Data Mining Applications in Banking Sector While Preserving Customer Privacy

Abstract

In real-life data mining applications, organizations cooperate by using each other’s data on the same data mining task for more accurate results, although they may have different security and privacy concerns. Privacy-preserving data mining (PPDM) practices involve rules and techniques that allow parties to collaborate on data mining applications while keeping their data private. The objective of this paper is to present a number of PPDM protocols and show how PPDM can be used in data mining applications in the banking sector. For this purpose, the paper discusses homomorphic cryptosystems and secure multiparty computing. Supported by experimental analysis, the paper demonstrates that data mining tasks such as clustering and Bayesian networks (association rules) that are commonly used in the banking sector can be efficiently and securely performed. This is the first study that combines PPDM protocols with applications for banking data mining. Doi: 10.28991/ESJ-2022-06-06-014 Full Text: PDF

Research on Customer Relationship Segmentation of Apparel Retail Industry through Data Mining

“…If two of the values are high, it suggests that these customers are general-value customers. If only one of the values is high, it implies that these customers need to be retained; if none of the three values are high, it proves that these customers are potential customers [8][9][10][11]. This paper used the RFM model to establish the customer segmentation index system for the apparel retail industry based on customers' consumption behavior, as shown in Table 1.…”