A method and system for secure sharing of urban and rural planning and construction data

Abstract

The present invention relates to the technical field of data processing, and particularly relates to a method and system for secure sharing of urban and rural planning and construction data. The method first obtains urban and rural planning and construction data, and data files of different types of urban and rural planning and construction data all include text data and non-text data; then, according to the sensitivity that can reflect the secrecy level of urban and rural planning and construction data, the urban and rural planning and construction data are classified into confidentiality levels to determine the confidentiality levels of the urban and rural planning and construction data, so as to provide a reference basis for the encryption method before data security sharing; finally, based on the confidentiality levels of the urban and rural planning and construction data, the urban and rural planning and construction data are securely shared. This method can effectively identify the levels and sensitivities of urban and rural planning and construction data, enabling key data to be protected in a timely manner, non-key data to be protected correspondingly, avoiding overprotection of non-key data, and improving the sharing efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method and system for secure sharing of urban and rural planning and construction data. Background Art

[0002] With the acceleration of the urbanization process and the rapid development of information technology, various types of data involved in urban and rural planning and construction are increasing continuously. These data usually include basic geographic information, social and economic information, environmental resource information, public facility construction information, transportation and infrastructure information, etc. In order to improve urban management efficiency and optimize resource allocation, the sharing and collaborative use of relevant data become particularly important, especially among enterprises, research institutions, and the general public. However, urban and rural planning and construction data usually involve a large amount of sensitive information, such as personal privacy, corporate secrets, etc. If this information fails to be properly protected during the sharing process, it may lead to serious data leakage, abuse, or even data tampering.

[0003] In the protection of urban and rural planning and construction data, text data is an important carrier for information transmission. However, text data usually has the phenomenon that key information is intertwined with non-key information, making it difficult to apply traditional classification methods for confidentiality levels. Different from objective data such as geographic data and economic data that can be classified according to established standards and rules, the sensitive information in text data may present a relatively complex distribution form. For example, in a text containing a planning scheme, there may be both specific land use plans and some environmental impact assessment reports. Some paragraphs may involve sensitive commercial data information, while other parts belong to public information or low-risk data. Traditional static classification methods for confidentiality levels cannot effectively identify the hierarchy and sensitivity of this information, resulting in some key data not being protected in a timely manner, while non-key data may be overprotected, affecting the data sharing efficiency.

[0004] Therefore, it is urgent to develop a method and system for secure sharing of urban and rural planning and construction data to solve the above problems. Summary of the Invention

[0005] In order to solve the technical problem that the hierarchy and sensitivity of urban and rural planning and construction data cannot be effectively identified, resulting in some key data not being protected in a timely manner, while non-key data may be overprotected, affecting the data sharing efficiency, the purpose of the present invention is to provide a method and system for secure sharing of urban and rural planning and construction data, and the specific technical solutions adopted are as follows:

[0006] In a first aspect, an embodiment of the present invention provides a method for secure sharing of urban and rural planning and construction data, and the method includes:

[0007] Obtain urban and rural planning and construction data, where the urban and rural planning and construction data includes basic geographic information data, socio-economic data, land use data, environmental resource data, infrastructure data, and building and housing data. Different types of urban and rural planning and construction data all include data files with text data and non-text data;

[0008] According to the sensitivity of the urban and rural planning and construction data, classify the confidentiality level of the urban and rural planning and construction data to determine the confidentiality level of the urban and rural planning and construction data;

[0009] Based on the confidentiality level of the urban and rural planning and construction data, perform secure sharing of the urban and rural planning and construction data.

[0010] In some embodiments, the step of classifying the confidentiality level of the urban and rural planning and construction data according to the sensitivity of the urban and rural planning and construction data to determine the confidentiality level of the urban and rural planning and construction data includes:

[0011] Obtain the confidentiality level of historical urban and rural planning and construction data, where the confidentiality level includes top secret (extremely sensitive), confidential (highly sensitive), secret (moderately sensitive), internal public, and external public;

[0012] Determine the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data;

[0013] Based on the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data and the confidentiality level of the historical urban and rural planning and construction data, determine the confidentiality level of the urban and rural planning and construction data.

