Privacy-preserving and verifiable data aggregation
There are several recent research studies on privacy-preserving aggregation of time series data, where an aggregator computes an aggregation of multiple users' data without learning each individual's private input value. However, none of the existing schemes allows the aggregation result t...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2016
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/3594 https://ink.library.smu.edu.sg/context/sis_research/article/4595/viewcontent/CISS14_0115.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | There are several recent research studies on privacy-preserving aggregation of time series data, where an aggregator computes an aggregation of multiple users' data without learning each individual's private input value. However, none of the existing schemes allows the aggregation result to be verified for integrity. In this paper, we present a new data aggregation scheme that protects user privacy as well as integrity of the aggregation. Towards this end, we first propose an aggregate signature scheme in a multi-user setting without using bilinear maps. We then extend the aggregate signature scheme into a solution for privacy-preserving and verifiable data aggregation. The solution allows multiple users to periodically send encrypted data to an untrusted aggregator such that the latter is able to compute the sum of the input data values and verify its integrity, without learning any other information. A formal security analysis shows that the solution is semantically secure and unforgeable. |
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