Privacy Preserving Mining in Code Profiling Data
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.
Quinn Taylor & Christophe Giraud-Carrier. (2010). Applications of data mining in software engineering. International Journal of Data Analysis Techniques and Strategies, 2(3), 243-257.
Antal, P., Fannes, G., Timmerman, D., Moreau, Y., & De Moor, B. (2003). Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection. Artificial Intelligence in Medicine, 29, 39-60.
Jian Wang, Yongcheng Luo, Yan Zhao, & Jiajin Le. (2009). A survey on privacy preserving data mining. In 1st International Workshop on Database Technology and Applications, 111-114.
Tu Honglei, Sun Wei, & Zhang Yanan. (2009). The research on software metrics and software complexity metrics. In International Forum on Computer Science-Technology and Applications, 131-136.
Yuriy Brun & Michael D. Ernst. (2004). Finding latent code errors via machine learning over program executions. In 26th International Conference on Software Engineering (ICSE), 480-490.
R. Mitchell & R. Chen. (2014). Adaptive intrusion detection of malicious unmanned air vehicles using behavior rule specifications. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(5), 593–604.
A.V.Krishna Prasad & Dr. S.Rama Krishna. (2010). Data mining for secure software engineering – Source code management tool case study. International Journal of Engineering Science and Technology, 2(7), 2667-2677.
Copyright (c) 2018 International Journal of Engineering and Management Research
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.