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.
翻译:空间数据共享在开启数据研究和提高政府机构透明度方面起着重要作用。然而,宝贵的空间数据,如高精度地理信息和个人交通记录等,不能公开,因为它们可能带来渗漏风险,如侵入、盗窃和擅自出售专有信息。当拥有机密数据的参与者彼此不信任,但希望使用其他数据集进行计算时,最常见的解决办法是向信任的第三方提供原始数据。然而,受信任的第三方经常受到攻击,数据泄漏。为了保持数据控制性,大多数公司和组织拒绝分享数据。在本研究中,我们引入安全多方计算空间数据共享,以解决共享问题。此外,我们描述了两个探索性空间数据分析协议的设计和实施:全球莫兰一号和当地莫兰一号。此外,我们建立了一个系统,以显示进程实现情况和结果的可视化。我们系统与现有的数据共享计划相兼容,我们的系统在确定正确结果时不会在空间数据共享过程中造成泄漏风险。