[0014] In some embodiments, the step of determining the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data includes:

[0015] Extract the text data of each data file in the urban and rural planning and construction data;

[0016] Determine the word vector of the text data of each data file in the urban and rural planning and construction data;

[0017] According to the following formula, determine the confidentiality representativeness of the word vector of the text data of each data file in the urban and rural planning and construction data:

[0018] ;

[0019] In the formula, represents the confidentiality representativeness of the word vector of the -th data file of the urban and rural planning and construction data of the word vector, represents the word vector of the -th data file of the urban and rural planning and construction data Inverse document frequency A function representing the calculation of similarity Indicates the existence of a word vector Identical to the word vector, the word vector set of the text data in the nth data file of the historical urban and rural planning and construction data nth data file corresponding to the confidentiality level of the historical urban and rural planning and construction data, with a value of , Indicates the nth word vector corresponding to the text data of the nth data file of the urban and rural planning and construction data Indicates the sequence number of the word in the text data of the nth data file of the urban and rural planning and construction data Indicates the sequence number of the data files in the historical urban and rural planning and construction data;

[0020] Based on the confidentiality representativeness of the word vectors of the text data of each data file in the urban and rural planning and construction data, determine the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data.

[0021] In some embodiments, determining the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data includes:

[0022] According to the following formula, based on the storage proportion of the text data of each data file in the urban and rural planning and construction data to the data file, determine the number of confidential representative words of each data file in the urban and rural planning and construction data:

[0023] ;

[0024] In the formula, Indicates the number of confidential representative words of the nth data file of the urban and rural planning and construction data Indicates the storage proportion of the text data in the nth data file of the urban and rural planning and construction data Indicates the number of word vectors included in the nth data file of the urban and rural planning and construction data Indicates a hyperparameter with a value of 20;

[0025] Sort the confidentiality representativeness of the word vectors of the text data of each data file in the urban-rural planning and construction data from large to small, and select the words corresponding to the confidentiality representativeness of the same number of word vectors as the number of confidential representative words of a certain data file from large to small in the sorted sequence as the confidential representative words of this data file;

[0026] Determine the confidentiality correlation between the urban-rural planning and construction data and the historical urban-rural planning and construction data according to the following formula:

[0027] ;

[0028] In the formula, represents the confidentiality correlation between the th data file of the urban-rural planning and construction data and the th data file of the historical urban-rural planning and construction data, represents a function to calculate the cosine similarity between two vectors, represents the number of confidential representative words of the th data file of the urban-rural planning and construction data, represents the l-th word vector corresponding to the l-th word in the text data of the th data file of the historical urban-rural planning and construction data, where l represents the serial number of the word in the text data of the th data file of the historical urban-rural planning and construction data.

[0029] In some embodiments, based on the confidentiality correlation between the urban-rural planning and construction data and the historical urban-rural planning and construction data and the confidentiality level of the historical urban-rural planning and construction data, determining the confidentiality level of the urban-rural planning and construction data includes:

[0030] Obtain the confidentiality correlation between the urban-rural planning and construction data and the historical urban-rural planning and construction data, and the confidentiality correlation between the urban-rural planning and construction data and the historical urban-rural planning and construction data includes the confidentiality correlation between each data file in the urban-rural planning and construction data and each data file in the historical urban-rural planning and construction data;

[0031] Perform clustering analysis on the confidentiality correlation between each data file in the urban-rural planning and construction data and each data file in the historical urban-rural planning and construction data by means of DBSCAN clustering, where the distance value between any two data files is the reciprocal of the confidentiality correlation between the two data files, the value of the scanning radius of the clustering analysis is 5, and the value of the minimum number of points included in the clustering analysis is 20;

[0032] According to the clustering analysis results, determine the confidentiality level of a certain data file in the historical urban and rural planning and construction data as the comparability of the confidentiality levels of each data file in the urban planning and construction data;

[0033] Based on the comparability, determine the probability that the confidentiality level of a certain data file in the historical urban and rural planning and construction data is the confidentiality level of each data file in the urban planning and construction data;

[0034] Take the confidentiality level corresponding to the probability with the maximum value as the confidentiality level of the data file in the urban and rural planning and construction data.

