Investigations and Design of Privacy-Preserving Data Mining Technique for Secure Data Publishing
Keywords:
PPDM, Privacy, Noise Inclusion Algorithm, Dimensionality Reduction, Experimental StudyAbstract
Growing digital data motivates us to develop data-intensive applications, for providing diverse areas of solutions. In this context, sometimes we need cross-domain data, where multiple data owners are contributing their data. In this situation, data dimensionality and privacy is the main concern. Thus, to deal with the privacy issues recent techniques are applying cryptographic and noise-based techniques. But, these techniques are resource-consuming and suffer from poor performance in terms of data utility. In this paper, we investigate a Privacy Preserving Data Mining (PPDM) model to deal with complexities of PPDM modeling. First, we have described the dimensionality issue with PPDM and discuss the utilization of dimensionality reduction techniques such as Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principle Component Analysis (KPCA), and Correlation Coefficient (CC). Second, we have considered the issue of privacy and utility of data. The aim is to balance privacy requirements and data utility. Thus, a client-side random noise inclusion algorithm is proposed. Finally, by picking suitable and effective techniques, we implemented a complete PPDM model. The experiments on publically available UCI datasets are performed. Finally, performance in terms of data quality matrix, privacy matrix, and performance matrix is discussed.
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