Bonfring International Journal of Software Engineering and Soft Computing

Online ISSN: 2277-5099 | Print ISSN: 2250-1045 | Frequency: 4 Issues/Year

Impact Factor: 0.375 | International Scientific Indexing(ISI) calculate based on International Citation Report(ICR)

Partial Information Hiding in Multi-Level Trust Privacy Preserving Datamining

M.S. Ramya

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Abstract:

Privacy Preserving Data Mining (PPDM) is used to extract relevant knowledge from large amount of data and at the same time protect the sensitive information from the data miners. The problem 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 is available to data miners at different trusted levels. In MLT-PPDM data owners generate perturbed data by various techniques like Parallel generation, Sequential generation and On-demand generation. MLT-PPDM is robust against the diversity attacks. In my work partial information hiding methodologies like random rotation perturbation, retention replacement and K-anonymity are incorporated with MLT-PPDM to enhance data security and to prevent leakage of the sensitive data. Finally MLT-PPDM approach is improved to tackle against the non-linear attacks.

Keywords: K-Anonymity, Diversity Attack, Random Rotation Perturbation, Non-Linear Attack, Multi-Level Trust, Parallel Generation, Sequential Generation, On-Demand Generation

Volume: 2 | Issue: Special Issue on Communication Technology Interventions for Rural and Social Development

Pages: 11-15

Issue Date: February , 2012