  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.
