Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00073 | 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) |
Keywords | DocType | ISBN |
privacy architecture IoT deep learning,PUF | Conference | 978-1-7281-4035-3 |
Citations | PageRank | References |
0 | 0.34 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ismini Psychoula | 1 | 14 | 3.05 |
| Liming Chen | 2 | 2607 | 201.71 |
| Xuanxia Yao | 3 | 92 | 5.65 |
| Huansheng Ning | 4 | 847 | 83.48 |