| With the introduction of the Action Plan for Promoting the Development of Big Data,open government data platforms are flourishing at the national level.While open government platforms provide convenience to the public in their lives and work,they also bring about the problem of personal privacy leakage,which is detrimental to the rights and interests of the general public.In order to strike a balance between data openness and personal privacy protection,the Law of the People’s Republic of China on the Protection of Personal Information was promulgated and implemented in November 2021,emphasising in particular that "the activities of state organs in handling personal information shall be governed by this Law,and the handling of personal information shall be carried out in accordance with the authority and procedures prescribed by law".In principle,state organs shall not disclose the personal information they handle to the public.This fundamentally establishes that the protection of personal information takes precedence over the process of opening up government data,and that data security has become an irreversible trend.However,the sheer scale and volume of government data makes the privacy risks and their causes even more complex,and it is difficult to effectively manage them with a single regulatory measure.In order to clarify the causes of privacy risks in government data opening,balance the conflict between government data opening and privacy protection,and systematically analyse the factors influencing personal privacy protection in government data opening,it has become an urgent issue to be addressed.A total of 17 factors are classified into four dimensions: policy,technology,data and users.With the help of an explanatory structural model,a recursive structural model of the factors influencing the protection of personal privacy in open government data is constructed,and the 17 factors are divided into eight levels,which are then categorized into three levels: surface level,middle level and deep level.In order to validate the structured model,the cross-influence matrix multiplication method was used to classify the influencing factors into autonomous,dependent and independent factor groups.A comparative analysis revealed that the findings were highly consistent with the three levels in the structural model,further proving that the construction of the explanatory structural model was reasonable and scientific.Afterwards,by distributing questionnaires to administrative agencies,enterprises and institutions,and staff related to data privacy security,the obtained data were analysed and calibrated,and the fuzzy set qualitative comparative analysis method was used to process the obtained valid data using fs QCA software to derive five group configurations affecting personal privacy protection in government data openness,and to explore the conditional combinations of influencing factors related to privacy security and The study explores the multiple concurrent factors and cause-effect complex mechanisms between the combination of conditions affecting privacy security and individual privacy security,and reveals the core and supporting conditions contributing to individual privacy security from a histological perspective.Finally,based on the results of the hierarchical division of the ISM-MICMAC model and the five configurations of histories that affect personal privacy security,recommendations are made for the open government data to achieve privacy security from four perspectives:policy,technology,data and users.The aim is to help managers of open government data platforms to better understand the important role of each influencing factor on privacy security,so as to clarify management points in future privacy risk prevention and control. |
