FLAIM: AIM-based Synthetic Data Generation in the Federated Setting-Reference-Cited by-同舟云学术

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FLAIM: AIM-based Synthetic Data Generation in the Federated Setting

Published:2024-08-24 Issue: Volume:33 Page:2165-2176
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Maddock Samuel¹^ORCID,Cormode Graham²^ORCID,Maple Carsten¹^ORCID

Affiliation:

1. University of Warwick, Coventry, United Kingdom

2. Meta AI & University of Warwick, Coventry, United Kingdom

Funder

EPSRC

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671990

Reference57 articles.

1. Toshinori Araki, Jun Furukawa, Yehuda Lindell, Ariel Nof, and Kazuma Ohara. 2016. High-throughput semi-honest secure three-party computation with an honest majority. In ACM SIGSAC CCS. Vienna, 805--817.

2. Generating synthetic data in finance

3. Sergul Aydore William Brown Michael Kearns Krishnaram Kenthapadi Luca Melis Aaron Roth and Ankit A Siva. 2021. Differentially private query release through adaptive projection. In ICML. PMLR 457--467.

4. James Henry Bell Kallista A Bonawitz Adrià Gascón Tancrède Lepoint and Mariana Raykova. 2020. Secure single-server aggregation with (poly) logarithmic overhead. In ACM SIGSAC CCS. Online 1253--1269.

5. Jock Blackard. 1998. Covertype. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C50K5N.

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