Privacy Preserving Mining in Code Profiling Data

  • Meenakshi Kathayat
Keywords: Privacy preserving, Data mining, Code Profiling, Correlation coefficient


Privacy preserving data mining is an important issue nowadays for data mining. Since various organizations and people are generating sensitive data or information these days. They don’t want to share their sensitive data however that data can be useful for data mining purpose. So, due to privacy preserving mining that data can be mined usefully without harming the privacy of that data. Privacy can be preserved by applying encryption on database which is to be mined because now the data is secure due to encryption. Code profiling is a field in software engineering where we can apply data mining to discover some knowledge so that it will be useful in future development of software. In this work we have applied privacy preserving mining in code profiling data such as software metrics of various codes. Results of data mining on actual and encrypted data are compared for accuracy. We have also analyzed the results of privacy preserving mining in code profiling data and found interesting results.


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How to Cite
Meenakshi Kathayat. (2018). Privacy Preserving Mining in Code Profiling Data. International Journal of Engineering and Management Research, 8(5), 24-28.