Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{nguyen-duy:2023:SymReg,
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author = "Du {Nguyen Duy} and Michael Affenzeller and
Ramin Nikzad-Langerodi",
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title = "Towards Vertical {Privacy-Preserving} Symbolic
Regression via Secure Multiparty Computation",
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booktitle = "Symbolic Regression",
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year = "2023",
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editor = "Michael Kommenda and William {La Cava} and
Gabriel Kronberger and Steven Gustafson",
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pages = "2420--2428",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, symbolic
regression, secure multiparty computation, federated
learning, privacy-preserving",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583133.3596337",
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size = "9 pages",
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abstract = "Symbolic Regression is a powerful data-driven
technique that searches for mathematical expressions
that explain the relationship between input variables
and a target of interest. Due to its efficiency and
flexibility, Genetic Programming can be seen as the
standard search technique for Symbolic Regression.
However, the conventional Genetic Programming algorithm
requires storing all data in a central location, which
is not always feasible due to growing concerns about
data privacy and security. While privacy-preserving
research has advanced recently and might offer a
solution to this problem, their application to Symbolic
Regression remains largely unexplored. Furthermore, the
existing work only focuses on the horizontally
partitioned setting, whereas the vertically partitioned
setting, another popular scenario, has yet to be
investigated. Herein, we propose an approach that
employs a privacy-preserving technique called Secure
Multiparty Computation to enable parties to jointly
build Symbolic Regression models in the vertical
scenario without revealing private data. Preliminary
experimental results indicate that our proposed method
delivers comparable performance to the centralized
solution while safeguarding data privacy.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Du Nguyen Duy
Michael Affenzeller
Ramin Nikzad-Langerodi
Citations