Next-Generation Consumer Electronics Data Auditing Scheme Toward Cloud-Edge Distributed and Resilient Machine Learning

Distributed and resilient machine learning (DRML) endues next-generation consumer electronics with AI function. Intuitively, AI provides innovative, humanized, convenient applications based on the data extended by next-generation consumer electronics. Cloud-edge computing is an ideal undertaken arch...

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Bibliographic Details
Published in:IEEE transactions on consumer electronics 2024-02, Vol.70 (1), p.2244-2256
Main Authors: Li, Yi, Shen, Jian, Vijayakumar, Pandi, Lai, Chin-Feng, Sivaraman, Audithan, Sharma, Pradip Kumar
Format: Article
Language:English
Subjects:
Artificial intelligence
blockchain
Consumer electronics
data auditing
Data integrity
distributed and resilient machine learning (DRML)
Edge computing
Electronics
Integrity
Machine learning
Next generation networking
Next-generation consumer electronics
Privacy
Security
Servers
Verification
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Summary:Distributed and resilient machine learning (DRML) endues next-generation consumer electronics with AI function. Intuitively, AI provides innovative, humanized, convenient applications based on the data extended by next-generation consumer electronics. Cloud-edge computing is an ideal undertaken architecture of DRML due to its distributed property. However, as one of the core elements driving AI applications, data could be lost or corrupted owing to damaged electronics, unstable communication, and even cloud providers' malicious behavior. It is essential to ensure the data integrity of next-generation electronics before AI applications. To this end, we proposed a privacy-protection distributed data auditing scheme for cloud-edge DRML. An efficient data integrity verification method that only uses algebraic operation is constructed. Then, Function Secret Share (FSS) extends the data integrity verification method to protect consumer privacy. Besides, a consensus for data auditing results is designed among the edge servers. Finally, we present an abundance of theoretical analyses and experimental findings to substantiate and validate the efficiency and effectiveness of our proposed scheme.
ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2024.3368206