A Privacy-Preserving Data Aggregation Scheme Based on Chinese Remainder Theorem in Mobile Crowdsensing System

Mobile crowdsensing provides a large-scale reliable data, since sensing nodes support wide distribution, flexible mobility, and opportunistic connectivity. The generated data often contains the sensing node's sensitive information. Existing schemes dedicated to protecting the privacy of percept...

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Published inIEEE systems journal Vol. 17; no. 3; pp. 4257 - 4266
Main Authors Zhu, Boyao, Li, Yumei, Hu, Guoxiong, Zhang, Mingwu
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN1932-8184
1937-9234
DOI10.1109/JSYST.2023.3262321

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Abstract Mobile crowdsensing provides a large-scale reliable data, since sensing nodes support wide distribution, flexible mobility, and opportunistic connectivity. The generated data often contains the sensing node's sensitive information. Existing schemes dedicated to protecting the privacy of perception data, but these schemes cannot balance the computational and communication overheads well. Therefore, we propose a privacy-preserving data aggregation scheme based on the Chinese remainder theorem. Using blinding factor and Paillier homomorphic encryption technology, which not only ensures the privacy of perceived data but also improves the robustness of the system. Specially, the introduction of secure multicast communication technology based on the Chinese remainder theorem protects task privacy and ensures only designated sensing nodes can obtain the task. In addition, we design an efficient signature scheme to ensure data integrity. Detailed security analysis shows that our scheme is existentially unforgeable against adaptively chosen message attack. Extensive experiments and performance analysis show that our scheme is efficient and has a low communication overhead.
AbstractList Mobile crowdsensing provides a large-scale reliable data, since sensing nodes support wide distribution, flexible mobility, and opportunistic connectivity. The generated data often contains the sensing node's sensitive information. Existing schemes dedicated to protecting the privacy of perception data, but these schemes cannot balance the computational and communication overheads well. Therefore, we propose a privacy-preserving data aggregation scheme based on the Chinese remainder theorem. Using blinding factor and Paillier homomorphic encryption technology, which not only ensures the privacy of perceived data but also improves the robustness of the system. Specially, the introduction of secure multicast communication technology based on the Chinese remainder theorem protects task privacy and ensures only designated sensing nodes can obtain the task. In addition, we design an efficient signature scheme to ensure data integrity. Detailed security analysis shows that our scheme is existentially unforgeable against adaptively chosen message attack. Extensive experiments and performance analysis show that our scheme is efficient and has a low communication overhead.
Author Li, Yumei
Hu, Guoxiong
Zhu, Boyao
Zhang, Mingwu
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Snippet Mobile crowdsensing provides a large-scale reliable data, since sensing nodes support wide distribution, flexible mobility, and opportunistic connectivity. The...
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SubjectTerms Authentication
Chinese remainder theorem
Communication
Cryptography
Data aggregation
Data management
Data privacy
integrity
mobile crowdsensing (MCS)
Nodes
Privacy
privacy-preserving
Sensors
Task analysis
Theorems
Title A Privacy-Preserving Data Aggregation Scheme Based on Chinese Remainder Theorem in Mobile Crowdsensing System
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