A REVIEW FOR PRIVACY PRESERVATION USING RANDOMAZATION FOR DATA MINING
P Patel Halak, D Patel Warish · Journal of Emerging Technologies and Innovative Research · 2016
In many organizations large amount of data are collected. These data are sometimes used by the organizations for data mining tasks. However, the data collected may contain private or sensitive information which should be protected.Privacy protection is an important issue if we release data for the mining or sharing purpose. Our technique protects the sensitive data with less information loss which increase data usability and also prevent the sensitive data for various types of attack. Data can also be reconstructed using our proposed technique. a novel hybrid method to achieve k-support anonymity based on statistical observations on the datasets. Our comprehensive experiments on real as well as synthetic datasets show that our techniques are effective and provide moderate privacy.clustring based noise techniques that not only preserve the privacy but also ensure effective data mining.