Privacy Preserving Clustering on Distorted data
نویسندگان
چکیده
منابع مشابه
Privacy Preserving Clustering on Distorted data
In designing various security and privacy related data mining applications, privacy preserving has become a major concern. Protecting sensitive or confidential information in data mining is an important long term goal. An increased data disclosure risks may encounter when it is released. Various data distortion techniques are widely used to protect sensitive data; these approaches protect data ...
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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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As most previous studies on privacy-preserving data mining placed specific importance on the security of massive amounts of data from a static database, consequently data undergoing privacy-preservation often leads to a decline in the accuracy of mining results. Furthermore, following by the rapid advancement of Internet and telecommunication technology, subsequently data types have transformed...
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ژورنال
عنوان ژورنال: IOSR Journal of Computer Engineering
سال: 2012
ISSN: 2278-8727,2278-0661
DOI: 10.9790/0661-0522529