research-article

Privacy-preserving Data Aggregation Computing in Cyber-Physical Social Systems

Authors:

Song GuoAuthors Info & Claims

ACM Transactions on Cyber-Physical Systems, Volume 3, Issue 1

Article No.: 8, Pages 1 - 23

https://doi.org/10.1145/3145625

Published: 29 August 2018 Publication History

Abstract

In cyber-physical social systems (CPSS), a group of volunteers report data about the physical environment through their cyber devices and data aggregation is widely utilized. An important issue in data aggregation for CPSS is to protect users’ privacy. In this article, we use bitwise XOR and propose a bit-choosing algorithm to realize privacy-preserving min, k-th min, and percentile computation. By our algorithm, the aggregator can confirm whether a user’s data value is equal to certain value or within certain scale. Consequently, it is also possible to count the number of users satisfying given conditions. Our bit-choosing algorithm makes sure that the users send non-repetition replies to the aggregator to raise the aggregation accuracy. We analyze the communication cost and the achievable accuracy of our algorithm. Via performance comparison against existing protocols, the efficiency and accuracy of our algorithm are verified.

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Cited By

Gao JZhang YZhong S(2024)Revisiting Privacy-Preserving Min and k-th Min Protocols for Mobile SensingIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.332480221:4(3211-3226)Online publication date: Jul-2024
https://doi.org/10.1109/TDSC.2023.3324802
Chen M(2024)Integrating Private and Accountable Threshold Signature Into Ciphertext-Policy Attribute-Based Encryption Supporting Collaboration Decryption2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831747(3893-3898)Online publication date: 6-Oct-2024
https://doi.org/10.1109/SMC54092.2024.10831747
Yogi MChakravarthy A(2023)Application of Distributed Constraint Optimization Technique for Privacy Preservation in Cyber-Physical SystemsIntelligent Cyber Physical Systems and Internet of Things10.1007/978-3-031-18497-0_24(319-327)Online publication date: 4-Feb-2023
https://doi.org/10.1007/978-3-031-18497-0_24
Show More Cited By

Index Terms

Privacy-preserving Data Aggregation Computing in Cyber-Physical Social Systems
1. Security and privacy
  1. Cryptography
    1. Information-theoretic techniques
  2. Systems security
    1. Information flow control
2. Theory of computation
  1. Computational complexity and cryptography
    1. Cryptographic protocols

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Published In

cover image ACM Transactions on Cyber-Physical Systems

ACM Transactions on Cyber-Physical Systems Volume 3, Issue 1

Special Issue on Dependability in CPS

January 2019

256 pages

ISSN:2378-962X

EISSN:2378-9638

DOI:10.1145/3274532

Editor:
Tei-Wei Kuo
National Taiwan University, and Academia Sinica, Taiwan

Issue’s Table of Contents

Copyright © 2018 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

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 29 August 2018

Accepted: 01 September 2017

Revised: 01 July 2017

Received: 01 February 2017

Published in TCPS Volume 3, Issue 1

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Funding Sources

NSFC
National China 973 Project
China Postdoctoral Science Special Foundation
MIC, Japan
China Postdoctoral Science Foundation
Strategic Information and Communications R8D Promotion Programme

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Cited By

Gao JZhang YZhong S(2024)Revisiting Privacy-Preserving Min and k-th Min Protocols for Mobile SensingIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.332480221:4(3211-3226)Online publication date: Jul-2024
https://doi.org/10.1109/TDSC.2023.3324802
Chen M(2024)Integrating Private and Accountable Threshold Signature Into Ciphertext-Policy Attribute-Based Encryption Supporting Collaboration Decryption2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831747(3893-3898)Online publication date: 6-Oct-2024
https://doi.org/10.1109/SMC54092.2024.10831747
Yogi MChakravarthy A(2023)Application of Distributed Constraint Optimization Technique for Privacy Preservation in Cyber-Physical SystemsIntelligent Cyber Physical Systems and Internet of Things10.1007/978-3-031-18497-0_24(319-327)Online publication date: 4-Feb-2023
https://doi.org/10.1007/978-3-031-18497-0_24
Wang XGarg SLin HKaddoum GHu JHossain M(2022)A Secure Data Aggregation Strategy in Edge Computing and Blockchain-Empowered Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2020.30235889:16(14237-14246)Online publication date: 15-Aug-2022
https://doi.org/10.1109/JIOT.2020.3023588
Thabit SLianShan YTao YAbdullah A(2022)Trust management and data protection for online social networksIET Communications10.1049/cmu2.1240116:12(1355-1368)Online publication date: 3-May-2022
https://doi.org/10.1049/cmu2.12401
Rawashdeh MAlshboul YZamil MSamarah SAlnusair AHossain M(2022)A security framework for QaaS model in intelligent transportation systemsMicroprocessors and Microsystems10.1016/j.micpro.2022.10450090(104500)Online publication date: Apr-2022
https://doi.org/10.1016/j.micpro.2022.104500
Qu YGao LYu SXiang YQu YGao LYu SXiang Y(2022)Hybrid Privacy Protection of IoT Using Reinforcement LearningPrivacy Preservation in IoT: Machine Learning Approaches10.1007/978-981-19-1797-4_5(77-109)Online publication date: 28-Apr-2022
https://doi.org/10.1007/978-981-19-1797-4_5
Qu YGao LYu SXiang YQu YGao LYu SXiang Y(2022)Personalized Privacy Protection of IoTs Using GAN-Enhanced Differential PrivacyPrivacy Preservation in IoT: Machine Learning Approaches10.1007/978-981-19-1797-4_4(49-76)Online publication date: 28-Apr-2022
https://doi.org/10.1007/978-981-19-1797-4_4
Qu YGao LYu SXiang YQu YGao LYu SXiang Y(2022)Decentralized Privacy Protection of IoTs Using Blockchain-Enabled Federated LearningPrivacy Preservation in IoT: Machine Learning Approaches10.1007/978-981-19-1797-4_3(19-48)Online publication date: 28-Apr-2022
https://doi.org/10.1007/978-981-19-1797-4_3
Qu YGao LYu SXiang YQu YGao LYu SXiang Y(2022)Current Methods of Privacy Protection in IoTsPrivacy Preservation in IoT: Machine Learning Approaches10.1007/978-981-19-1797-4_2(7-18)Online publication date: 28-Apr-2022
https://doi.org/10.1007/978-981-19-1797-4_2
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