Preservation of private information using Secure Multi-Party Computation

Abstract

Background/Objectives: Secure Multi-party Computation (SMC) method is used to secure individual's sensitive data during privacy preserving data mining and data publishing. This paper proposes a new protocol using real and ideal models of SMC to compute sum of multiple parties' private data without revealing their data to each other. Methods/Statistical Analysis: Many approaches have been utilized for preserving privacy of sensitive data such as anonymization, data perturbation and SMC. In SMC, several protocols have been used for this purpose. Secure sum protocol is one of the important protocols, which is used to calculate the sum of private data secretly. Multiple parties perform the addition and subtraction operation based on the secure sum protocol and they transfer the intermediate sum among them through a trusted third party. Finally, trusted third party transforms sum to all the parties. Findings: The computation and communication cost is calculated in each round of computation and compared with the existing protocol. The empirical result shows that the proposed protocol out performed than the existing protocol in terms of computation and communication complexity. Applications/Improvements: This protocol can be applied in various fields where the privacy preservation of sensitive data is needed such as insurance companies, banking system, government survey, hospitals, etc. The complexity of the protocol can be further reduced with high security in future.