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  • 标题:Privacy-Preserving Data Mining based on Integrated Customer Databases from Different Enterprises
  • 本地全文:下载
  • 作者:Gábor SZŰCS ; Attila KISS
  • 期刊名称:Economy Informatics
  • 印刷版ISSN:1582-7941
  • 出版年度:2014
  • 卷号:14
  • 期号:1
  • 出版社:INFOREC Association
  • 摘要:The paper is about data mining projects in real applications, where preserving the users' pri- vacy is important. The aim was to build a secure multiparty computation (SMC) data mining system with SMC data mining algorithm that would be able to solve the task of classification in a horizontally distributed environment with multiple parties trying for a joint data mining project. For solution of this kind of privacy preserving problems we have designed and devel- oped an SMC system with different modules, a client module, a trusted third party and a clas- sification module. We have worked out a new classification method; our k-means based su- pervised classifier preserves high level anonymity and provides k-anonymity, where k is a us- er parameter. At the end of the paper a bank example and its results with high accuracy pre- sent the efficiency of our system.
  • 关键词:Anonymity; Customer Databases; Data Mining; Secure Multiparty Computation
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