Spatial data sharing with secure multi-party computation for exploratory spatial data analysis

TL;DR

This paper introduces a secure multi-party computation framework for spatial data sharing, enabling collaborative analysis like Moran's I without risking data leakage, thus enhancing privacy and trust among participants.

Contribution

It presents novel protocols for secure spatial data analysis using multi-party computation, ensuring privacy while accurately computing spatial statistics.

Findings

System correctly computes Moran's I statistics without data leakage

Secure protocols outperform traditional sharing methods in privacy preservation

Demonstrates practical implementation with visualization of results

Abstract

Spatial data sharing plays a significant role in opening data research and promoting government agency transparency. However, valuable spatial data, like high-precision geographic information and personal traffic records, cannot be made public because they may incur leakage risks such as intrusion, theft, and the unauthorised sale of proprietary information. When participants with confidential data distrust each other but want to use the other datasets for calculations, the most common solution is to provide their original data to a trusted third party. However, the trusted third party frequently risks being attacked and having the data leaked. To maintain data controllability, most companies and organisations refuse to share their data. In this study, we introduce secure multi-party computation to spatial data sharing to address the sharing problem. Additionally, we describe the design and implementation of the protocols of two exploratory spatial data analyses: global Moran's I and local Moran's I. Furthermore, we build a system to demonstrate process realisation and results visualisation. Comparing our system with existing data-sharing schemes, our system Identifies the correct result without incurring leaking risks during spatial data sharing.