research-article

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

Authors:

Mohammad Saidur Rahman,

Ibrahim Khalil,

Mohammed Atiquzzaman,

Elisa BertinoAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 22, Issue 3

Article No.: 57, Pages 1 - 25

https://doi.org/10.1145/3453171

Published: 06 December 2021 Publication History

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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Cited By

Luzolo PElrawashdeh ZTchappi IGalland SOutay F(2024)Combining multi-agent systems and Artificial Intelligence of Things: Technical challenges and gainsInternet of Things10.1016/j.iot.2024.101364(101364)Online publication date: Sep-2024
https://doi.org/10.1016/j.iot.2024.101364
Jenkins CPrasad P(2024)Edge-AI Addresses the Big Data Challenges of IoT in the Cloud EdgeInnovative Technologies in Intelligent Systems and Industrial Applications10.1007/978-3-031-71773-4_11(171-180)Online publication date: 30-Dec-2024
https://doi.org/10.1007/978-3-031-71773-4_11
Wang HZhou Y(2023)Combination of Artificial Intelligence with Mergers and AcquisitionsBCP Business & Management10.54691/bcpbm.v39i.406939(235-241)Online publication date: 22-Feb-2023
https://doi.org/10.54691/bcpbm.v39i.4069
Show More Cited By

Index Terms

A Lossless Data-Hiding based IoT Data Authenticity Model in Edge-AI for Connected Living
1. Security and privacy
  1. Network security
    1. Security protocols
  2. Software and application security
    1. Domain-specific security and privacy architectures

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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
Georgia Institute of Technology, USA

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 December 2021

Accepted: 01 February 2021

Revised: 01 December 2020

Received: 01 October 2020

Published in TOIT Volume 22, Issue 3

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Cited By

Luzolo PElrawashdeh ZTchappi IGalland SOutay F(2024)Combining multi-agent systems and Artificial Intelligence of Things: Technical challenges and gainsInternet of Things10.1016/j.iot.2024.101364(101364)Online publication date: Sep-2024
https://doi.org/10.1016/j.iot.2024.101364
Jenkins CPrasad P(2024)Edge-AI Addresses the Big Data Challenges of IoT in the Cloud EdgeInnovative Technologies in Intelligent Systems and Industrial Applications10.1007/978-3-031-71773-4_11(171-180)Online publication date: 30-Dec-2024
https://doi.org/10.1007/978-3-031-71773-4_11
Wang HZhou Y(2023)Combination of Artificial Intelligence with Mergers and AcquisitionsBCP Business & Management10.54691/bcpbm.v39i.406939(235-241)Online publication date: 22-Feb-2023
https://doi.org/10.54691/bcpbm.v39i.4069
Mao BLiu JWu YKato N(2023)Security and Privacy on 6G Network Edge: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.324467425:2(1095-1127)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3244674

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