Imperial College London


Faculty of MedicineDepartment of Surgery & Cancer

Senior Lecturer



+44 (0)20 7594 0806benny.lo Website




B414BBessemer BuildingSouth Kensington Campus






BibTex format

author = {Sun, Y and Wong, C and Yang, GZ and Lo, B},
doi = {10.1109/BSN.2017.7936042},
pages = {206--210},
publisher = {IEEE},
title = {Secure key generation using gait features for Body Sensor Networks},
url = {},
year = {2017}

RIS format (EndNote, RefMan)

AB - 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.
AU - Sun,Y
AU - Wong,C
AU - Yang,GZ
AU - Lo,B
DO - 10.1109/BSN.2017.7936042
EP - 210
PY - 2017///
SP - 206
TI - Secure key generation using gait features for Body Sensor Networks
UR -
UR -
ER -