Weitao Xu

Secure key generation and distribution protocol for wearable devices

Smart wearable devices have enormous applications in todays world and hence their usage is increasing significantly. As these devices communicate using wireless medium, the communication must be protected from eavesdropping by using shared secret keys for data encryption. In many applications, it is essential to use a common secret key for secured communication among multiple devices. In this paper, we present our novel secret key generation and distribution protocol exploiting accelerometer data collected from smart wearable devices. We propose (i) source separation method for processing accelerometer sensor data, and (ii) key distribution protocol based on Fuzzy vault. Our scheme is information theoretically secure and our experimental results show that the maximum key generation rate of our scheme is 50 bps which is suitable for practical applications.

Transportation mode detection using kinetic energy harvesting wearables

Detecting the transportation mode of an individuals everyday travel provides useful information in urban design, real-time journey planning, and activity monitoring. In existing systems, accelerometer and GPS are the dominantly used signal sources which quickly drain the limited battery life of the wearable devices. In this paper, we investigate the feasibility of using the output voltage from the kinetic energy harvesting device as the signal source to achieve transportation mode detection. The proposed idea is based on the intuition that the vibrations experienced by the passenger during motoring of different transportation modes are different. Thus, voltage generated by the energy harvesting devices should contain distinctive features to distinguish different transportation modes. Using the dataset collected from a real energy harvesting device, we present the initial demonstration of the proposed method. We can achieve 98.84% of accuracy in determining whether the user is traveling by pedestrian or motorized modes, and in a fine-grained classification of three different motorized modes (car, bus, and train), an overall accuracy over 85% is achieved.

Gait-Watch: A Context-aware Authentication System for Smart Watch Based on Gait Recognition

With recent advances in mobile computing and sensing technology, smart wearable devices have pervaded our everyday lives. The security of these wearable devices is becoming a hot research topic because they store various private information. Existing approaches either only rely on a secret PIN number or require an explicit user authentication process. In this paper, we present Gait-watch, a context-aware authentication system for smart watch based on gait recognition. We address the problem of recognizing the user under various walking activities (e.g., walking normally, walking with calling the phone), and propose a sparse fusion method to improve recognition accuracy. Extensive evaluations show that Gait-watch improves recognition accuracy by up to 20% by leveraging the activity information, and the proposed sparse fusion method is 10% better than several state-of-the-art gait recognition methods. We also report a user study to demonstrate that Gait-watch can accurately authenticate the user in real world scenarios and require low system cost.

Sensor-assisted face recognition system on smart glass via multi-view sparse representation classification

Face recognition is one of the most popular research problems on various platforms. New research issues arise when it comes to resource constrained devices, such as smart glasses, due to the overwhelming computation and energy requirements of the accurate face recognition methods. In this paper, we propose a robust and efficient sensor-assisted face recognition system on smart glasses by exploring the power of multimodal sensors including the camera and Inertial Measurement Unit (IMU) sensors. The system is based on a novel face recognition algorithm, namely Multi-view Sparse Representation Classification (MVSRC), by exploiting the prolific information among multi-view face images. To improve the efficiency of MVSRC on smart glasses, we propose a novel sampling optimization strategy using the less expensive inertial sensors. Our evaluations on public and private datasets show that the proposed method is up to 10% more accurate than the state-of-the-art multi-view face recognition methods while its computation cost is in the same order as an efficient benchmark method (e.g., Eigenfaces). Finally, extensive real-world experiments show that our proposed system improves recognition accuracy by up to 15% while achieving the same level of system overhead compared to the existing face recognition system (OpenCV algorithms) on smart glasses.

Gait-Key: A Gait-Based Shared Secret Key Generation Protocol for Wearable Devices

Recent years have witnessed a remarkable growth in the number of smart wearable devices. For many of these devices, an important security issue is to establish an authenticated communication channel between legitimate devices to protect the subsequent communications. Due to the wireless nature of the communication and the extreme resource constraints of sensor devices, providing secure, efficient, and user-friendly device pairing is a challenging task. Traditional solutions for device pairing mostly depend on key predistribution, which is unsuitable for wearable devices in many ways. In this article, we design Gait-Key, a shared secret key generation scheme that allows two legitimate devices to establish a common cryptographic key by exploiting users’ walking characteristics (gait). The intuition is that the sensors on different locations on the same body experience similar accelerometer signals when the user is walking. However, one main challenge is that the accelerometer also captures motion signals produced by other body parts (e.g., swinging arms). We address this issue by using the blind source separation technique to extract the informative signal produced by the unique gait patterns. Our experimental results show that Gait-Key can generate a common 128-bit key for two legitimate devices with 98.3% probability. To demonstrate the feasibility, the proposed key generation scheme is implemented on modern smartphones. The evaluation results show that the proposed scheme can run in real time on modern mobile devices and incurs low system overhead.

