Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/626814
Title: A Framework for Categorizing and Applying Privacy-Preservation Techniques in Big Data Mining
Authors: Lei Xu;Chunxiao Jiang;Yan Chen;Jian Wang;Yong Ren
subject: game theory|big data management|sensitive information|provenance|privacy|data mining|information security|information privacy|big data
Year: 2016
Publisher: IEEE
Abstract: To protect sensitive information in mined data, researchers need a way to organize a variety of ongoing work. The Rampart framework categorizes protection approaches and encourages interdisciplinary solutions to the growing variety of privacy problems associated with knowledge discovery from data.
Description: 
URI: http://localhost/handle/Hannan/162398
http://localhost/handle/Hannan/626814
ISSN: 0018-9162
volume: 49
issue: 2
Appears in Collections:2016

Files in This Item:
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Title: A Framework for Categorizing and Applying Privacy-Preservation Techniques in Big Data Mining
Authors: Lei Xu;Chunxiao Jiang;Yan Chen;Jian Wang;Yong Ren
subject: game theory|big data management|sensitive information|provenance|privacy|data mining|information security|information privacy|big data
Year: 2016
Publisher: IEEE
Abstract: To protect sensitive information in mined data, researchers need a way to organize a variety of ongoing work. The Rampart framework categorizes protection approaches and encourages interdisciplinary solutions to the growing variety of privacy problems associated with knowledge discovery from data.
Description: 
URI: http://localhost/handle/Hannan/162398
http://localhost/handle/Hannan/626814
ISSN: 0018-9162
volume: 49
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7404188.pdf1.15 MBAdobe PDFThumbnail
Preview File
Title: A Framework for Categorizing and Applying Privacy-Preservation Techniques in Big Data Mining
Authors: Lei Xu;Chunxiao Jiang;Yan Chen;Jian Wang;Yong Ren
subject: game theory|big data management|sensitive information|provenance|privacy|data mining|information security|information privacy|big data
Year: 2016
Publisher: IEEE
Abstract: To protect sensitive information in mined data, researchers need a way to organize a variety of ongoing work. The Rampart framework categorizes protection approaches and encourages interdisciplinary solutions to the growing variety of privacy problems associated with knowledge discovery from data.
Description: 
URI: http://localhost/handle/Hannan/162398
http://localhost/handle/Hannan/626814
ISSN: 0018-9162
volume: 49
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7404188.pdf1.15 MBAdobe PDFThumbnail
Preview File