Privacy Preserving k Secure Sum Protocol

TL;DR

This paper introduces a privacy-preserving secure sum protocol using data segmentation and randomization techniques within Secure Multiparty Computation to enhance data privacy during collaborative sum calculations.

Contribution

It proposes a novel secure sum protocol that increases data privacy by segmenting data and applying randomization, improving resistance to data hacking.

Findings

Enhanced privacy through data segmentation and randomization

Increased complexity deters data hacking attempts

Protocol maintains accurate sum computation with privacy protections

Abstract

Secure Multiparty Computation (SMC) allows parties to know the result of cooperative computation while preserving privacy of individual data. Secure sum computation is an important application of SMC. In our proposed protocols parties are allowed to compute the sum while keeping their individual data secret with increased computation complexity for hacking individual data. In this paper the data of individual party is broken into a fixed number of segments. For increasing the complexity we have used the randomization technique with segmentation