Accumulative Privacy Preserving Data Mining Using Gaussian Noise Data Perturbation at Multi Level Trust

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Provided by: The International Journal of Innovative Research in Computer and Communication Engineering
Topic: Big Data
Format: PDF
Generally data mining develops the exact models about the collected data. Data perturbation, a widely employed and accepted Privacy Preserving Data Mining (PPDM) approach add random noise to original data, that prevent data miner to publish the accurate information about original data that is not allowed by data owner. Under the single level trust a data owner generate only one perturbed copy of its data with affixed amount of uncertainty. In this paper, the aim is to enlarge the scope of perturbation-based PPDM to Multi-Level Trust (MLT-PPDM).
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