Learning Without Peeking: Secure Multi-Party Computation Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Kim:2018:SSBSE,

• author = "Jinhan Kim and Michael G. Epitropakis and Shin Yoo",
• title = "Learning Without Peeking: Secure Multi-Party Computation Genetic Programming",
• booktitle = "SSBSE 2018",
• year = "2018",
• editor = "Thelma Elita Colanzi and Phil McMinn",
• volume = "11036",
• series = "LNCS",
• pages = "246--261",
• address = "Montpellier, France",
• month = "8-9 " # sep,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, SBSE",
• isbn13 = "978-3-319-99241-9",
• DOI = "doi:10.1007/978-3-319-99241-9_13",
• abstract = "Genetic Programming is widely used to build predictive models for defect proneness or development efforts. The predictive modelling often depends on the use of sensitive data, related to past faults or internal resources, as training data. We envision a scenario in which revealing the training data constitutes a violation of privacy. To ensure organisational privacy in such a scenario, we propose SMCGP, a method that performs Genetic Programming as Secure Multiparty Computation. In SMCGP, one party uses GP to learn a model of training data provided by another party, without actually knowing each data point in the training data. We present an SMCGP approach based on the garbled circuit protocol, which is evaluated using two problem sets: a widely studied symbolic regression benchmark, and a GP-based fault localisation technique with real world fault data from Defects4J benchmark. The results suggest that SMCGP can be equally accurate as the normal GP, but the cost of keeping the training data hidden can be about three orders of magnitude slower execution.",

}

Genetic Programming entries for Jinhan Kim Michael G Epitropakis Shin Yoo

Citations