Secure key generation using gait features for Body Sensor Networks

Abstract

Abstract-With increasing popularity of wearable and Body Sensor Networks technologies, there is a growing concern on the security and data protection of such low-power pervasive devices. With very limited computational power, BSN sensors often cannot provide the necessary data protection to collect and process sensitive personal information. Since conventional network security schemes are too computationally demanding for miniaturized BSN sensors, new methods of securing BSNs have proposed, in which Biometric Cryptosystem (BCS) appears to be an effective solution. With regards to BCS security solutions, physiological traits, such as an individual's face, iris, fingerprint, electrocardiogram (ECG), and photoplethysmogram (PPG) have been widely exploited. However, behavioural traits such as gait are rarely studied. In this paper, a novel lightweight symmetric key generation scheme based on the timing information of gait is proposed. By extracting similar timing information from gait acceleration signals simultaneously from body worn sensors, symmetric keys can be generated on all the sensor nodes at the same time. Based on the characteristics of generated keys and BSNs, a fuzzy commitment based key distribution scheme is also developed to distribute the keys amongst the sensor nodes.

Security Properties of Gait for Mobile Device Pairing

“…The protocol exploits the acceleration along the z-axis and concatenates the key sequence as gray-coded, scaled and rounded IPIs. As reported in [12] (cf. Table 2 in Section 5.1) the security and inter-class similarity depends on the speed of consecutive steps and steplength.…”

“…Careful choice of the parameters is therefore demanded to limit the advantage gained by an adversary through the use of fuzzy cryptography. From the protocols we investigated, BANDANA [11] and the IPI-protocol [12] employ BCH codes for error correction. [16] integrates the fuzzy vault design that operates on order-invariant tuples generated by Walkie-Talkie.…”

Demo of BANDANA - Body Area Network Device-to-device Authentication using Natural gAit

“…Recently proposed protocols for ad-hoc pairing of devices co-present on the same body [3]- [7] leverage sensor data to form characteristic sequences, called fingerprints, in each wearable device. These fingerprints can be collated with a template database to identify legitimate users or detect impostors.…”

“…The existing approaches [3]- [7] utilized the correlation of movement data to generate fingerprints. It is observed that fingerprints on devices carried or worn by the same individual are more similar than those attached to distinct users.…”

“…Acceleration amplitudes are then quantized as binary sequences based on whether they are lower or higher than a threshold region. The Inter-Pulse-Interval protocol [7] exploits the random residual by which individual steps (left and right) differ from the mean gait cycle in time domain. The key is formed from first bits of the graycode representing gait fingerprints.…”