Security Provision for Miners Data Using Singular Value Decomposition in Privacy Preserving Data Mining
Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection of the confidentiality of this information has been a long-term goal for the database security research community and for the government statistical agencies. Recent advances in data mining and machine learning algorithms have increased the disclosure risks that one may encounter when releasing data to outside parties. It brings out a new branch of data mining, known as Privacy Preserving Data Mining (PPDM). Privacy-Preserving is a major concern in the application of data mining techniques to datasets containing personal, sensitive, or confidential information. Data distortion is a critical component to preserve privacy in security-related data mining applications; we propose a Singular Value Decomposition (SVD) method for data distortion. We focus primarily on privacy preserving data clustering. Our proposed method Singular Value Decomposition (SVD) distorts only confidential numerical attributes to meet privacy requirements.
|Year of publication:||
|Authors:||Narendar Machha ; M.Y. Babu|
Global Journal of Computer Science and Technology
|Type of publication:||Article|
Global Journal of Computer Science and Technology; Vol 10, No 6 (2010): Global Journal of Computer Science and Technology
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