Paper 2024/141
Secure Statistical Analysis on Multiple Datasets: Join and Group-By
, Bar-Ilan University, NTT (Japan), NTT (Japan), NTT (Japan), Bar-Ilan University, Bar-Ilan University, Aptos Labs, NTT (Japan)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.
Metadata
- Available format(s)
-
PDF
- Category
- Cryptographic protocols
- Publication info
- Published elsewhere. Major revision. 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS)
- DOI
- https://doi.org/10.1145/3576915.3623119
- Keywords
- Privacy-preserving protocolsmultiparty computationjoingroup-byhonest majority
- Contact author(s)
-
Gilad Asharov @ biu ac il
koki hamada @ ntt com
dai ikarashi @ ntt com
9h358j30qe @ gmail com
ariel nof @ biu ac il
benny @ pinkas net
tomida junichi @ gmail com - History
- 2024-02-02: approved
- 2024-02-01: received
- See all versions
- Short URL
- https://ia.cr/2024/141
- License
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CC BY
BibTeX
@misc{cryptoeprint:2024/141,
author = {Gilad Asharov and Koki Hamada and Dai Ikarashi and Ryo Kikuchi and Ariel Nof and Benny Pinkas and Junichi Tomida},
title = {Secure Statistical Analysis on Multiple Datasets: Join and Group-By},
howpublished = {Cryptology {ePrint} Archive, Paper 2024/141},
year = {2024},
doi = {https://doi.org/10.1145/3576915.3623119},
url = {https://eprint.iacr.org/2024/141}
}
