| dc.description.abstract | Internet of things (IoT) devices are becoming ubiquitous in, and even essential to, many aspects of day-to-day life, from fitness trackers, pacemakers, to industrial control systems. On a larger scale, live stream of sleep patterns data recorded via fitness tracker devices was utilized to quantify the effect of a seismic activity on sleep. While the benefits of IoT are undeniable, IoT ecosystem comes with its own set of system vulnerabilities that include malicious actors manipulating the flow of information to and from the IoT devices, which can lead to the capture of sensitive data and loss of data privacy. My thesis explores a Privacy-Preserving ECG based Active Authentication (PPEA2) scheme that is deployable on power-limited wearable systems to counter these vulnerabilities.
Electrocardiogram (ECG) is a record of the electrical activity of the heart, and it has been shown to be unique for every person. This work leverages that idea to design a feature extraction followed by an authentication scheme based on the extracted features. The proposed scheme preserves the privacy of the extracted features by employing a light-weight secure computation approach based on secure weighted hamming distance computation from an oblivious transfer. It computes a joint set between two participating entities without revealing the keys to either of them. | en |
