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Authors
Affiliations
1 Velammal College of Engineering and Technology, Madurai, IN
2 Department of Computer Science, Velammal College of Engineering and Technology, Madurai, IN
3 Sri Ramakrishna Engineering College, Coimbatore, IN
4 Hindustan University, Chennai, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 3 (2013), Pagination: 123-128
Abstract
Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. So people have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data.The difficulty in privacy-sensitive domain is solved by the development of the Multi-Level Trust Privacy Preserving Data Mining (MLT-PPDM) where multiple differently perturbed copies of the same data are available to data miners at different trusted levels. In MLT-PPDM data owners generate perturbed data by various techniques like Batch generation and On-demand generation. MLT-PPDM can overcome the diversity attacks. Partial information hiding methodologies like random perturbation, random rotation perturbation are incorporated with MLT-PPDM to enhance data security and to prevent leakage of the sensitive data. The solution allows a data owner to generate perturbed copies of its data for arbitrary trust levels on demand. Finally MLT-PPDM approach is improved to tackle against the non-linear attacks. The time and space complexities are calculated for both techniques and the results show that on-demand algorithm is best among them.
Keywords
Gaussian Noise, Multi-Level Trust, Partial Information Hiding, Perturbation Technique, Single Level Trust.
