Secure Statistical Analysis on Multiple Datasets: Join and Group-By

Gilad Asharov; Koki Hamada; Ryo Kikuchi; Ariel Nof; Benny Pinkas; Junichi Tomida

doi:10.1145/3576915.3623119

Secure Statistical Analysis on Multiple Datasets: Join and Group-By

Gilad Asharov, Koki Hamada, Ryo Kikuchi, Ariel Nof, Benny Pinkas, Junichi Tomida

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security
Publisher	Association for Computing Machinery, Inc
Pages	3298-3312
Number of pages	15
ISBN (Electronic)	9798400700507
DOIs	https://doi.org/10.1145/3576915.3623119
State	Published - 2023
Event	30th ACM SIGSAC Conference on Computer and Communications Security, CCS 2023 - Copenhagen, Denmark Duration: 26 Nov 2023 → 30 Nov 2023

Publication series

Name	CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

Conference

Conference	30th ACM SIGSAC Conference on Computer and Communications Security, CCS 2023
Country/Territory	Denmark
City	Copenhagen
Period	26/11/23 → 30/11/23

Bibliographical note

Publisher Copyright:
© 2023 Copyright held by the owner/author(s).

Funding

The authors are grateful to Dai Ikarashi for suggesting the idea of the protocols and for implementing most of them. Asharov is sponsored by the Israel Science Foundation (grant No. 2439/20), by JPM Faculty Research Award, and by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 891234. The authors are grateful to Dai Ikarashi for suggesting the idea of the protocols and for implementing most of them. Asharov is sponsored by the Israel Science Foundation (grant No. 2439/20), by JPM Faculty Research Award, and by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 891234.

Funders	Funder number
JPMorgan Chase and Company
Horizon 2020 Framework Programme
H2020 Marie Skłodowska-Curie Actions	891234
Israel Science Foundation	2439/20
Horizon 2020

Keywords

Privacy-preserving protocols
group-by
honest majority
join
multiparty computation

Access to Document

10.1145/3576915.3623119

Cite this

Asharov, G., Hamada, K., Kikuchi, R., Nof, A., Pinkas, B., & Tomida, J. (2023). Secure Statistical Analysis on Multiple Datasets: Join and Group-By. In CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (pp. 3298-3312). (CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security). Association for Computing Machinery, Inc. https://doi.org/10.1145/3576915.3623119

Asharov, Gilad ; Hamada, Koki ; Kikuchi, Ryo et al. / Secure Statistical Analysis on Multiple Datasets : Join and Group-By. CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. Association for Computing Machinery, Inc, 2023. pp. 3298-3312 (CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security).

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Asharov, G, Hamada, K, Kikuchi, R, Nof, A , Pinkas, B & Tomida, J 2023, Secure Statistical Analysis on Multiple Datasets: Join and Group-By. in CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, Association for Computing Machinery, Inc, pp. 3298-3312, 30th ACM SIGSAC Conference on Computer and Communications Security, CCS 2023, Copenhagen, Denmark, 26/11/23. https://doi.org/10.1145/3576915.3623119

Secure Statistical Analysis on Multiple Datasets: Join and Group-By. / Asharov, Gilad; Hamada, Koki; Kikuchi, Ryo et al.
CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. Association for Computing Machinery, Inc, 2023. p. 3298-3312 (CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Asharov G, Hamada K, Kikuchi R, Nof A , Pinkas B, Tomida J. Secure Statistical Analysis on Multiple Datasets: Join and Group-By. In CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. Association for Computing Machinery, Inc. 2023. p. 3298-3312. (CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security). doi: 10.1145/3576915.3623119

Secure Statistical Analysis on Multiple Datasets: Join and Group-By

Abstract

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Bibliographical note

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Keywords

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