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

Qingzhi Liu, Yuchen Huang, Chenglu Jin, Xiaohan Zhou, Ying Mao, Cagatay Catal, Long Cheng

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

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 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.
Original languageEnglish
JournalACM Transactions on Multimedia Computing, Communications, and Applications
DOIs
Publication statusPublished - 8 Mar 2024

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