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26431 Publicly Verifiable Private Aggregation of Time-Series Data
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Bakondi, B.G. and Peter, A. and Everts, M.H. and Hartel, P.H. and Jonker, W. (2015) Publicly Verifiable Private Aggregation of Time-Series Data. In: 10th International Conference on Availability, Reliability and Security, ARES 2015, 24-27 Aug 2015, Toulouse, France. pp. 50-59. IEEE. ISBN 978-1-4673-6590-1

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Aggregation of time-series data offers the possibility to learn certain statistics over data periodically uploaded by different sources. In case of privacy sensitive data, it is desired to hide every data provider’s individual values from the other participants (including the data aggregator). Existing privacy preserving time-series data aggregation schemes focus on the sum as aggregation means, since it is the most essential statistics used in many applications such as smart metering, participatory sensing, or appointment scheduling. However, all existing schemes have an important drawback: they do not provide verifiable outputs, thus users have to trust the data aggregator that it does not output fake values.
We propose a publicly verifiable data aggregation scheme for privacy preserving time-series data summation. We prove its security and verifiability under the XDH assumption and a widely used, strong variant of the Co-CDH assumption. Moreover, our scheme offers low computation complexity on the users’ side, which is essential in many applications.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-SCS: Services, Cyber security and Safety, EWI-DB: Databases
Research Program:CTIT-General
Research Project:COMMIT/THeCS: Trusted Healthcare Systems, CRIPTIM: Critical Infrastructure Protection Through Cryptographic Incident Management
ID Code:26431
Deposited On:24 November 2015
More Information:statisticsmetis

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