Sensor-Assisted Multi-View Face Recognition System on Smart Glass

Face recognition is a hot research topic with a variety of application possibilities, including video surveillance and mobile payment. It has been well researched in traditional computer vision community. However, new research issues arise when it comes to resource constrained devices, such as smart glasses, due to the overwhelming computation and energy requirements of the accurate face recognition methods. In this paper, we propose a robust and efficient sensor-assisted face recognition system on smart glasses by exploring the power of multimodal sensors including the camera and Inertial Measurement Unit (IMU) sensors. The system is based on a novel face recognition algorithm, namely Multi-view Sparse Representation Classification (MVSRC), by exploiting the prolific information among multi-view face images. To improve the efficiency of MVSRC on smart glasses, we propose two novel sampling optimization strategies using the less expensive inertial sensors. Our evaluations on public and private datasets show that the proposed method is up to 10 percent more accurate than the state-of-the-art multi-view face recognition methods while its computation cost is the same order as an efficient benchmark method (e.g., Eigenfaces). Finally, extensive real-world experiments show that our proposed system improves recognition accuracy by up to 15 percent while achieving the same level of system overhead compared to the existing face recognition system (OpenCV algorithms) on smart glasses.

Poster: An Online Approach for Gait Recognition on Smart Glasses

With the fast development and increasing population of the wearable devices involves in our daily life, the security of the privacy information on those devices is attracting significant attentions. One of the possible solution is to enable the devices to recognise the real owner with authentication system. Biometrics recognition is popular used for authentication systems. The biometrics used including faces, fingerprints, gait cycles and etc. Using gait cycles as the criteria for identities recognition is superior than other biometrics as the gait information can be collected by the IMU sensors which are most popular embedded on portable devices and they cannot be reproduced by the invaders. We propose, Securitas, the continuous authentication system exploits the information from IMU sensors on the smart glasses to distinguish different wearers.

Mobile Applications Based on Smart Wearable Devices

Ubiquity of wearable devices sparked a new set of mobile computing applications that leverage the prolific information of sensors. I will focus on two main research questions: face recognition on smart glass and gait recognition on smart watch. Face recognition is one of the most popular research problems on various platforms. New research issues arise when it comes to resource constrained devices, such as smart glasses, due to the overwhelming computation and energy requirements of the accurate face recognition methods. Biometric gait recognition refers to verifying or identifying persons by their walking style, and it provides a unobtrusive way to authenticate the user and unlock the smart watches.

VEH-COM: Demodulating vibration energy harvesting for short range communication

This paper investigates the possibility of using a vibration energy harvesting (VEH) device as a communication receiver. By modulating the ambient vibration energy using a transmitting speaker, and demodulating the harvested power at the receiving VEH, we aim to transmit small amounts of data at low rates between two proximate devices. The key advantage of using VEH as a receiver is that the modulated sound waves can be successfully demodulated directly from the harvested power without employing the power-consuming digital signal processing (DSP), which makes a VEH receiver significantly more power efficient than a conventional microphone-based decoder. To address the extremely narrow bandwidth of VEH, we design a simple ON-OFF keying modulation, but optimized for VEH hardware. Experiments with a real VEH device shows that, at a distance of 2 cm, a laptop speaker with the proposed modulation scheme can achieve 30 bps communication for a target bit error rate of less than 1%, which would enable many emerging short range applications, such as mobile payment. The communication range of a laptop can be extended to 80 cm for 5 bps, allowing a range of other audio-based device-to-device communications, such as a web advertisement on a laptop browser transferring tokens to a nearby smartphone. We also demonstrate that the proposed VEH-based sound decoding is resilient to background noise, thanks to its extremely narrow power harvesting bandwidth, which works as a natural noise filter.

Accelerometer and Fuzzy Vault-Based Secure Group Key Generation and Sharing Protocol for Smart Wearables

The increased usage of smart wearables in various applications, specifically in health-care, emphasizes the need for secure communication to transmit sensitive health-data. In a practical scenario, where multiple devices are carried by a person, a common secret key is essential for secure group communication. Group key generation and sharing among wearables have received very little attention in the literature due to the underlying challenges: 1) difficulty in obtaining a good source of randomness to generate strong cryptographic keys, and 2) finding a common feature among all the devices to share the key. In this paper, we present a novel solution to generate and distribute group secret keys by exploiting on-board accelerometer sensor and the unique walking style of the user, i.e., gait. We propose a method to identify the suitable samples of accelerometer data during all routine activities of a subject to generate the keys with high entropy. In our scheme, the smartphone placed on waist employs fuzzy vault, a cryptographic construct, and utilizes the acceleration due to gait, a common characteristic extracted on all wearable devices to share the secret key. We implement our solution on commercially available off-the-shelf smart wearables, measure the system performance, and conduct experiments with multiple subjects. Our results demonstrate that the proposed solution has a bit rate of 750 b/s, low system overhead, distributes the key securely and quickly to all legitimate devices, and is suitable for practical applications.