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A Lossless Data-Hiding based IoT Data Authenticity Model in Edge-AI for Connected Living

Published:06 December 2021Publication History
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Abstract

Edge computing is an emerging technology for the acquisition of Internet-of-Things (IoT) data and provisioning different services in connected living. Artificial Intelligence (AI) powered edge devices (edge-AI) facilitate intelligent IoT data acquisition and services through data analytics. However, data in edge networks are prone to several security threats such as external and internal attacks and transmission errors. Attackers can inject false data during data acquisition or modify stored data in the edge data storage to hamper data analytics. Therefore, an edge-AI device must verify the authenticity of IoT data before using them in data analytics. This article presents an IoT data authenticity model in edge-AI for a connected living using data hiding techniques. Our proposed data authenticity model securely hides the data source’s identification number within IoT data before sending it to edge devices. Edge-AI devices extract hidden information for verifying data authenticity. Existing data hiding approaches for biosignal cannot reconstruct original IoT data after extracting the hidden message from it (i.e., lossy) and are not usable for IoT data authenticity. We propose the first lossless IoT data hiding technique in this article based on error-correcting codes (ECCs). We conduct several experiments to demonstrate the performance of our proposed method. Experimental results establish the lossless property of the proposed approach while maintaining other data hiding properties.

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      • Published in

        cover image ACM Transactions on Internet Technology
        ACM Transactions on Internet Technology  Volume 22, Issue 3
        August 2022
        631 pages
        ISSN:1533-5399
        EISSN:1557-6051
        DOI:10.1145/3498359
        • Editor:
        • Ling Liu
        Issue’s Table of Contents

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        Publication History

        • Published: 6 December 2021
        • Accepted: 1 February 2021
        • Revised: 1 December 2020
        • Received: 1 October 2020
        Published in toit Volume 22, Issue 3

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