SVD based Data Transformation Methods for Privacy Preserving Clustering
نویسندگان
چکیده
منابع مشابه
SVD based Data Transformation Methods for Privacy Preserving Clustering
Nowadays privacy issues are major concern for many government and other private organizations to delve important information from large repositories of data. Privacy preserving clustering which is one of the techniques emerged to addresses the problem of extracting useful clustering patterns from distorted data without accessing the original data directly. In this paper two hybrid data transfor...
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Knowledge extraction process poses certain problems like accessing sensitive, personal or business information. Privacy invasion occurs owing to the abuse of personal information. Hence privacy issues are challenging concern of the data miners. Privacy preservation is a complex task as it ensures the privacy of individuals without losing the accuracy of data mining results. In this paper, fuzzy...
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Despite its benefit in a wide range of applications, data mining techniques also have raised a number of ethical issues. Some such issues include those of privacy, data security, intellectual property rights, and many others. In this paper, we address the privacy problem against unauthorized secondary use of information. To do so, we introduce a family of geometric data transformation methods (...
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Preserving the privacy of individuals when data are shared for clustering is a complex problem. The challenge is how to protect the underlying data values subjected to clustering without jeopardizing the similarity between objects under analysis. In this short paper, we revisit a family of geometric data transformation methods (GDTMs) that distort numerical attributes by translations, scalings,...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2013
ISSN: 0975-8887
DOI: 10.5120/13473-1157