A Cryptographic Privacy Preserving Approach over Classification
Sivasankar Vakkalagadda, Satyanarayana Mummana · 2013
We proposed an efficient privacy preserving technique during the classification of data. We introduce a Cryptographic based approach that protects centralized sample data sets utilized for decision tree mining of data. Preservation of privacy is applied to sanitize the samples prior to their release to third parties in order to mitigate the threat of their inadvertent disclosure or reveal. In contrast to other sanitization approaches, our approach does not affect the accuracy efficiency of results of data mining .The decision tree can be built directly from the pre-processed data sets, it means originals do not need to be formed. Moreover, this approach provides an efficient privacy preserving technique over data mining and can be applied at any time during the data collection process so that privacy protection can be in effect even while samples are still being collected.