Result intcgrity verification of outsourced Bayesian network structure learning

Ruilin Liu, Hui Wang, Changhe Yuan

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

3 Scopus citations

Abstract

There has been considerable recent interest in the datamining-as-a-service paradigm: the client that lacks computational resources outsources his/her data and data mining needs to a third-party service provider. One of the security issues of this outsourcing paradigm is how the client can verify that the service provider indeed has returned correct data mining results. In this paper, we focus on the problem of result verification of outsourced Bayesian network (BN) structure learning. We consider the untrusted service provider that intends to return wrong BN structures. We develop three efficient probabilistic verification approaches to catch the incorrect BN structure with high probability and cheap overhead. Our experimental results demonstrate that our verification methods can capture wrong BN structure effectively and efficiently.

Original languageEnglish
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
EditorsMohammed Zaki, Zoran Obradovic, Pang Ning-Tan, Arindam Banerjee, Chandrika Kamath, Srinivasan Parthasarathy
Pages713-721
Number of pages9
ISBN (Electronic)9781510811515
DOIs
StatePublished - 2014
Event14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States
Duration: 24 Apr 201426 Apr 2014

Publication series

NameSIAM International Conference on Data Mining 2014, SDM 2014
Volume2

Conference

Conference14th SIAM International Conference on Data Mining, SDM 2014
Country/TerritoryUnited States
CityPhiladelphia
Period24/04/1426/04/14

Keywords

  • Bayesian network structure learning
  • Cloud computing
  • Data-mining-as-a-service (DMaS)
  • Result integrity

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