Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation

Created by W.Langdon from gp-bibliography.bib Revision:1.7662

@InProceedings{nguyen-duy:2023:SymReg,

• author = "Du {Nguyen Duy} and Michael Affenzeller and Ramin Nikzad-Langerodi",
• title = "Towards Vertical {Privacy-Preserving} Symbolic Regression via Secure Multiparty Computation",
• booktitle = "Symbolic Regression",
• year = "2023",
• editor = "Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson",
• pages = "2420--2428",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, symbolic regression, secure multiparty computation, federated learning, privacy-preserving",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3596337",
• size = "9 pages",
• 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.",
• 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