Abstract
We propose a privacy-preserving protocol for computing aggregation queries over the join of private tables. In this problem, several parties wish to share aggregated information over the join of their tables, but want to conceal the details that generate such information. The join operation presents a challenge to privacy preservation because it requires matching individual records from private tables. We solve this problem by a novel sketching protocol that securely computes some randomized summary information over private tables. It ensures that during the query computation process, no party will learn other parties’ private data, including the individual records and data distributions. Previous works have not provided this level of privacy for such queries.
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She, R., Wang, K., Fu, A.W., Xu, Y. (2007). Computing Join Aggregates over Private Tables. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2007. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74553-2_8
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DOI: https://doi.org/10.1007/978-3-540-74553-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74552-5
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