PROVIDING SECURITY FOR SHOPPING PREFERENCES USING DIFFERENTIAL TRANSACTION SCHEMES

Abstract

Online banks may expose customers' buying interests due to the numerous attacks. Users can put a halt to their spending before sending the data to their online bank, which may or may not treat the data confidentially. Direct use of different data protection in online banking would actually cause issues because current differential data protection systems do not account for the noise limit problem.

The current system includes a scheme called Optimized Differential private Online transaction (O-DIOR) that can be used by online banks to set consumption limits that account for extra noises. We revise O-DIOR and develop a new scheme called RO-DIOR that allows us to select new boundaries while maintaining compliance with the differential privacy definition. Further, we provide extensive theoretical analysis to show that our schemes are able to fulfil the differential privacy requirement.

Keeping customers' personal information safe when banking online is complicated

by the fact that different customers have different privacy expectations. In the past, differential privacy was applied directly. The inherent noise in such systems makes it impossible to draw any conclusions about consumers' intentions or behaviours based on transaction data. Following this, we propose a new scheme that uses optimised differential privacy to update and improve existing schemes without requiring the user to choose single limits at random. Our systems have been shown to comply with the differential confidentiality requirement, and we intend to use this to our advantage when addressing the boundary issue under differential privacy. This includes, but is not limited to, safeguarding shopping locations, resolving data transmission protection issues, and creating safeguarding techniques for shopping applications.