Next Article in Journal / Special Issue
Who Is at Risk for Problematic Video Gaming? Risk Factors in Problematic Video Gaming in Clinically Referred Canadian Children and Adolescents
Previous Article in Journal
When Augmented Reality Met Art: Lessons Learned from Researcher–Artist Interdisciplinary Work
Previous Article in Special Issue
Webometrics: Some Critical Issues of WWW Size Estimation Methods
Open AccessArticle

Enhancing Privacy in Wearable IoT through a Provenance Architecture

Department of Information Sciences and Technology (IST), Pennsylvania State University, Beaver Campus, Monaca, PA 15061, USA
Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
Author to whom correspondence should be addressed.
Multimodal Technol. Interact. 2018, 2(2), 18;
Received: 23 February 2018 / Revised: 19 April 2018 / Accepted: 20 April 2018 / Published: 23 April 2018
(This article belongs to the Special Issue Interactive Web)
The Internet of Things (IoT) is inspired by network interconnectedness of humans, objects, and cloud services to facilitate new use cases and new business models across multiple enterprise domains including healthcare. This creates the need for continuous data streaming in IoT architectures which are mainly designed following the broadcast model. The model facilitates IoT devices to sense and deliver information to other nodes (e.g., cloud, physical objects, etc.) that are interested in the information. However, this is a recipe for privacy breaches since sensitive data, such as personal vitals from wearables, can be delivered to undesired sniffing nodes. In order to protect users’ privacy and manufacturers’ IP, as well as detecting and blocking malicious activity, this research paper proposes privacy-oriented IoT architecture following the provenance technique. This ensures that the IoT data will only be delivered to the nodes that subscribe to receive the information. Using the provenance technique to ensure high transparency, the work is able to provide trace routes for digital audit trail. Several empirical evaluations are conducted in a real-world wearable IoT ecosystem to prove the superiority of the proposed work. View Full-Text
Keywords: Internet of Things (IoT), sensors; mobile devices; middleware; wearables; provenance; privacy Internet of Things (IoT), sensors; mobile devices; middleware; wearables; provenance; privacy
Show Figures

Figure 1

MDPI and ACS Style

Lomotey, R.K.; Sofranko, K.; Orji, R. Enhancing Privacy in Wearable IoT through a Provenance Architecture. Multimodal Technol. Interact. 2018, 2, 18.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop