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Multimodal Technologies Interact. 2018, 2(2), 18;

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.
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)
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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

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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

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Multimodal Technologies Interact. EISSN 2414-4088 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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