Secure Multi-Party Computation For Data Mining In Cryptographically Protected Environments

Abstract

A sophisticated cryptographic paradigm known as Secure Multi-Party Computation (SMPC) allows several parties to work together to calculate a function over their private inputs while maintaining the confidentiality of those inputs. SMPC makes it easier to share findings and insights about data mining in environments with cryptography protection while keeping individual data private. This research delves into the basic workings of SMPC, highlighting its potential uses in a range of industries, such as finance and healthcare, where sensitive data can obstruct data sharing. We discuss the difficulties in putting SMPC protocols into practice, like complexity and performance overhead, and we point out improvements in protocol efficiency that have made useful applications possible. Moreover, this study describes typical applications of SMPC, including secure auctions, cooperative machine learning, and privacy-preserving data processing. The goal of this study is to present a thorough overview of the function that SMPC plays in enabling safe and effective data mining operations by analysing current methods and investigating emerging trends. Our results highlight the need for continued investigation and improvement of SMPC techniques in order to promote broad use across many industries, eventually augmenting privacy protection and permitting beneficial data cooperation.