Authentication Based on Physiological Sample Clauses

Authentication Based on Physiological. Features Poon et al. [38] conducted several experiments to show that Inter-Pulse-Intervals (IPI) of physiological features can be used for authentication in e-health networks. IPI is available for different kinds of bio-sensors that collect different kinds of physiological signals (e.g., ECG, PPG, heart sounds, blood pressure wave and blood flow). Xxxx et al. [39] studied the development of authentication techniques by using biometrics on mobile phones, and identified that physiological biometrics can provide high authentication accuracy. Xxxxx et al. [40] proposed a framework for biometric fusion based on a single acquisition device and multiple matching units. It demonstrated that even if a single matcher is weak, or degrades its performance in presence of hostile en- vironmental conditions, different matchers can provide complementary information to improve the authentica- tion accuracy. Miao et al. [41] proposed a single-window Fourier transform scheme to improve the identification performance of generated entity identifiers based on physiological features. Xxxx et al. [42] introduced cross correlation to make fast authentication based on ECG signals in mobile and wearable devices with low false acceptance rate and false rejection rate. [43] analyzed that the extracted ECG features from time domain and frequency domain can be used for authentication. Time domain features include IPIs, statistics, Xxxxxx features, Non-Stationary index, fractal dimension and high order crossings of the physiological signal. Since severe channel interference may affect the signal strength and the accuracy of the negotiated keys [45], most of the channel-based key agreement schemes can- not be applied to mobile crowded environment, which is a significant application scenario in BANs. Existing authentication technologies based on physiological fea- tures establish strong basis for secure communication between bio-sensors, which enable the physiological- feature-based key agreement scheme a promising solu- tion. However, existing physiological-feature-based key agreement schemes can only negotiate session keys for bio-sensors that collect the same kind of physiological features, which is not feasible for bio-sensors that col- lect different kinds of physiological features. In addi- tion, existing physiological-feature-based key agreement schemes cannot provide session keys with a determinis- tic security level. Therefore, an authenticated key agree- ment scheme with high fle...