[0035] In some embodiments, according to the following formula, determine the comparability of the confidentiality level of a certain data file in the historical urban and rural planning and construction data as the confidentiality level of each data file in the urban planning and construction data:

[0036] ;

[0037] In the formula, represents the comparability of the confidentiality level of the th data file in the set of data files corresponding to the non-outlier points in the cluster of the clustering analysis results, represents the set of data files corresponding to the non-outlier points in the cluster of the clustering analysis results, represents the normalization function, represents the th serial number of the data file in the set of data files corresponding to the non-outlier points in the cluster of the clustering analysis results, z represents the serial number of the zth data file in the set of data files corresponding to the non-outlier points in the cluster of the clustering analysis results, represents the maximum value function, represents the confidentiality correlation between the th data file and the zth data file in the set of data files corresponding to the non-outlier points in the cluster of the clustering analysis results.

[0038] In some embodiments, according to the following formula, determine the probability that the confidentiality level of a certain data file in the historical urban and rural planning and construction data is the confidentiality level of each data file in the urban planning and construction data:

[0039] ;

[0040] In the formula, represents the probability that the confidentiality level of the th data file in the set of data files corresponding to the non-outlier points in the cluster of the clustering analysis results belongs to the kth confidentiality level, represents the minimum number of points included in the clustering analysis, with a value of 20, A set of data files with confidentiality levels, indicating the confidentiality correlation between the th data file and the pth data file in the set of data files corresponding to non-outlier points in the clusters representing the clustering analysis results, where k represents the labels of five confidentiality levels.

[0041] In some embodiments, according to the following formula, the confidentiality level corresponding to the probability of obtaining the maximum value is used as the confidentiality level of the data files in the urban and rural planning and construction data:

[0042] ;

[0043] In the formula, represents the confidentiality level of the th data file in the urban and rural planning and construction data, represents the function for finding the maximum point, and k takes values of 1, 2,..., 5.

[0044] In some embodiments, the confidentiality levels of the urban and rural planning and construction data include top secret (extremely sensitive), confidential (highly sensitive), secret (medium sensitive), internal public, and external public. Based on the confidentiality levels of the urban and rural planning and construction data, the secure sharing of the urban and rural planning and construction data includes:

[0045] For data files with high confidentiality levels in urban and rural planning and construction, they are encrypted using a high-strength encryption algorithm and then securely shared to ensure that the data files cannot be cracked, where high confidentiality levels include top secret (extremely sensitive) and confidential (highly sensitive);

[0046] For data files with medium confidentiality levels in urban and rural planning and construction, they are encrypted using a medium-strength encryption algorithm and then securely shared to balance security and sharing efficiency, where medium confidentiality levels include secret (medium sensitive);

[0047] For data files with low confidentiality levels in urban and rural planning and construction, they are encrypted using a lightweight encryption algorithm and then securely shared to improve sharing efficiency, where low confidentiality levels include internal public and external public.

[0048] In a second aspect, an embodiment of the present invention provides an urban and rural planning and construction data secure sharing system, and the system includes:

[0049] An acquisition module, configured to acquire urban and rural planning and construction data, where the urban and rural planning and construction data includes basic geographic information data, social and economic data, land use data, environmental resource data, infrastructure data, and building and housing data, and different types of urban and rural planning and construction data all include data files with text data and non-text data;

[0050] A determination module, configured to classify the confidentiality level of the urban and rural planning and construction data according to the sensitivity of the urban and rural planning and construction data, and determine the confidentiality level of the urban and rural planning and construction data;

[0051] A sharing module, configured to perform secure sharing of the urban and rural planning and construction data based on the confidentiality level of the urban and rural planning and construction data.

