Applications of Secure Multi-Party Computation in Financial Services

TL;DR

This paper discusses how Secure Multi-Party Computation (SMPC) enables privacy-preserving analysis of sensitive financial data, highlighting its potential to enhance security, transparency, and trust in digital financial services.

Contribution

It reviews current challenges and future directions for making SMPC protocols more scalable and efficient for practical use in finance.

Findings

SMPC can facilitate secure financial transactions

Current limitations include scalability and computational efficiency

Future research needed for practical deployment

Abstract

The concept of Secure Multi-Party Computation (SMPC) is a cryptographic service that allows generating analysis of sensitive data related to finance under the collaboration of all stakeholders without violating the privacy of the research participants. This article shows the increasing significance of privacy protection in the contemporary financial services, where various stakeholders should comply with stringent security and regulatory standards. It discusses the main issues of scalability, computational efficiency, and working with very large datasets, and it identifies the directions of future research to make SMPC protocols more practical and efficient. The results highlight the possibility of SMPC to facilitate safe, transparent, and trustful financial transactions in an ecosystem that is becoming more digital.