A privacy risk identification framework of open government data: A mixed-method study in China

Abstract

Open government data (OGD) has great potential to promote economic growth, stimulate innovation, and improve service efficiency. However, as more and more private information is collected by government information systems, private data become increasingly vulnerable. Thus, governments must monitor the privacy risks of OGD. The focus of this study is to identify privacy risk factors in the process of developing OGD. Using a mixed-method design, we developed a privacy risk identification framework based on evidence from China. According to the results of qualitative interviews, the privacy risk identification framework mainly includes five risk dimensions: data risk, institutional risk, technical risk, structural risk, and behavioral risk. We identified 17 risk factors under these five dimensions. We further developed the measurement items for each risk factor and verified the indicator framework through quantitative methods. Our research provides a theoretical basis for identifying the privacy risks in OGD, supporting governments in discovering and dealing with them accordingly. Future research can continuously explore potential privacy risks arising from merging technologies such as generative artificial intelligence when applied to OGD.