[0052] The present invention has the following beneficial effects:

[0053] A method and system for secure sharing of urban and rural planning and construction data provided by an embodiment of the present invention. The method first obtains urban and rural planning and construction data, and different types of urban and rural planning and construction data all include data files with text data and non-text data; then, according to the sensitivity that can reflect the secrecy level of the urban and rural planning and construction data, classify the confidentiality level of the urban and rural planning and construction data, and determine the confidentiality level of the urban and rural planning and construction data, so as to provide a reference basis for the encryption method before data security sharing; finally, based on the confidentiality level of the urban and rural planning and construction data, perform secure sharing of the urban and rural planning and construction data. This method can effectively identify the level and sensitivity of urban and rural planning and construction data, so that key data is protected in a timely manner, non-key data is protected correspondingly, avoid overprotection of non-key data, and improve the sharing efficiency. Description of the Drawings

[0054] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0055] Figure 1 It is a flowchart of a method for secure sharing of urban and rural planning and construction data provided by an embodiment of the present invention;

[0056] Figure 2 It is an effect diagram of clustering analysis in a method for secure sharing of urban and rural planning and construction data provided by an embodiment of the present invention;

[0057] Figure 3 It is a structural diagram of a system for secure sharing of urban and rural planning and construction data provided by an embodiment of the present invention. Detailed Embodiments

[0058] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0060] Next, the specific scheme of a method for secure sharing of urban and rural planning and construction data provided by the present invention will be specifically described in conjunction with the accompanying drawings. Please refer to Figure 1 , the method includes:

[0061] Step 101, obtain urban and rural planning and construction data.

[0062] Among them, urban and rural planning and construction data includes basic geographic information data, social and economic data, land use data, environmental resource data, infrastructure data, and building and housing data. Different types of urban and rural planning and construction data all include data files with text data and non-text data.

[0063] Before secure data sharing, it is necessary to encrypt the urban and rural planning and construction data, and the encryption methods for urban and rural planning and construction data with different sensitivity levels are different. Given the data without classified confidentiality levels, its confidentiality level can be inferred and determined by analyzing its association with the data with known confidentiality levels, and step 102 is introduced.

[0064] In some embodiments, the text data may include urban planning reports, meeting minutes, planning regulations and policy documents, design specification documents, social and economic analysis reports, etc. The non-text data includes geographic information data (coordinates, vector maps, remote sensing images), economic data (GDP, employment rate, industrial ratio), land use data (GIS data, plot numbers), building data (building height, area, material type), sensor data (temperature, humidity, flow rate), etc.

[0065] Step 102, according to the sensitivity of the urban and rural planning and construction data, divide the confidentiality level of the urban and rural planning and construction data to determine the confidentiality level of the urban and rural planning and construction data.

[0066] Sensitivity can reflect the secrecy level of the data file, and the confidentiality level of the urban and rural planning and construction data can provide a reference basis for the encryption method before secure data sharing.

[0067] In some embodiments, step 102 includes:

[0068] Step 1021: Obtain the confidentiality level of historical urban and rural planning and construction data. The confidentiality levels include top secret (extremely sensitive), secret (highly sensitive), confidential (moderately sensitive), internal disclosure, and external disclosure.

[0069] The confidentiality level of historical urban and rural planning and construction data can provide a basis for the confidentiality level of urban and rural planning and construction data to be classified.

[0070] Step 1022: Determine the confidentiality correlation between urban and rural planning and construction data and historical urban and rural planning and construction data.

[0071] To determine whether the confidentiality level of a certain data file in historical urban and rural planning and construction data can provide a basis for the confidentiality level of a certain data file in urban and rural planning and construction data, it is necessary to determine the confidentiality correlation between the two.

[0072] In some embodiments, Step 1022 includes:

[0073] Step 10221: Extract the text data of each data file in the urban and rural planning and construction data.

[0074] For subsequent analysis of the differences between text data and non-text data. Extract the text data of each data file in the urban and rural planning and construction data, and extract the text data in units of one data file.

