Demo Abstract: CScrypt - A Compressive-Sensing-Based Encryption Engine for the Internet of Things

Paper

Xue, Wanli, Luo, Chengwen, Rana, Rajib, Hu, Wen and Seneviratne, Aruna. 2016. "Demo Abstract: CScrypt - A Compressive-Sensing-Based Encryption Engine for the Internet of Things." 14th ACM Conference on Embedded Network Sensor Systems (SenSys '16). Stanford, Calif, USA 14 - 16 Nov 2016 https://doi.org/10.1145/2994551.2996525

Paper/Presentation Title	Demo Abstract: CScrypt - A Compressive-Sensing-Based Encryption Engine for the Internet of Things
Presentation Type	Paper
Authors	Xue, Wanli (Author), Luo, Chengwen (Author), Rana, Rajib (Author), Hu, Wen (Author) and Seneviratne, Aruna (Author)
Journal or Proceedings Title	Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems (SenSys '16)
Journal Citation	pp. 286-287
Number of Pages	2
Year	2016
ISBN	9781450342636
Digital Object Identifier (DOI)	https://doi.org/10.1145/2994551.2996525
Web Address (URL) of Paper	https://dl.acm.org/doi/10.1145/2994551.2996525
Web Address (URL) of Conference Proceedings	https://dl.acm.org/doi/proceedings/10.1145/2994551
Conference/Event	14th ACM Conference on Embedded Network Sensor Systems (SenSys '16)
Event Details	14th ACM Conference on Embedded Network Sensor Systems (SenSys '16) Event Date 14 to end of 16 Nov 2016 Event Location Stanford, Calif, USA
Abstract	Internet of Things (IoT) have been connecting the physi- cal world seamlessly and provides tremendous opportunities to a wide range of applications. However, potential risks exist when IoT system collects local sensor data and up- loads to the Cloud. The private data leakage can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this demo, we solve this problem of guaranteeing the user data privacy and secu- rity using compressive sensing based cryptographic method. We present CScrypt, a compressive-sensing-based encryp- tion engine for the Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Our sys- tem exploits the fact that each individual's biometric data can be trained to a unique dictionary which can be used as an encryption key meanwhile to compress the original data. We will demonstrate a functioning prototype of our system using live data stream when attending the conference.
Keywords	compressive sensing; security; encryption; Internet of Things
Contains Sensitive Content	Does not contain sensitive content
ANZSRC Field of Research 2020	461199. Machine learning not elsewhere classified
Public Notes	Files associated with this item cannot be displayed due to copyright restrictions.
Byline Affiliations	University of New South Wales
	Shenzhen University, China
	Institute for Resilient Regions
Institution of Origin	University of Southern Queensland

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