Improved privacy-preserving Bayesian network parameter learning on vertically partitioned data

Zhiqiang Yang; Rebecca N. Wright

doi:https://doi.org/10.1109/ICDE.2005.230

Improved privacy-preserving Bayesian network parameter learning on vertically partitioned data

Zhiqiang Yang, Rebecca N. Wright

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 Scopus citations

Abstract

Privacy concerns often prevent different parties from sharing their data in order to carry out data mining applications on their joint data. Privacy-preserving data mining seeks to address this by enabling parties to jointly compute a data mining algorithm on distributed data without sharing their data. In this paper, we address a particular data mining problem, that of learning the parameters of Bayesian network on a vertically partitioned database. We provide a simple privacy-preserving protocol for learning the parameters of Bayesian network on vertically partitioned databases. In comparison to the previously known solution for this problem (Meng, Sivakumar, and Kargupta, 2004), our solution provides better performance, full privacy, and complete accuracy. In combination with our previous work on privacy-preserving learning of Bayesian network structure on vertically partitioned databases, this work provides a complete privacy-preserving protocol for learning Bayesian networks (both structure and parameters) on vertically partitioned data, with very little over-head beyond computing the structure alone.

Original language	English (US)
Title of host publication	Proceedings - International Workshop on Biomedical Data Engineering, BMDE2005
DOIs	https://doi.org/10.1109/ICDE.2005.230
State	Published - 2005
Externally published	Yes
Event	21st International Conference on Data Engineering Workshops 2005 - Tokyo, Japan Duration: Apr 3 2005 → Apr 4 2005

Publication series

Name	Proceedings - International Workshop on Biomedical Data Engineering, BMDE2005
Volume	2005

Other

Other	21st International Conference on Data Engineering Workshops 2005
Country/Territory	Japan
City	Tokyo
Period	4/3/05 → 4/4/05

ASJC Scopus subject areas

Engineering(all)

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https://doi.org/10.1109/ICDE.2005.230

Cite this

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title = "Improved privacy-preserving Bayesian network parameter learning on vertically partitioned data",

abstract = "Privacy concerns often prevent different parties from sharing their data in order to carry out data mining applications on their joint data. Privacy-preserving data mining seeks to address this by enabling parties to jointly compute a data mining algorithm on distributed data without sharing their data. In this paper, we address a particular data mining problem, that of learning the parameters of Bayesian network on a vertically partitioned database. We provide a simple privacy-preserving protocol for learning the parameters of Bayesian network on vertically partitioned databases. In comparison to the previously known solution for this problem (Meng, Sivakumar, and Kargupta, 2004), our solution provides better performance, full privacy, and complete accuracy. In combination with our previous work on privacy-preserving learning of Bayesian network structure on vertically partitioned databases, this work provides a complete privacy-preserving protocol for learning Bayesian networks (both structure and parameters) on vertically partitioned data, with very little over-head beyond computing the structure alone.",

author = "Zhiqiang Yang and Wright, {Rebecca N.}",

year = "2005",

doi = "https://doi.org/10.1109/ICDE.2005.230",

language = "English (US)",

isbn = "0769526578",

series = "Proceedings - International Workshop on Biomedical Data Engineering, BMDE2005",

booktitle = "Proceedings - International Workshop on Biomedical Data Engineering, BMDE2005",

note = "21st International Conference on Data Engineering Workshops 2005 ; Conference date: 03-04-2005 Through 04-04-2005",

}

Yang, Z & Wright, RN 2005, Improved privacy-preserving Bayesian network parameter learning on vertically partitioned data. in Proceedings - International Workshop on Biomedical Data Engineering, BMDE2005., 1647809, Proceedings - International Workshop on Biomedical Data Engineering, BMDE2005, vol. 2005, 21st International Conference on Data Engineering Workshops 2005, Tokyo, Japan, 4/3/05. https://doi.org/10.1109/ICDE.2005.230

Improved privacy-preserving Bayesian network parameter learning on vertically partitioned data. / Yang, Zhiqiang; Wright, Rebecca N.

Proceedings - International Workshop on Biomedical Data Engineering, BMDE2005. 2005. 1647809 (Proceedings - International Workshop on Biomedical Data Engineering, BMDE2005; Vol. 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Improved privacy-preserving Bayesian network parameter learning on vertically partitioned data

AU - Yang, Zhiqiang

AU - Wright, Rebecca N.

PY - 2005

Y1 - 2005

N2 - Privacy concerns often prevent different parties from sharing their data in order to carry out data mining applications on their joint data. Privacy-preserving data mining seeks to address this by enabling parties to jointly compute a data mining algorithm on distributed data without sharing their data. In this paper, we address a particular data mining problem, that of learning the parameters of Bayesian network on a vertically partitioned database. We provide a simple privacy-preserving protocol for learning the parameters of Bayesian network on vertically partitioned databases. In comparison to the previously known solution for this problem (Meng, Sivakumar, and Kargupta, 2004), our solution provides better performance, full privacy, and complete accuracy. In combination with our previous work on privacy-preserving learning of Bayesian network structure on vertically partitioned databases, this work provides a complete privacy-preserving protocol for learning Bayesian networks (both structure and parameters) on vertically partitioned data, with very little over-head beyond computing the structure alone.

AB - Privacy concerns often prevent different parties from sharing their data in order to carry out data mining applications on their joint data. Privacy-preserving data mining seeks to address this by enabling parties to jointly compute a data mining algorithm on distributed data without sharing their data. In this paper, we address a particular data mining problem, that of learning the parameters of Bayesian network on a vertically partitioned database. We provide a simple privacy-preserving protocol for learning the parameters of Bayesian network on vertically partitioned databases. In comparison to the previously known solution for this problem (Meng, Sivakumar, and Kargupta, 2004), our solution provides better performance, full privacy, and complete accuracy. In combination with our previous work on privacy-preserving learning of Bayesian network structure on vertically partitioned databases, this work provides a complete privacy-preserving protocol for learning Bayesian networks (both structure and parameters) on vertically partitioned data, with very little over-head beyond computing the structure alone.

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M3 - Conference contribution

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BT - Proceedings - International Workshop on Biomedical Data Engineering, BMDE2005

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ER -

Improved privacy-preserving Bayesian network parameter learning on vertically partitioned data

Abstract

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