research-article

Secure Sampling for Approximate Multi-party Query Processing

Authors:

Feifei LiAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 3

Article No.: 219, Pages 1 - 27

https://doi.org/10.1145/3617339

Published: 13 November 2023 Publication History

Abstract

We study the problem of random sampling in the secure multi-party computation (MPC) model. In MPC, taking a sample securely must have a cost Ω(n) irrespective to the sample size s. This is in stark contrast with the plaintext setting, where a sample can be taken in O(s) time trivially. Thus, the goal of approximate query processing (AQP) with sublinear costs seems unachievable under MPC. To get around this inherent barrier, in this paper we take a two-stage approach: In the offline stage, we generate a batch of n/s samples with (n) total cost, which can then be consumed to answer queries as they arrive online. Such an approach allows us to achieve an Õ(s) amortized cost per query, similar to the plaintext setting. Based on our secure batch sampling algorithms, we build MASQUE, an MPC-AQP system that achieves sublinear online query costs by running an MPC protocol to evaluate the queries on pre-generated samples. MASQUE achieves the strong security guarantee of the MPC model, i.e., nothing is revealed beyond the query result, which itself can be further protected by (amplified) differential privacy

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Cited By

Luo WFang YLin CZhou Y(2024)Efficient Parallel D-Core Decomposition at ScaleProceedings of the VLDB Endowment10.14778/3675034.367505417:10(2654-2667)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.14778/3675034.3675054
Shen LZhang YZhang HWang Y(2023)Data Player: Automatic Generation of Data Videos with Narration-Animation InterplayIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332719730:1(109-119)Online publication date: 3-Nov-2023
https://dl.acm.org/doi/10.1109/TVCG.2023.3327197

Index Terms

Secure Sampling for Approximate Multi-party Query Processing

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 3

PACMMOD

September 2023

472 pages

EISSN:2836-6573

DOI:10.1145/3632968

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Published: 13 November 2023

Published in PACMMOD Volume 1, Issue 3

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Cited By

Luo WFang YLin CZhou Y(2024)Efficient Parallel D-Core Decomposition at ScaleProceedings of the VLDB Endowment10.14778/3675034.367505417:10(2654-2667)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.14778/3675034.3675054
Shen LZhang YZhang HWang Y(2023)Data Player: Automatic Generation of Data Videos with Narration-Animation InterplayIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332719730:1(109-119)Online publication date: 3-Nov-2023
https://dl.acm.org/doi/10.1109/TVCG.2023.3327197

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