Granular content distribution for IoT remote sensing data supporting privacy preservation

Zhang, X., Zhang, G., Huang, X. and Poslad, S. (2022) Granular content distribution for IoT remote sensing data supporting privacy preservation. Remote Sensing, 14(21), 5574. (doi: 10.3390/rs14215574)

Abstract

Facilitated by the Internet of Things (IoT) and diverse IoT devices, remote sensing data are evolving into the multimedia era with an expanding data scale. Massive remote sensing data are collected by IoT devices to monitor environments and human activities. Because IoT devices are involved in the data collection, there are probably private data contained in the collected remote sensing data, such as the device owner information and the precise location. Therefore, when data analysts, researchers, and other stakeholders require remote sensing data from numerous IoT devices for different analyses and investigations, how to distribute massive remote sensing data efficiently and regulate different people to view different parts of the distributed remote sensing data is a challenge to be addressed. Many general solutions rely on granular access control for content distribution but do not consider the low computational efficiency caused by the huge file size of the remote sensing data or certain IoT devices only have a constrained computational performance. Therefore, we propose a new granular content distribution scheme, which is more lightweight and practical for the distribution of multimedia remote sensing data with the consideration of the large data size to avoid complicated operations to the data. Furthermore, a dual data integrity check (hash summary and watermark) designed in our scheme can detect tampering or forgery from encrypted remote sensing data before decrypting it and validate it again after decryption. The security analyses and experimental results manifest that our new scheme can maintain high computational efficiency and block tampering and forgery during the granular content distribution for IoT remote sensing data.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhang, Mr Xiaoshuai
• Authors: Zhang, X., Zhang, G., Huang, X., and Poslad, S.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Remote Sensing
• Publisher: MDPI
• ISSN: 2072-4292
• ISSN (Online): 2072-4292
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Remote Sensing 14(21):5574
• Publisher Policy: Reproduced under a Creative Commons licence