Scale-based secured sensitive data storage for banking services in cloud

Abstract

Cloud-based banking system provides flexible services to customers through mobile banking, net banking etc. Banking data contains highly sensitive data. Current security mechanism applies security to entire customer information; if it is broken by attackers provide access to all information. The entire attribute encryption increases data size and requires more storage space in the cloud. It will increase the cloud usage cost. The proposed scale-based secure sensitive data (SSSD) storage technique provides a personalised level of security to user data through privacy score. The Likert Scale assignment and Dichotomous Response Matrix generation reduces the sensitive and nonsensitive data classification complexity. The sensitivity differs from user to user; privacy score identifies the common sensitive attributes for an entire user and association rule mining is used to find the related sensitive attributes for individual users. Simultaneous prediction and encryption of sensitive attributes through SSSD minimises space occupation and encryption time consumption effectively.