Problem Statement
Collaborative data analysis among multiple parties is often hindered by concerns about
privacy and the security of sensitive information. Secure Multi-Party Computation
(SMPC) addresses these concerns by allowing parties to jointly compute functions over
their inputs while keeping those inputs private.
Abstract
This project focuses on implementing SMPC for privacy-preserving data analysis. The
objective is to enable multiple parties to collaboratively analyze their datasets without
revealing sensitive information. The system will use cryptographic techniques to
perform computations on encrypted data, ensuring privacy while deriving valuable
insights from joint analyses.
Outcome
A secure multi-party computation framework that facilitates collaborative data analysis
without exposing individual datasets, promoting privacy-preserving practices in
multi-party scenarios.