A more secure framework for open government data sharing based on federated learning

Abstract

Open government data, abbreviated as OGD, attracts significant public interest with substantial social value recently, which enables the government to make more accurate and efficient decisions based on real and comprehensive data. It also helps break down information silos, improve service quality and management efficiency, and enhance public trust in government activities. This is crucial for advancing public management modernization, fostering technological innovation, and strengthening governance capabilities. The focus of this study is how to solve the problem of more secure sharing of OGD. And we developed a more secure framework for open government data sharing based on federated learning. Inspired by the government data authorization operation model, this framework includes four categories of participants: OGD providers, OGD collectors, OGD operators, and OGD users. We further analyzed modeling techniques for horizontal federated learning, vertical federated learning, and federated transfer learning. By applying this framework to typical scenarios in China, its actual effectiveness has been illustrated in preventing information leakage, protecting data privacy, and improving model security, providing more reliable and efficient solutions for government governance and public services. Future research can continuously explore the application of privacy-computing-related technologies in secure sharing of OG to further enhance data security and the potential of OGD.