Sneak

Abstract

Secure multiparty computation (SMC) is a technique that allows multiple parties to jointly compute a function while keeping their inputs private. This technique has gained significant attention due to its potential applications in various fields, including privacy-preserving healthcare, politics and finance.

SMC involves a set of protocols that enable parties to achieve secure computation and analysis. These protocols typically involve a trusted third party or a cryptographic algorithm that ensures the privacy of the inputs. Some popular cryptographic algorithms used in SMC include homomorphic encryption, secret sharing, and the one discussed in this thesis, denoted as the round-robin scramble.

This thesis focuses on the realization of a secure system for analysing sensitive data across multiple nodes in a distributed network. The thesis discusses the approach, design, and implementation of such a system with emphasis on security, usability, and scalability. Security is of upper importance to prevent information disclosure, followed by usability to ensure practicality and ease of use. Scalability is addressed to accommodate networks of varying sizes. The proposed system, named Sneak, offers near-zero information disclosure by leveraging Python, enabling robust and valid complex analysis operations across distributed networks.