research-article

Differentially Private Tensor Train Deep Computation for Internet of Multimedia Things

Authors:

Nicholaus J. Gati,

Laurence T. Yang,

Mamoun AlazabAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 16, Issue 3s

Article No.: 95, Pages 1 - 20

https://doi.org/10.1145/3421276

Published: 31 December 2020 Publication History

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.

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Zhao HYang LYang ZLiu DNie XRen B(2024)Sparse Bayesian Tensor Completion for Data Recovery in Intelligent IoT SystemsIEEE Internet of Things Journal10.1109/JIOT.2024.337820211:15(25682-25693)Online publication date: 1-Aug-2024
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Index Terms

Differentially Private Tensor Train Deep Computation for Internet of Multimedia Things
1. Security and privacy
  1. Security services
    1. Privacy-preserving protocols
  2. Software and application security
    1. Domain-specific security and privacy architectures

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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 16, Issue 3s

Special Issue on Privacy and Security in Evolving Internet of Multimedia Things and Regular Papers

October 2020

190 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3444536

Editor:
Alberto Del Bimbo
University of Firenze, Italy

Issue’s Table of Contents

Copyright © 2020 ACM.

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Association for Computing Machinery

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Publication History

Published: 31 December 2020

Accepted: 01 August 2020

Revised: 01 August 2020

Received: 01 April 2019

Published in TOMM Volume 16, Issue 3s

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Cited By

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https://doi.org/10.3390/electronics13030578
Zhao HYang LYang ZLiu DNie XRen B(2024)Sparse Bayesian Tensor Completion for Data Recovery in Intelligent IoT SystemsIEEE Internet of Things Journal10.1109/JIOT.2024.337820211:15(25682-25693)Online publication date: 1-Aug-2024
https://doi.org/10.1109/JIOT.2024.3378202
Jing PCui KZhang JLi YSu Y(2024)Multimodal High-Order Relationship Inference Network for Fashion Compatibility Modeling in Internet of Multimedia ThingsIEEE Internet of Things Journal10.1109/JIOT.2023.328560111:1(353-365)Online publication date: 1-Jan-2024
https://doi.org/10.1109/JIOT.2023.3285601
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