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Context-Dependent Implicit Authentication for Wearable Device User

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Document pages: 7 pages

Abstract: As market wearables are becoming popular with a range of services, includingmaking financial transactions, accessing cars, etc. that they provide based onvarious private information of a user, security of this information is becomingvery important. However, users are often flooded with PINs and passwords inthis internet of things (IoT) world. Additionally, hard-biometric, such asfacial or finger recognition, based authentications are not adaptable formarket wearables due to their limited sensing and computation capabilities.Therefore, it is a time demand to develop a burden-free implicit authenticationmechanism for wearables using the less-informative soft-biometric data that areeasily obtainable from the market wearables. In this work, we present acontext-dependent soft-biometric-based wearable authentication system utilizingthe heart rate, gait, and breathing audio signals. From our detailed analysis,we find that a binary support vector machine (SVM) with radial basis function(RBF) kernel can achieve an average accuracy of $0.94 pm 0.07$, $F 1$ score of$0.93 pm 0.08$, an equal error rate (EER) of about $0.06$ at a lowerconfidence threshold of 0.52, which shows the promise of this work.

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