Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation

Du Nguyen Duy, Michael Affenzeller, Ramin Nikzad-Langerodi

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationGECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages2420-2428
Number of pages9
ISBN (Electronic)9798400701207
DOIs
Publication statusPublished - 15 Jul 2023
Event2023 Genetic and Evolutionary Computation Conference Companion, GECCO 2023 Companion - Lisbon, Portugal
Duration: 15 Jul 202319 Jul 2023

Publication series

NameGECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2023 Genetic and Evolutionary Computation Conference Companion, GECCO 2023 Companion
Country/TerritoryPortugal
CityLisbon
Period15.07.202319.07.2023

Keywords

  • Federated Learning
  • Genetic Programming
  • Privacy-Preserving
  • Secure Multiparty Computation
  • Symbolic Regression

Fingerprint

Dive into the research topics of 'Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation'. Together they form a unique fingerprint.

Cite this