[0075] Step 10222: Determine the word vectors of the text data of each data file in the urban and rural planning and construction data.

[0076] Word vectors can reflect data characteristics.

[0077] When considering the confidentiality correlation between two data files, it is necessary to analyze the similarity between the corresponding descriptive data. For data with a relatively high similarity in corresponding descriptions, their confidentiality levels also have a relatively high probability of being the same. Therefore, it is first necessary to obtain the word vectors with strong confidentiality representativeness in each data.

[0078] In some embodiments, the specific implementation method of Step 10222 is: Use the word segmentation tool jieba to process the text information in the urban and rural planning and construction data, and then obtain the word vectors corresponding to each word in each text data according to the traditional word2vec.

[0079] Step 10223: According to the following formula, determine the confidentiality representativeness of the word vectors of the text data of each data file in the urban and rural planning and construction data:

[0080] ;

[0081] In the formula, represents the word vector of the th data file in the urban and rural planning and construction data Confidential representativeness, reflecting the confidentiality level of word vectors, Indicates the th data file's word vector of urban and rural planning and construction data Inverse document frequency, which is the frequency of a single word in a single data file divided by the frequency in all data files, can reflect the proportion of the word in the data file, Indicates the function for calculating similarity, Indicates the existence of a word vector Same as the word vector, the set of word vectors of the text data in the th data file of historical urban and rural planning and construction data, which can reflect the scope of the data file containing the word vector Indicates the th data file's corresponding confidentiality level of historical urban and rural planning and construction data, reflecting the confidentiality level of the data file, with values , where the values from small to large represent the confidentiality level from low to high, Indicates the th data file's text data's th word's corresponding word vector, which can reflect data characteristics, Indicates the th data file's text data's word serial number in urban and rural planning and construction data, Indicates the serial number of the data file in urban and rural planning and construction data, Indicates the serial number of the data file in historical urban and rural planning and construction data.

[0082] The larger it is, the greater the proportion of the word in urban and rural planning and construction data, The larger it is, the more similar the confidentiality levels of the word in the data files with known confidentiality levels (data files of historical urban and rural planning and construction data) and in multiple data files of urban and rural planning and construction data, and the stronger the confidentiality representativeness of the word, that is, The larger it is.

[0083] According to the confidentiality representativeness sizes corresponding to the obtained word vectors, the confidentiality correlation size between two data files can be calculated. Considering non-text data (such as maps), its text data is relatively less, while the amount of data it contains is relatively large. At this time, the relatively small text data may contain key information of the corresponding data, such as annotation information in the map, the area where the map belongs, etc. However, due to the small amount of text data, the inverse document frequency of each word vector is small, resulting in a low confidentiality representativeness of each word. Therefore, step 10224 is introduced to solve the above problems.

[0084] ​Step 10224: Determine the confidential correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data based on the confidential representativeness of the word vectors of the text data in each data file of the urban and rural planning and construction data.

[0085] The confidential correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data can reflect the similarity in the degree of confidentiality between the two data files, so as to predict the confidentiality level of the urban and rural planning and construction data in the future.

[0086] In some embodiments, Step 10224 includes:

[0087] (1) Based on the following formula, determine the number of confidential representative words in each data file of the urban and rural planning and construction data according to the storage ratio of the text data in each data file of the urban and rural planning and construction data to the data file:

[0088] ;

[0089] In the formula, represents the number of confidential representative words in the th data file of the urban and rural planning and construction data, reflecting the quantity of confidential representative words in the data file and taking a non-zero value, represents the storage ratio of the text data in the th data file of the urban and rural planning and construction data, reflecting the proportion of the text data in the data file, represents the number of word vectors included in the th data file of the urban and rural planning and construction data, reflecting the quantity of the word vector included in the data file, represents a hyperparameter with a value of 20.

