Securing Financial Sector in the Cloud: A Multi-Cloud Approach to Fraud Detection Using Secure Multi-Party Computation

Kumar Dixit, Tanmaya (2024) Securing Financial Sector in the Cloud: A Multi-Cloud Approach to Fraud Detection Using Secure Multi-Party Computation. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (1MB) | Preview

Abstract

In the world of digitalization, banks and other financial institution face huge difficulty in detecting fraudulent transactions such as money laundering, credit card fraud and many other financial frauds. Challenge arises with traditional fraud detection systems within an individual organization and the reason is limited visibility of the data each organization is analysing the data pattern in one isolated which means only the transaction involving its customers which restricts them from detecting any complex fraud patterns that extend over multiple entities. Improving fraud detection in the financial sector especially in this digital world where everything is going on cloud is very crucial due to the rise in online transactions and sophisticated fraud schemes. This research proposes a multi-cloud framework which integrates Secure Multi party computation (SMPC), Homomorphic encryption (HE) and machine learning algorithm specifically Decision tree, random forest and Logistic regression, to address these challenges. The proposed framework will allow different financial institutes to share and analyse data securely without compromising data privacy. SMPC allows multiple party to compute function on encrypted data while keeping the data of each institution involve safe and secure, HE will enhance the security by allowing the computation on encrypted data and decision tree will be used to identify the fraudulent pattern. This combines approach will help In improving fraud detection while maintaining data privacy providing a secure and scalable solution for financial sector.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Gupta, Shaguna UNSPECIFIED
Subjects:	H Social Sciences > HG Finance Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	03 Jul 2025 11:50
Last Modified:	03 Jul 2025 11:50
URI:	https://norma.ncirl.ie/id/eprint/8029

Actions (login required)

View Item