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Computing Join Aggregates Over Private Tables

Computing Join Aggregates Over Private Tables

Rong She, Ke Want, Ada Waichee Fu, Xu Yabo
Copyright: © 2008 |Volume: 4 |Issue: 4 |Pages: 20
ISSN: 1548-3924|EISSN: 1548-3932|ISSN: 1548-3924|EISBN13: 9781615202027|EISSN: 1548-3924|DOI: 10.4018/jdwm.2008100102
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MLA

She, Rong, et al. "Computing Join Aggregates Over Private Tables." IJDWM vol.4, no.4 2008: pp.22-41. http://doi.org/10.4018/jdwm.2008100102

APA

She, R., Want, K., Fu, A. W., & Xu Yabo. (2008). Computing Join Aggregates Over Private Tables. International Journal of Data Warehousing and Mining (IJDWM), 4(4), 22-41. http://doi.org/10.4018/jdwm.2008100102

Chicago

She, Rong, et al. "Computing Join Aggregates Over Private Tables," International Journal of Data Warehousing and Mining (IJDWM) 4, no.4: 22-41. http://doi.org/10.4018/jdwm.2008100102

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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 without letting any non-owning party know the actual join values or make any inference about the data in other parties??. We solve this problem by using a novel private sketching protocol that securely exchanges some randomized summary information about private tables. This protocol (1) conceals individual private values and their distributions from all non-owning parties, (2) works on many general forms of aggregation functions, (3) handles group-by aggregates, and (4) handles roll-up/drill-down operations. Previous works have not provided this level of privacy for such queries

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