research-article

Privacy and Integrity Protection for IoT Multimodal Data Using Machine Learning and Blockchain

Authors:

Long ChengAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 6

Article No.: 153, Pages 1 - 18

https://doi.org/10.1145/3638769

Published: 08 March 2024 Publication History

Abstract

With the wide application of Internet of Things (IoT) technology, large volumes of multimodal data are collected and analyzed for various diagnoses, analyses, and predictions to help in decision-making and management. However, the research on protecting data integrity and privacy is quite limited, while the lack of proper protection for sensitive data may have significant impacts on the benefits and gains of data owners. In this research, we propose a protection solution for data integrity and privacy. Specifically, our system protects data integrity through distributed systems and blockchain technology. Meanwhile, our system guarantees data privacy using differential privacy and Machine Learning (ML) techniques. Our system aims to maintain the usability of the data for further data analytical tasks of data users, while encrypting the data according to the requirements of data owners. We implement our solution with smart contracts, distributed file systems, and ML models. The experimental results show that our proposed solution can effectively encrypt source IoT data according to the requirements of data users while data integrity can be protected under the blockchain.

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Cited By

Singh AKundur DConti M(2024)Introduction to the Special Issue on Integrity of Multimedia and Multimodal Data in Internet of ThingsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364304020:6(1-4)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1145/3643040
Cui ZGuo ZLan YLi XDai H(2024)Incorporating Part of Speech Information in span representation for Named Entity RecognitionApplied Soft Computing10.1016/j.asoc.2024.111844163:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111844

Index Terms

Privacy and Integrity Protection for IoT Multimodal Data Using Machine Learning and Blockchain
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 6

June 2024

715 pages

EISSN:1551-6865

DOI:10.1145/3613638

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

Issue’s Table of Contents

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 the author(s) 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: 08 March 2024

Online AM: 06 January 2024

Accepted: 17 December 2023

Revised: 19 November 2023

Received: 02 May 2023

Published in TOMM Volume 20, Issue 6

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Cited By

Singh AKundur DConti M(2024)Introduction to the Special Issue on Integrity of Multimedia and Multimodal Data in Internet of ThingsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364304020:6(1-4)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1145/3643040
Cui ZGuo ZLan YLi XDai H(2024)Incorporating Part of Speech Information in span representation for Named Entity RecognitionApplied Soft Computing10.1016/j.asoc.2024.111844163:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111844

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