[0090] It should be noted that when , that is, when the number of word vectors included in the th data file of the urban and rural planning and construction data is less than or equal to 20, it is determined that the number of confidential representative words in the th data file of the urban and rural planning and construction data is ; when , that is, when the number of word vectors included in the th data file of the urban and rural planning and construction data is greater than 20, it is determined that the number of confidential representative words in the th data file of the urban and rural planning and construction data is the product of the storage ratio of the text data in the th data file of the urban and rural planning and construction data and the hyperparameter and the storage ratio of the non-text data in the th data file of the urban and rural planning and construction data and the The sum of the products of the number of word vectors contained in each data file, that is .

[0091] (2) Sort the confidentiality representativeness of the word vectors of the text data of each data file in the urban-rural planning and construction data from large to small. Among the sorted sequence, select the words corresponding to the confidentiality representativeness of the same number of word vectors as the number of confidential representative words of a certain data file from large to small as the confidential representative words of this data file.

[0092] By sorting the confidentiality representativeness of the word vectors from large to small, the confidentiality degree of the word vectors can be grasped. Among the sorted sequence, select the words corresponding to the confidentiality representativeness of the same number of word vectors as the number of confidential representative words of a certain data file from large to small. The result of the number calculation in the above step (1) can be used to select the words corresponding to the confidentiality representativeness of the same number of word vectors as the number of confidential representative words of a certain data file, so as to obtain the confidential representative words of this data file for subsequent calculation of the confidentiality correlation between the urban-rural planning and construction data and the historical urban-rural planning and construction data.

[0093] (3) According to the following formula, determine the confidentiality correlation between the urban-rural planning and construction data and the historical urban-rural planning and construction data:

[0094] ;

[0095] In the formula, represents the confidentiality correlation between the th data file of the urban-rural planning and construction data and the th data file of the historical urban-rural planning and construction data, which can reflect the similarity degree of the two data files in terms of confidentiality degree. represents the function to calculate the cosine similarity between two vectors. represents the number of confidential representative words of the th data file of the urban-rural planning and construction data and the value is not zero. represents the l-th word vector corresponding to the l-th word in the text data of the th data file of the historical urban-rural planning and construction data, where l represents the serial number of the word in the text data of the th data file of the historical urban-rural planning and construction data.

[0096] By obtain the cosine similarity degree between all the word vectors of the th data file in the urban-rural planning and construction data and all the word vectors of the th data file in the historical urban-rural planning and construction data. Add the weight to obtain the average value of the cosine similarity degree.

[0097] Step 1023: Determine the confidentiality level of the urban and rural planning and construction data based on the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data, as well as the confidentiality level of the historical urban and rural planning and construction data.

[0098] The determined confidentiality level of the urban and rural planning and construction data can provide a reference basis for the encryption method before data security sharing.

[0099] Regarding the confidentiality correlation between any two data files obtained after the above Step 1022, for data files with known confidentiality levels, the confidentiality correlation between any two data files with the same confidentiality level should be relatively high. However, considering that the confidential part of the content may not be described in words, there will still be a small number of data files with relatively low correlation at the same confidentiality level. Therefore, Step 1023 is introduced to predict the confidentiality level.

[0100] In some embodiments, Step 1023 includes:

[0101] Step 10231: Obtain the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data. The confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data includes the confidentiality correlation between each data file in the urban planning and construction data and each data file in the historical urban and rural planning and construction data.

[0102] Step 10232: Through the DBSCAN clustering method, perform clustering analysis on the confidentiality correlation between each data file in the urban planning and construction data and each data file in the historical urban and rural planning and construction data. Among them, the distance value between any two data files is the reciprocal of the confidentiality correlation between the two data files. The value of the scanning radius of the clustering analysis is 5, which reflects the scope of the clustering analysis. The value of the minimum number of points included in the clustering analysis is 20, which reflects the minimum number of data files for clustering analysis.

[0103] The clustering analysis can obtain the distribution trend of the confidentiality correlation between data files. And the stronger the confidentiality correlation of a data file, the more likely it is to be distributed in the same cluster. The closer the distribution position of the data file is to the cluster center of its corresponding cluster, it indicates that the confidentiality level of the data file is closer to the confidentiality level corresponding to the cluster center.

