Learning Without Peeking: Secure Multi-Party Computation Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@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