11,526 research outputs found
Design and evaluation of a person-centric heart monitoring system over fog computing infrastructure
Heart disease and stroke are becoming the leading cause of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that helps physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECG have limited accuracy and rely on external resources to analyze the signal and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyze the signal and identify abnormal behavior. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud and (c) the overall power consumption. Based on this concept, the HEART platform is presented that combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate and instant monitoring of the heart. The performance of the system is evaluated concerning the accuracy of detecting abnormal events and the power consumption of the wearable device. Results indicate that a very high percentage of success can be achieved in terms of event detection ratio and the device being operative up to a several days without the need for a recharge
Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results
In this paper, automated user verification techniques for smartphones are
investigated. A unique non-commercial dataset, the University of Maryland
Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication
research is introduced. This paper focuses on three sensors - front camera,
touch sensor and location service while providing a general description for
other modalities. Benchmark results for face detection, face verification,
touch-based user identification and location-based next-place prediction are
presented, which indicate that more robust methods fine-tuned to the mobile
platform are needed to achieve satisfactory verification accuracy. The dataset
will be made available to the research community for promoting additional
research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201
PowerSpy: Location Tracking using Mobile Device Power Analysis
Modern mobile platforms like Android enable applications to read aggregate
power usage on the phone. This information is considered harmless and reading
it requires no user permission or notification. We show that by simply reading
the phone's aggregate power consumption over a period of a few minutes an
application can learn information about the user's location. Aggregate phone
power consumption data is extremely noisy due to the multitude of components
and applications that simultaneously consume power. Nevertheless, by using
machine learning algorithms we are able to successfully infer the phone's
location. We discuss several ways in which this privacy leak can be remedied.Comment: Usenix Security 201
Fingerprinting Smart Devices Through Embedded Acoustic Components
The widespread use of smart devices gives rise to both security and privacy
concerns. Fingerprinting smart devices can assist in authenticating physical
devices, but it can also jeopardize privacy by allowing remote identification
without user awareness. We propose a novel fingerprinting approach that uses
the microphones and speakers of smart phones to uniquely identify an individual
device. During fabrication, subtle imperfections arise in device microphones
and speakers which induce anomalies in produced and received sounds. We exploit
this observation to fingerprint smart devices through playback and recording of
audio samples. We use audio-metric tools to analyze and explore different
acoustic features and analyze their ability to successfully fingerprint smart
devices. Our experiments show that it is even possible to fingerprint devices
that have the same vendor and model; we were able to accurately distinguish
over 93% of all recorded audio clips from 15 different units of the same model.
Our study identifies the prominent acoustic features capable of fingerprinting
devices with high success rate and examines the effect of background noise and
other variables on fingerprinting accuracy
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