etc.). The mAuditor consists of the profiler and the analyzer. The profiler collects the system trace and parse the trace if needed. The parsed trace is utilized by the analyzer, which analyzes the resource usage patterns and compare them with predefined configurations. mAuditor with its low-overhead and non-obtrusive design, monitors mHealth apps’ resource usage patterns in real-time and triggers alerts to users if abnormal resource usage patterns are detected.
This work is being spearheaded by Haiming Jin and supported by his colleagues at UIUC, Ting-yu Wang and Klara Nahrstedt.