Abstract
Privacy-preserving data mining is a critical topic in data mining today. Because numerous companies and individuals are producing sensitive data or information these days, they do not wish to divulge their sensitive data, yet that data might be beneficial for data mining. As a result of privacy preserving mining, such data may be mined usefully without jeopardizing its privacy. Because the data is now safe owing to encryption, privacy may be retained by encrypting the database that is to be mined. Code profiling is a branch of software engineering in which we may utilize data mining to identify knowledge that will be valuable in future software development. We used privacy-preserving mining of code profiling data such as software metrics of diverse codes in this work. The accuracy of data mining results on real and encrypted data is compared. We also looked at the outcomes of privacy preserving mining in code profiling data and discovered some intriguing results.
Keywords: Data mining, Privacy protection, code profiling, and correlation coefficient
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