File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Outsourced privacy-preserving C4.5 decision tree algorithm over horizontally and vertically partitioned dataset among multiple parties

TitleOutsourced privacy-preserving C4.5 decision tree algorithm over horizontally and vertically partitioned dataset among multiple parties
Authors
KeywordsC4.5 decision tree
Outsourced computation
PPWAP
Privacy preserving data mining
Secure multiparty computation
Issue Date2019
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=1386-7857
Citation
Cluster Computing, 2019, v. 22, p. 1581-1593 How to Cite?
AbstractMany companies want to share data for data-mining tasks. However, privacy and security concerns have become a bottleneck in the data-sharing field. The secure multiparty computation (SMC)-based privacy-preserving data mining has emerged as a solution to this problem. However, there is heavy computation cost at user side in traditional SMC solutions. This study introduces an outsourcing method to reduce the computation cost of the user side. We also preserve the privacy of the shared databy proposing an outsourced privacy-preserving C4.5 algorithm over horizontally and vertically partitioned data for multiple parties based on the outsourced privacy preserving weighted average protocol (OPPWAP) and outsourced secure set intersection protocol (OSSIP). Consequently, we have found that our method can achieve a result same the original C4.5 decision tree algorithm while preserving data privacy. Furthermore, we also implement the proposed protocols and the algorithms. It shows that a sublinear relationship exists between the computational cost of the user side and the number of participating parties.
Persistent Identifierhttp://hdl.handle.net/10722/277565
ISSN
2017 Impact Factor: 1.601
2015 SCImago Journal Rankings: 0.605

 

DC FieldValueLanguage
dc.contributor.authorLi, Y-
dc.contributor.authorJiang, ZL-
dc.contributor.authorYao, L-
dc.contributor.authorWang, X-
dc.contributor.authorYiu, SM-
dc.contributor.authorHuang, Z-
dc.date.accessioned2019-09-20T08:53:29Z-
dc.date.available2019-09-20T08:53:29Z-
dc.date.issued2019-
dc.identifier.citationCluster Computing, 2019, v. 22, p. 1581-1593-
dc.identifier.issn1386-7857-
dc.identifier.urihttp://hdl.handle.net/10722/277565-
dc.description.abstractMany companies want to share data for data-mining tasks. However, privacy and security concerns have become a bottleneck in the data-sharing field. The secure multiparty computation (SMC)-based privacy-preserving data mining has emerged as a solution to this problem. However, there is heavy computation cost at user side in traditional SMC solutions. This study introduces an outsourcing method to reduce the computation cost of the user side. We also preserve the privacy of the shared databy proposing an outsourced privacy-preserving C4.5 algorithm over horizontally and vertically partitioned data for multiple parties based on the outsourced privacy preserving weighted average protocol (OPPWAP) and outsourced secure set intersection protocol (OSSIP). Consequently, we have found that our method can achieve a result same the original C4.5 decision tree algorithm while preserving data privacy. Furthermore, we also implement the proposed protocols and the algorithms. It shows that a sublinear relationship exists between the computational cost of the user side and the number of participating parties.-
dc.languageeng-
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=1386-7857-
dc.relation.ispartofCluster Computing-
dc.rightsThis is a post-peer-review, pre-copyedit version of an article published in [insert journal title]. The final authenticated version is available online at: http://dx.doi.org/[insert DOI]-
dc.subjectC4.5 decision tree-
dc.subjectOutsourced computation-
dc.subjectPPWAP-
dc.subjectPrivacy preserving data mining-
dc.subjectSecure multiparty computation-
dc.titleOutsourced privacy-preserving C4.5 decision tree algorithm over horizontally and vertically partitioned dataset among multiple parties-
dc.typeArticle-
dc.identifier.emailYiu, SM: smyiu@cs.hku.hk-
dc.identifier.authorityYiu, SM=rp00207-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s10586-017-1019-9-
dc.identifier.scopuseid_2-s2.0-85021896106-
dc.identifier.hkuros305925-
dc.identifier.volume22-
dc.identifier.spage1581-
dc.identifier.epage1593-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats