Differentially Private Tensor Train Deep Computation for Internet of Multimedia Things

Nicholaus J. Gati, Laurence Tianruo Yang, Jun Feng, Yijun Mo, Mamoun Alazab

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

The significant growth of the Internet of Things (IoT) takes a key and active role in healthcare, smart homes, smart manufacturing, and wearable gadgets. Due to complexness and difficulty in processing multimedia data, the IoT based scheme, namely Internet of Multimedia Things (IoMT) exists that is specialized for services and applications based on multimedia data. However, IoMT generated data are facing major processing and privacy issues. Therefore, tensor-based deep computation models proved a better platform to process IoMT generated data. A differentially private deep computation method working in the tensor space can attest to its efficacy for IoMT. Nevertheless, the deep computation model comprises a multitude of parameters; thus, it requires large units of memory and expensive computing units with higher performance levels, which hinders its performance for IoMT. Motivated by this, therefore, the paper proposes a deep private tensor train autoencoder (dPTTAE) technique to deal with IoMT generated data. Notably, the compression of weight tensors to manageable tensor train format is achieved through Tensor Train (TT) network. Moreover, TT format parameters are trained through higher-order back-propagation and gradient descent. We applied dPTTAE on three representative datasets. Comprehensive experimental evaluations and theoretical analysis show that dPTTAE enhances training time efficiency, and greatly improve memory utilization efficiency, attesting its potential for IoMT.

Original languageEnglish
Article number95
Pages (from-to)1-20
Number of pages20
JournalACM Transactions on Multimedia Computing, Communications and Applications
Volume16
Issue number3s
DOIs
Publication statusPublished - Jan 2021

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© 2020 ACM.

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Copyright 2021 Elsevier B.V., All rights reserved.

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