A Hybrid Approach for privacy preserving using randomization for data mining

Computer science and engineering

Many organizations large amount of data are collected. These data are further used by the organizations for the analysis purposes which help gaining useful knowledge. 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. A hybrid approach for used to improved security and accuracy to private data. Our novel hybrid approach towards privacy preserving k-anonymity and artificial neural network techniques are effective, scalable and no information loss.

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