Computing Join Aggregates over Private Tables

Computing Join Aggregates over Private Tables

She Rong (Simon Fraser University, Canada), Wang Ke (Simon Fraser University, Canada), Waichee Fu Ada (Chinese University of Hong Kong, Hong Kong) and Xu Yabo (Simon Fraser University, Canada)
DOI: 10.4018/978-1-60566-717-1.ch014
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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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