Reference : Your Moves, Your Device: Establishing Behavior Profiles Using Tensors
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/32825
Your Moves, Your Device: Establishing Behavior Profiles Using Tensors
English
Falk, Eric mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Charlier, Jérémy Henri J. mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
State, Radu mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Nov-2017
Advanced Data Mining and Applications - 13th International Conference, ADMA 2017
460-474
Yes
No
International
978-3-319-69178-7
Advanced Data Mining and Applications - 13th International Conference, ADMA 2017
from 05-11-2017 to 06-11-2017
[en] Smartphones became a person's constant companion. As the strictly personal devices they are, they gradually enable the replacement of well established activities as for instance payments, two factor authentication or personal assistants. In addition, Internet of Things (IoT) gadgets extend the capabilities of the latter even further. Devices such as body worn fitness trackers allow users to keep track of daily activities by periodically synchronizing data with the smartphone and ultimately with the vendor's computational centers in the cloud. These fitness trackers are equipped with an array of sensors to measure the movements of the device, to derive information as step counts or make assessments about sleep quality. We capture the raw sensor data from wrist-worn activity trackers to model a biometric behavior profile of the carrier. We establish and present techniques to determine rather the original person, who trained the model, is currently wearing the bracelet or another individual. Our contribution is based on CANDECOMP/PARAFAC (CP) tensor decomposition so that computational complexity facilitates: the execution on light computational devices on low precision settings, or the migration to stronger CPUs or to the cloud, for high to very high granularity. This precision parameter allows the security layer to be adaptable, in order to be compliant with the requirements set by the use cases. We show that our approach identifies users with high confidence.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN)
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/32825

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