research-article

Practical Privacy-preserving High-order Bi-Lanczos in Integrated Edge-Fog-Cloud Architecture for Cyber-Physical-Social Systems

Authors:

Laurence T. Yang,

Ronghao ZhangAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 19, Issue 2

Article No.: 26, Pages 1 - 18

https://doi.org/10.1145/3230641

Published: 28 March 2019 Publication History

Abstract

Smart environments, also referred to as cyber-physical-social systems (CPSSs), are expected to significantly benefit from the integration of edge, fog, and cloud for intelligence service flexibility, efficiency, and cost saving. High-order Bi-Lanczos method has emerged as a powerful tool serving as multi-dimensional data processing, such as prevailing feature extraction, classification, and clustering of high-order data, in CPSSs. However, integrated edge-fog-cloud architecture is open and users have very limited control; how to carry out big data processing without compromising the security and privacy is a challenging issue in edge-fog-cloud-assisted smart applications. In this work, we propose a novel and practical privacy-preserving high-order Bi-Lanczos scheme in integrated edge-fog-cloud architectural paradigm for smart environments. More precisely, we first propose a privacy-preserving big data processing model using the synergy of edge, fog, and cloud. The proposed model enables edge, fog, and cloud to cooperatively complete big data processing without compromising users’ privacy for large-scale tensor data in CPSSs. Subsequently, making use of the model, we present a privacy-preserving high-order Bi-Lanczos scheme. Finally, we theoretically and empirically analyze the security and efficiency of the proposed privacy-preserving high-order Bi-Lanczos scheme based on an intelligent surveillance system case study. And the results demonstrate that the proposed scheme provides a privacy-preserving and efficient way of computations in integrated edge-fog-cloud paradigm for smart environments.

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Huang HXiao DLiang J(2024)Secure Low-complexity Compressive Sensing with Preconditioning Prior Regularization ReconstructionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363530820:4(1-22)Online publication date: 11-Jan-2024
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Wang XWang YYang JJia XLi LDing WWang F(2024)The survey on multi-source data fusion in cyber-physical-social systems: Foundational infrastructure for industrial metaverses and industries 5.0Information Fusion10.1016/j.inffus.2024.102321107(102321)Online publication date: Jul-2024
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Lv ZCheng CGuerrieri AFortino G(2023)Behavioral Modeling and Prediction in Social Perception and Computing: A SurveyIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.323021110:4(2008-2021)Online publication date: Aug-2023
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Index Terms

Practical Privacy-preserving High-order Bi-Lanczos in Integrated Edge-Fog-Cloud Architecture for Cyber-Physical-Social Systems
1. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools
    2. Computing platforms
2. 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 Internet Technology

ACM Transactions on Internet Technology Volume 19, Issue 2

Special Issue on Fog, Edge, and Cloud Integration

May 2019

288 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3322882

Editor:
Ling Liu
Georgia Institute of Technology, USA

Issue’s Table of Contents

Copyright © 2019 ACM.

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 ACM 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: 28 March 2019

Accepted: 01 May 2018

Revised: 01 April 2018

Received: 01 December 2017

Published in TOIT Volume 19, Issue 2

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Shenzhen Fundamental Research Program
Fundamental Research Funds for the Central Universities
National Key Research 8 Development Plan of China

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Cited By

Huang HXiao DLiang J(2024)Secure Low-complexity Compressive Sensing with Preconditioning Prior Regularization ReconstructionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363530820:4(1-22)Online publication date: 11-Jan-2024
https://dl.acm.org/doi/10.1145/3635308
Wang XWang YYang JJia XLi LDing WWang F(2024)The survey on multi-source data fusion in cyber-physical-social systems: Foundational infrastructure for industrial metaverses and industries 5.0Information Fusion10.1016/j.inffus.2024.102321107(102321)Online publication date: Jul-2024
https://doi.org/10.1016/j.inffus.2024.102321
Lv ZCheng CGuerrieri AFortino G(2023)Behavioral Modeling and Prediction in Social Perception and Computing: A SurveyIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.323021110:4(2008-2021)Online publication date: Aug-2023
https://doi.org/10.1109/TCSS.2022.3230211
Kumar RAgrawal N(2023)Analysis of multi-dimensional Industrial IoT (IIoT) data in Edge–Fog–Cloud based architectural frameworks : A survey on current state and research challengesJournal of Industrial Information Integration10.1016/j.jii.2023.10050435(100504)Online publication date: Oct-2023
https://doi.org/10.1016/j.jii.2023.100504
Yao ALi GLi XJiang FXu JLiu X(2023)Differential privacy in edge computing-based smart city Applications:Security issues, solutions and future directionsArray10.1016/j.array.2023.10029319(100293)Online publication date: Sep-2023
https://doi.org/10.1016/j.array.2023.100293
Feng JYang LZhang RQiang WChen J(2022)Privacy Preserving High-Order Bi-Lanczos in Cloud–Fog Computing for Industrial ApplicationsIEEE Transactions on Industrial Informatics10.1109/TII.2020.299808618:10(7009-7018)Online publication date: Oct-2022
https://doi.org/10.1109/TII.2020.2998086
Wang HFan KZhang KWang ZLi HYang Y(2022)Secure and Efficient Data-Privacy-Preserving Scheme for Mobile Cyber–Physical SystemsIEEE Internet of Things Journal10.1109/JIOT.2021.30814569:22(22375-22388)Online publication date: 15-Nov-2022
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Lv ZLou R(2022)Edge-Fog-Cloud Secure Storage with Deep-Learning-Assisted Digital TwinsIEEE Internet of Things Magazine10.1109/IOTM.002.21001455:2(36-40)Online publication date: Jun-2022
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Wu XQi LGao JJi GXu X(2022)An ensemble of random decision trees with local differential privacy in edge computingNeurocomputing10.1016/j.neucom.2021.01.145485(181-195)Online publication date: May-2022
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R. Alagheband MMashatan A(2022)Advanced digital signatures for preserving privacy and trust management in hierarchical heterogeneous IoT: Taxonomy, capabilities, and objectivesInternet of Things10.1016/j.iot.2021.10049218(100492)Online publication date: May-2022
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