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Title	A Privacy Aware Architecture For Iot Enabled Systems
ID_Doc	3778
Authors	Psychoula I.; Chen L.; Yao X.; Ning H.
Year	2019
Published	Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
DOI	http://dx.doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00073
Abstract	The Internet of Things has been used widely for the development of many different types of smart systems. But the constant monitoring of activities and behaviours and the collection along with the use of confidential data have led to increasing concerns about privacy which have indeed become one of the main barriers to technology acceptance and adoption. The development of new personalised technologies increases the use of sensitive data which should be kept hidden from third parties. The current methods are not sufficient and effective to assess and mitigate the significant privacy risks exposed by IoT and to support the design and adoption of privacy-aware smart systems. In this context, this paper proposes an architecture for privacy preservation which enables fine-grained control over data, anonymization and authentication. In particular, we introduce a privacy aware IoT architecture that utilises physical unclonable functions and deep learning to ensure the privacy of the collected IoT data. © 2019 IEEE.
Author Keywords	Architecture; Deep learning; IoT; Privacy; PUF

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