Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol
Many miraculous ideas have been proposed to deal with the privacy-preserving time-series data aggregation problem in pervasive computing applications, such as mobile cloud computing. The main challenge consists in computing the global statistics of individual inputs that are protected by some confid...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-07-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/155014771341606 |
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| _version_ | 1832547864028381184 |
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| author | Yongkai Li Shubo Liu Jun Wang Mengjun Liu |
| author_facet | Yongkai Li Shubo Liu Jun Wang Mengjun Liu |
| author_sort | Yongkai Li |
| collection | DOAJ |
| description | Many miraculous ideas have been proposed to deal with the privacy-preserving time-series data aggregation problem in pervasive computing applications, such as mobile cloud computing. The main challenge consists in computing the global statistics of individual inputs that are protected by some confidentiality mechanism. However, those works either suffer from collusive attack or require time-consuming initialization at every aggregation request. In this paper, we proposed an efficient aggregation protocol which tolerates up to k passive adversaries that do not try to tamper the computation. The proposed protocol does not require a trusted key dealer and needs only one initialization during the whole time-series data aggregation. We formally analyzed the security of our protocol and results showed that the protocol is secure if the Computational Diffie-Hellman (CDH) problem is intractable. Furthermore, the implementation showed that the proposed protocol can be efficient for the time-series data aggregation. |
| format | Article |
| id | doaj-art-ac1385712d684c8ebd89b432d9b85e0e |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2016-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-ac1385712d684c8ebd89b432d9b85e0e2025-02-03T06:43:09ZengWileyInternational Journal of Distributed Sensor Networks1550-14772016-07-011210.1177/155014771341606Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation ProtocolYongkai Li0Shubo Liu1Jun Wang2Mengjun Liu3School of Computer, Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education and State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Computer, Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education and State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Computer, Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education and State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Computer, Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education and State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, ChinaMany miraculous ideas have been proposed to deal with the privacy-preserving time-series data aggregation problem in pervasive computing applications, such as mobile cloud computing. The main challenge consists in computing the global statistics of individual inputs that are protected by some confidentiality mechanism. However, those works either suffer from collusive attack or require time-consuming initialization at every aggregation request. In this paper, we proposed an efficient aggregation protocol which tolerates up to k passive adversaries that do not try to tamper the computation. The proposed protocol does not require a trusted key dealer and needs only one initialization during the whole time-series data aggregation. We formally analyzed the security of our protocol and results showed that the protocol is secure if the Computational Diffie-Hellman (CDH) problem is intractable. Furthermore, the implementation showed that the proposed protocol can be efficient for the time-series data aggregation.https://doi.org/10.1177/155014771341606 |
| spellingShingle | Yongkai Li Shubo Liu Jun Wang Mengjun Liu Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol International Journal of Distributed Sensor Networks |
| title | Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol |
| title_full | Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol |
| title_fullStr | Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol |
| title_full_unstemmed | Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol |
| title_short | Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol |
| title_sort | collusion tolerable and efficient privacy preserving time series data aggregation protocol |
| url | https://doi.org/10.1177/155014771341606 |
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