abstract

Privacy-preserving approach to bayesian network structure learning from distributed data

Authors:

Olivier Regnier-Coudert,

John McCallAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Pages 815 - 816

https://doi.org/10.1145/2001858.2002103

Published: 12 July 2011 Publication History

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Abstract

In many situations, data is scattered across different sites, making the modeling process difficult or sometimes impossible. Some applications could benefit from collaborations between organisations but data security or privacy policies often act as a barrier to data mining on such contexts. In this paper, we present a novel approach to learning Bayesian Networks (BN) structures from multiple datasets, based on the use of Ensembles and an Island Model Genetic Algorithm (IMGA). The proposed design ensures no data is shared during the process and can fit many applications.

References

[1]

R. Chen, K. Sivakumar, and H. Kargupta. Collective mining of Bayesian networks from distributed heterogeneous data. Knowledge and Information Systems, 6(2):164--187, 2004.

Digital Library

Google Scholar

[2]

G. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine learning, 9(4):309--347, 1992.

Crossref

Google Scholar

[3]

R. Kabli, F. Herrmann, and J. McCall. A chain-model genetic algorithm for bayesian network structure learning. In Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 1271--1278. ACM, 2007.

Digital Library

Google Scholar

[4]

A. Tsymbal, S. Puuronen, and D. Patterson. Ensemble feature selection with the simple Bayesian classification. Information Fusion, 4(2):87--100, 2003.

Crossref

Google Scholar

[5]

D. Whitley, S. Rana, and R. Heckendorn. The island model genetic algorithm: On separability, population size and convergence. Journal of Computing and Information Technology, 7:33--48, 1999.

Google Scholar

[6]

Z. Yang and R. Wright. Privacy-preserving computation of Bayesian networks on vertically partitioned data. IEEE Transactions on Knowledge and Data Engineering, pages 1253--1264, 2006.

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Cited By

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Gao CLi JXia SChoo KLou WDong C(2022)MAS-Encryption and its Applications in Privacy-Preserving ClassifiersIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.300922134:5(2306-2323)Online publication date: 1-May-2022
https://doi.org/10.1109/TKDE.2020.3009221
Resende ARailsback DDowsley RNascimento AAranha D(2022)Fast Privacy-Preserving Text Classification Based on Secure Multiparty ComputationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2022.314400717(428-442)Online publication date: 2022
https://doi.org/10.1109/TIFS.2022.3144007
Canillas RTalbi RBouchenak SHasan OBrunie LSarrat L(2018)Exploratory Study of Privacy Preserving Fraud DetectionProceedings of the 19th International Middleware Conference Industry10.1145/3284028.3284032(25-31)Online publication date: 10-Dec-2018
https://dl.acm.org/doi/10.1145/3284028.3284032
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Index Terms

Privacy-preserving approach to bayesian network structure learning from distributed data
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
  2. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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Information

Published In

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

July 2011

1548 pages

ISBN:9781450306904

DOI:10.1145/2001858

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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Gao CLi JXia SChoo KLou WDong C(2022)MAS-Encryption and its Applications in Privacy-Preserving ClassifiersIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.300922134:5(2306-2323)Online publication date: 1-May-2022
https://doi.org/10.1109/TKDE.2020.3009221
Resende ARailsback DDowsley RNascimento AAranha D(2022)Fast Privacy-Preserving Text Classification Based on Secure Multiparty ComputationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2022.314400717(428-442)Online publication date: 2022
https://doi.org/10.1109/TIFS.2022.3144007
Canillas RTalbi RBouchenak SHasan OBrunie LSarrat L(2018)Exploratory Study of Privacy Preserving Fraud DetectionProceedings of the 19th International Middleware Conference Industry10.1145/3284028.3284032(25-31)Online publication date: 10-Dec-2018
https://dl.acm.org/doi/10.1145/3284028.3284032
Zincir-Heywood NKayacik G(2017)Evolutionary computation in network management and securityProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3067726(1094-1112)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3067726
Regnier-Coudert OMcCall J(2012)An Island Model Genetic Algorithm for Bayesian network structure learning2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6252982(1-8)Online publication date: Jun-2012
https://doi.org/10.1109/CEC.2012.6252982

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