Synthetic Data for Payment Systems: AI-Powered Privacy-Preserving Testing (Published)
In modern banking, ensuring that new payment systems operate accurately and securely requires extensive testing. However, testing with real-world data introduces privacy risks, and synthetic data offers a promising alternative. This paper explores the potential of Generative AI for producing realistic, privacy‑compliant synthetic transaction data. The proposed approach addresses challenges such as data privacy, diverse dataset creation, and the ability to simulate rare or edge-case scenarios—thus enhancing the robustness of payment systems.
abstract
Keywords: Privacy, Synthetic data, generative AI, machine learning, payment systems
DBMS Integration with Cloud Computing (Published)
A Cloud database management system is a distributed database that brings computing as a service beyond a product. A cloud computing system with database management system is the allocation of required resources, software and information between different devices over a network which is basically on the internet. It is to be expected that this number will arise incomparably in the future. According to the consequences, there is an emerging interest in outsourcing database management skills to third parties that can afford these tasks for cheap and best cost according to the direction of computation just like merging it into the cloud. In this paper, we focus the current tendency in database management system and the potentiality of creating this as one of the best service provider in the cloud. Furthermore we also designed architecture of database management system in the cloud.
Keywords: Cloud Computing, DBMS, Data Outsourcing, Database Management System, Privacy