Mining Frequent Patterns Securely in Distributed System(Data Mining)

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概要

• 論文の詳細を見る

• Data mining across different companies, organizations, online shops, or the likes is necessary so as to discover valuable shared patterns, associations, trends, or dependencies in their shared data. Privacy, however, is a concern. In many situations it is required that data mining should be conducted without any privacy being violated. In response to this requirement, in this paper we propose an effective distributed privacy-preserving data mining approach called SDDM. SDDM is characterized by its ability to resist collusion. Unless the number of colluding sites in a distributed system is larger than or equal to 4, privacy cannot be violated. Results of performance study demonstrated the effectiveness of SDDM.

• 社団法人電子情報通信学会の論文
• 2006-11-01

著者

• Takata Toyoo

  Faculty Of Software And Information Science Iwate Prefectural University

• Takata Toyoo

  Iwate Prefectural University

• Miyazaki Masatoshi

  Argo Solutions Co. Ltd.

• WANG JIAHONG

  Iwate Prefectural University

• FUKASAWA Takuya

  Project-EF Corporation

• URABE Shintaro

  Iwate Prefectural University

• Urabe Shintaro

  Faculty Of Software And Information Science Iwate Prefectural University:(present Office)hitachi Eas

• Wang Jiahong

  Faculty of Software and Information Science, Iwate Prefectural University

関連論文

• A Soft-Decision Iterative Decoding Algorithm Using a Top-Down and Recursive Minimum Distance Search(Special Section on Information Theory and Its Applications)
• On the Number of Minimum Weight Codewords of Subcodes of Reed-Muller Codes (Special Section on Information Theory and Its Applications)
• A High Collusion-Resistant Approach to Distributed Privacy-preserving Data Mining
• Mining Sequential Patterns More Efficiently by Reducing the Cost of Scanning Sequence Databases(Data Mining)
• Mining Frequent Patterns Securely in Distributed System(Data Mining)
• Mining Sequential Patterns More Efficiently by Reducing the Cost of Scanning Sequence Databases
• Mining Sequential Patterns More Efficiently by Reducing the Cost of Scanning Sequence Databases
• A High Collusion-Resistant Approach to Distributed Privacy-preserving Data Mining
• A High Collusion-Resistant Approach to Distributed Privacy-preserving Data Mining

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