[0104] As Figure 2 shown, there are still some data files with relatively low confidentiality correlation between the data files on the clustering edge and the data files within the cluster. And different clusters represent data files with different confidentiality levels, and the encryption methods they require are different.

[0105] Step 10233: Determine the comparability of the confidentiality level of a certain data file in the historical urban and rural planning and construction data with the confidentiality levels of each data file in the urban planning and construction data according to the clustering analysis results.

[0106] The clustering analysis results can reflect the clustering situation of the data files corresponding to each confidentiality level. When the confidentiality correlation between the data files with known confidentiality levels and the clustering center is relatively large, the comparability for reference is higher when predicting the confidentiality level of the data files with unknown confidentiality levels.

[0107] In some embodiments, Step 10233 is implemented according to the following formula:

[0108] ;

[0109] In the formula, represents the comparability of the confidentiality level of the th data file in the set of data files corresponding to the non-outlier points in the clustering of the clustering analysis results, reflecting the degree of reference of the confidentiality level of the th data file, represents the set of data files corresponding to the non-outlier points in the clustering of the clustering analysis results, that is, the set of data files in the clustering, represents the normalization function, represents the serial number of the th data file in the set of data files corresponding to the non-outlier points in the clustering of the clustering analysis results, z represents the serial number of the zth data file in the set of data files corresponding to the non-outlier points in the clustering of the clustering analysis results, represents the maximum value function, represents the confidentiality correlation between the th data file and the zth data file in the set of data files corresponding to the non-outlier points in the clustering of the clustering analysis results, reflecting the degree of similarity in confidentiality between the two data files.

[0110] When , that is, when the th data file belongs to the set of data files in the clustering, the value of the comparability of the confidentiality level of the th data file is a fixed value of 1, that is, the set of data files where the th data file is located has been found; when , that is, when the th data file does not belong to the set of data files in the clustering, the value of the comparability of the confidentiality level of the th data file is the th data file and the set of data files The normalized value of the maximum of the confidential correlation between the z-th data files, so that when the th data file does not belong to the set of data files in the cluster When, find a set of data files suitable for the th data file, The larger the value, the stronger the confidential correlation between the two data files, then The larger the value, The larger the value, the stronger the comparability.

[0111] Step 10234, based on the comparability, determine the probability that the confidential level of a certain data file in the historical urban and rural planning construction data is the confidential level of each data file in the urban planning construction data.

[0112] Through each data file with a known confidential level, determine the data file with an unknown confidential level. Respectively, at each confidential level, based on the comparability, find the data file with a relatively high similarity degree between the data file with an unknown confidential level and the data file with a known confidential level, and obtain the confidential level probability of the data file with an unknown confidential level to reflect the possible degree that the confidential level of the data file with an unknown confidential level belongs to each confidential level.

[0113] In some embodiments, according to the following formula, implement step 10234:

[0114] ;

[0115] In the formula, represents the probability that the confidential level of the p-th data file in the set of data files corresponding to the non-outlier points in the cluster of the clustering analysis result belongs to the k-th confidential level, reflecting the possible degree that the confidential level of the p-th data file belongs to the k-th confidential level, represents the minimum number of points included in the clustering analysis, with a value of 20, represents the set of data files at the confidential level, that is, the clustering set composed of data files at the k-th confidential level, confidential level data file set, represents the th data file in the set of data files corresponding to the non-outlier points in the cluster of the clustering analysis result and the confidential correlation between the p-th data file, reflecting the degree of similarity between the two data files in terms of confidentiality level, and k represents the label of the five confidential levels. The larger the value, the greater the reference degree of the confidential level of the th data file, The larger the value, the greater the degree of similarity between the two data files in terms of confidentiality level, and the greater the possible degree that the confidential level of the p-th data file belongs to the k-th confidential level, that is, The larger it is.

[0116] Step 10235: Use the confidentiality level corresponding to the probability of obtaining the maximum value as the confidentiality level of the data file in the urban and rural planning and construction data, so as to obtain the accurate confidentiality level of the data file.

