Publicly Verifiable Private Aggregation of Time-Series Data

B. G. Bakondi, A. Peter, M. H. Everts, P. H. Hartel, Willem Jonker

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publication10th International Conference on Availability, Reliability and Security, ARES 2015
Number of pages10
Publication statusPublished - 1 Aug 2015
Externally publishedYes
Event10th International Conference on Availability, Reliability and Security - Toulouse, France
Duration: 24 Aug 201528 Aug 2015


Conference10th International Conference on Availability, Reliability and Security
Abbreviated titleARES 2015


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