Big Data Set Privacy Preserving in Banking Using Machine Learning Techniques

S Anjali Devi, Meesala Bhagya Harika,InnamuriHarshitha,KodaliSarath Sai

The main objective is to Implementing privacy preserving for analysis of customer data in the banking sector. As banking allows lending to borrower and give deposits to the customers internal information such as financial and personal data can be obtained from online transactions, loan applications, chat records, call center interactions, and complaint records. As there is large amount of confidential data in banking the data need to be secured. Privacy regulations and other privacy concerns may prevent data holders from transferring information for performing data analysis to achieve a solution to this problem. Data mining techniques such as Clustering, KNN, SVM, Neural Networks are used these techniques are implemented to acquire more accuracy and provide better security for the data. Techniques like k-anonymity and l-diversity are having the drawbacks they do not provide appropriated privacy to the data. This paper presents methods where privacy and security of bank related sensitive data on one hand and resulting data set is mined without considerable loss in accuracy obtained in models.

Volume 11 | Issue 7

Pages: 609-620