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2021-06-02
Anbumani, P., Dhanapal, R..  2020.  Review on Privacy Preservation Methods in Data Mining Based on Fuzzy Based Techniques. 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). :689—694.
The most significant motivation behind calculations in data mining will play out excavation on incomprehensible past examples since the extremely large data size. During late occasions there are numerous phenomenal improvements in data assembling because of the advancement in the field of data innovation. Lately, Privacy issues in data Preservation didn't get a lot of consideration in the process mining network; nonetheless, a few protection safeguarding procedures in data change strategies have been proposed in the data mining network. There are more normal distinction between data mining and cycle mining exist yet there are key contrasts that make protection safeguarding data mining methods inadmissible to mysterious cycle data. Results dependent on the data mining calculation can be utilized in different regions, for example, Showcasing, climate estimating and Picture Examination. It is likewise uncovered that some delicate data has a result of the mining calculation. Here we can safeguard the Privacy by utilizing PPT (Privacy Preservation Techniques) strategies. Important Concept in data mining is privacy preservation Techniques (PPT) because data exchanged between different persons needs security, so that other persons didn't know what actual data transferred between the actual persons. Preservation in data mining deals that not showing the output information / data in the data mining by using various methods while the output data is precious. There are two techniques used for privacy preservation techniques. One is to alter the input information / data and another one is to alter the output information / data. The method is proposed for protection safeguarding in data base environmental factors is data change. This capacity has fuzzy three-sided participation with this strategy for data change to change the first data collection.