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Securing Cyber-Physical Social Interactions on Wrist-Worn Devices

Lookup NU author(s): Dr Bo WeiORCiD


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© 2020 ACM. Since ancient Greece, handshaking has been commonly practiced between two people as a friendly gesture to express trust and respect, or form a mutual agreement. In this article, we show that such physical contact can be used to bootstrap secure cyber contact between the smart devices worn by users. The key observation is that during handshaking, although belonged to two different users, the two hands involved in the shaking events are often rigidly connected, and therefore exhibit very similar motion patterns. We propose a novel key generation system, which harvests motion data during user handshaking from the wrist-worn smart devices such as smartwatches or fitness bands, and exploits the matching motion patterns to generate symmetric keys on both parties. The generated keys can be then used to establish a secure communication channel for exchanging data between devices. This provides a much more natural and user-friendly alternative for many applications, e.g., exchanging/sharing contact details, friending on social networks, or even making payments, since it doesn't involve extra bespoke hardware, nor require the users to perform pre-defined gestures. We implement the proposed key generation system on off-the-shelf smartwatches, and extensive evaluation shows that it can reliably generate 128-bit symmetric keys just after around 1s of handshaking (with success rate >99%), and is resilient to different types of attacks including impersonate mimicking attacks, impersonate passive attacks, or eavesdropping attacks. Specifically, for real-time impersonate mimicking attacks, in our experiments, the Equal Error Rate (EER) is only 1.6% on average. We also show that the proposed key generation system can be extremely lightweight and is able to run in-situ on the resource-constrained smartwatches without incurring excessive resource consumption.

Publication metadata

Author(s): Shen Y, Du B, Xu W, Luo C, Wei B, Cui L, Wen H

Publication type: Article

Publication status: Published

Journal: ACM Transactions on Sensor Networks

Year: 2020

Volume: 16

Issue: 2

Online publication date: 09/04/2020

Acceptance date: 01/01/2020

ISSN (print): 1550-4859

ISSN (electronic): 1550-4867

Publisher: ACM


DOI: 10.1145/3378669


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