Welcome to the resource topic for 2024/141
Title:
Secure Statistical Analysis on Multiple Datasets: Join and Group-By
Authors: Gilad Asharov, Koki Hamada, Dai Ikarashi, Ryo Kikuchi, Ariel Nof, Benny Pinkas, Junichi Tomida
Abstract:We implement a secure platform for statistical analysis over multiple organizations and multiple datasets. We provide a suite of protocols for different variants of JOIN and GROUP-BY operations. JOIN allows combining data from multiple datasets based on a common column. GROUP-BY allows aggregating rows that have the same values in a column or a set of columns, and then apply some aggregation summary on the rows (such as sum, count, median, etc.). Both operations are fundamental tools for relational databases. One example use case of our platform is in data marketing in which an analyst would join purchase histories and membership information, and then obtain statistics, such as “Which products were bought by people earning this much per annum?”
Both JOIN and GROUP-BY involve many variants, and we design protocols for several common procedures. In particular, we propose a novel group-by-median protocol that has not been known so far. Our protocols rely on sorting protocols, and work in the honest majority setting and against malicious adversaries. To the best of our knowledge, this is the first implementation of JOIN and GROUP-BY protocols secure against a malicious adversary.
ePrint: https://eprint.iacr.org/2024/141
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