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Federated boosted decision trees with differential privacy
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Maddock, Samuel, Cormode, Graham, Wang, Tianhao, Maple, Carsten and Jha, Somesh (2022) Federated boosted decision trees with differential privacy. In: ACM SIGSAC Conference on Computer and Communications Security (CCS ’22), Los Angeles, CA, USA, 7–11 Nov 2022. Published in: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (CCS ’22) pp. 2249-2263. ISBN 9781450394505. doi:10.1145/3548606.3560687
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Official URL: https://doi.org/10.1145/3548606.3560687
Abstract
There is great demand for scalable, secure, and effcient privacypreserving machine learning models that can be trained over distributed data. While deep learning models typically achieve the best results in a centralized non-secure setting, different models can excel when privacy and communication constraints are imposed. Instead, tree-based approaches such as XGBoost have attracted much attention for their high performance and ease of use; in particular, they often achieve state-of-the-art results on tabular data. Consequently, several recent works have focused on translating Gradient Boosted Decision Tree (GBDT) models like XGBoost into federated settings, via cryptographic mechanisms such as Homomorphic Encryption (HE) and Secure Multi-Party Computation (MPC). However, these do not always provide formal privacy guarantees, or consider the full range of hyperparameters and implementation settings. In this work, we implement the GBDT model under Differential Privacy (DP). We propose a general framework that captures and extends existing approaches for differentially private decision trees. Our framework of methods is tailored to the federated setting, and we show that with a careful choice of techniques it is possible to achieve very high utility while maintaining strong levels of privacy.
Item Type: | Conference Item (Paper) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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Library of Congress Subject Headings (LCSH): | Machine learning , Deep learning (Machine learning), Data privacy, Data mining , Computer security , Decision trees | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Journal or Publication Title: | Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (CCS ’22) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Publisher: | ACM | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
ISBN: | 9781450394505 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Official Date: | 7 November 2022 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Dates: |
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Page Range: | pp. 2249-2263 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DOI: | 10.1145/3548606.3560687 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Reuse Statement (publisher, data, author rights): | © ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in CCS '22: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security November 2022 Pages 2249–2263 https://doi.org/10.1145/3548606.3560687 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Date of first compliant deposit: | 10 October 2022 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Date of first compliant Open Access: | 20 December 2022 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Title of Event: | ACM SIGSAC Conference on Computer and Communications Security (CCS ’22) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Type of Event: | Conference | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Location of Event: | Los Angeles, CA, USA | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Date(s) of Event: | 7–11 Nov 2022 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Open Access Version: |
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