Research On Traceable Declassification Method And Its Usability Evaluation Scheme For Vector Geographic Data

With the arrival of the big data era,the role of geographic information data in social production and life is becoming increasingly important.As sharing demands continue to expand,geographic information data has penetrated into various areas of life,including vector and raster geographic data.As an important geographic information resource,vector geographic data has a higher value in geographic information applications than raster geographic data due to its higher accuracy and faster transmission.However,in practical production,issues such as illegal dissemination and information leakage often arise,which can threaten national security and social interests.Therefore,the security of vector geographic data needs to be addressed urgently.To protect the shared security of geographic information data,we need to accelerate the socialization process of geographic information applications,promote the popularization of geographic information results,and meet the demands of the times.Nowadays,the application scope of vector geographic data is getting wider,but when sharing,the security protection issues and availability of data have become bottlenecks that constrain its development.The declassification technology is the geographic information security technology developed under this application demand.The declassification technology is divided into attribute declassification and geometric precision declassification,and the essence of geometric precision declassification technology is to add a certain degree of interference to the coordinates of data to reduce its location accuracy so as to promote the application sharing method.Analyzing the existing geometric declassification techniques,it is found that they cannot meet the management requirements of traceability when declassified data is shared.Therefore,this paper proposes a geometric accuracy declassification algorithm based on digital fingerprints.With the support of a digital fingerprint,the algorithm can adjust the offset size of the digital fingerprint embedded in data to meet the declassification accuracy required by geometric precision declassification and realize the traceable management of decrypted data when it is shared and used.The declassification algorithm has been experimentally verified to achieve a good geometric declassification effect and to trace the illegal propagators of declassified data when they are used.The algorithm can not only achieve the purpose of geometric precision declassification but also meet the security of sharing declassified data and solve the problem of traceability when declassified data is used.With the expansion of sharing requirements and the rapid development of declassified technology,how to measure the availability of declassified data has also become an urgent problem to be solved.Therefore,in order to evaluate the usability of declassified data,this paper proposes a quantitative evaluation scheme,which includes four evaluation indexes such as accuracy evaluation,graphical morphological similarity,spatial directional relationship consistency,and topological relationship consistency.These quantitative indexes can evaluate the usability of declassified data in three aspects:visualization of decrypted results,reliability of declassified models,and validity of declassified results.After experimental comparison and verification,this quantitative evaluation scheme has good credibility and provides scientific support for measuring the shared usability of declassified data in use.In summary,this paper proposes a traceable geometric precision declassification algorithm based on digital fingerprints and a quantitative evaluation scheme to evaluate the usability of declassified data,which solves the problems of sharing security and sharing usability of vectorial geographic data.The research results are of great significance for protecting national geographic information security and promoting the shared application of vector geographic data.