Abstract
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 language | Undefined |
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Title of host publication | 10th International Conference on Availability, Reliability and Security, ARES 2015 |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 50-59 |
Number of pages | 10 |
ISBN (Print) | 978-1-4673-6590-1 |
DOIs | |
Publication status | Published - 1 Aug 2015 |
Event | 10th International Conference on Availability, Reliability and Security, ARES 2015: The International Dependability Conference - Toulouse, France Duration: 24 Aug 2015 → 27 Aug 2015 Conference number: 10 |
Publication series
Name | |
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Publisher | IEEE |
Conference
Conference | 10th International Conference on Availability, Reliability and Security, ARES 2015 |
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Abbreviated title | ARES |
Country/Territory | France |
City | Toulouse |
Period | 24/08/15 → 27/08/15 |
Keywords
- SCS-Cybersecurity
- IR-98162
- METIS-315015
- EWI-26431