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 TypePaper
AuthorsXue, Wanli (Author), Luo, Chengwen (Author), Rana, Rajib (Author), Hu, Wen (Author) and Seneviratne, Aruna (Author)
Journal or Proceedings TitleProceedings of the 14th ACM Conference on Embedded Network Sensor Systems (SenSys '16)
Journal Citationpp. 286-287
Number of Pages2
Year2016
ISBN9781450342636
Digital Object Identifier (DOI)https://doi.org/10.1145/2994551.2996525
Web Address (URL) of Paperhttps://dl.acm.org/doi/10.1145/2994551.2996525
Web Address (URL) of Conference Proceedingshttps://dl.acm.org/doi/proceedings/10.1145/2994551
Conference/Event14th 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.

Keywordscompressive sensing; security; encryption; Internet of Things
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
Public Notes

Files associated with this item cannot be displayed due to copyright restrictions.

Byline AffiliationsUniversity of New South Wales
Shenzhen University, China
Institute for Resilient Regions
Institution of OriginUniversity of Southern Queensland
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