[0117] In some embodiments, step 10235 is implemented according to the following formula:

[0118] ;

[0119] In the formula, represents the confidentiality level of the th data file in the urban and rural planning and construction data, represents the function for finding the maximum point, and k takes values of 1, 2,..., 5, can find the confidentiality level corresponding to obtaining the maximum value at , so as to obtain the accurate confidentiality level of the data file.

[0120] Step 103: Based on the confidentiality level of the urban and rural planning and construction data, perform secure sharing of the urban and rural planning and construction data, so as to ensure the security of the data and prevent the data from being leaked during sharing.

[0121] In some embodiments, the confidentiality levels of the urban and rural planning and construction data include top secret (extremely sensitive), secret (highly sensitive), confidential (moderately sensitive), internal public, and external public. Based on the confidentiality level of the urban and rural planning and construction data, step 103 includes:

[0122] Step 1031: For the data files with a high confidentiality level in urban and rural planning and construction, use a high-strength encryption algorithm for encryption and then perform secure sharing to ensure that the data files cannot be cracked. Among them, the high confidentiality level includes top secret (extremely sensitive) and secret (highly sensitive) .

[0123] Data files with a high confidentiality level (top secret (extremely sensitive) : major financial data; secret (highly sensitive) : future land development and infrastructure construction plans) need to be shared after high-strength encryption to ensure the security of the data.

[0124] In some embodiments, the high-strength encryption algorithm can be the AES-256 encryption algorithm.

[0125] Step 1032: For the data files with a medium confidentiality level in urban and rural planning and construction, use a medium-strength encryption algorithm for encryption and then perform secure sharing to balance security and sharing efficiency. Among them, the medium confidentiality level includes confidential (moderately sensitive) 。

[0126] Data files with a medium confidentiality level (secret, moderately sensitive : approved projects, tender data, environmental assessments) need to be shared after medium-strength encryption to achieve a balance between security and sharing efficiency.

[0127] In some embodiments, the medium-strength encryption algorithm can be the AES-128 encryption algorithm.

[0128] Step 1033, for data files with a low confidentiality level in urban and rural planning and construction, use a lightweight encryption algorithm for encryption and then perform secure sharing to improve sharing efficiency, where the low confidentiality level includes internal public and external public 。

[0129] Data files with a low confidentiality level (internal public : business processes, planning research data, external public : published plans, public transportation information) need to be shared after lightweight encryption, thereby improving sharing efficiency.

[0130] In some embodiments, the lightweight encryption algorithm can be the DES encryption algorithm.

[0131] In some embodiments, according to the identity and needs of the user, different roles (such as public users, ordinary staff, senior management, core decision-making personnel) are defined for different accounts, and corresponding data access permissions are assigned to ensure that users can only access the data within their permissions, so that the shared data users have access permissions and ensure the security of the data.

[0132] In some embodiments, according to the role and permissions of the user, it is determined whether to allow access. After the access is run, the decrypted data is delivered to the user, and at the same time, a data access log is recorded for subsequent auditing and monitoring.

[0133] In summary, a method for secure sharing of urban and rural planning and construction data provided by an embodiment of the present invention can effectively identify the hierarchy and sensitivity of urban and rural planning and construction data, so that key data is protected in a timely manner, non-key data is protected correspondingly, overprotection of non-key data is avoided, and sharing efficiency is improved.

[0134] In a second aspect, an embodiment of the present invention provides a system for secure sharing of urban and rural planning and construction data. Refer to Figure 3 ,the system includes:

[0135] An acquisition module 301, configured to acquire urban and rural planning and construction data, where the urban and rural planning and construction data includes basic geographic information data, social and economic data, land use data, environmental resource data, infrastructure data, and building and housing data, and different types of urban and rural planning and construction data all include data files with text data and non-text data.

[0136] A determination module 302, configured to classify the confidentiality level of the urban and rural planning and construction data according to the sensitivity of the urban and rural planning and construction data, and determine the confidentiality level of the urban and rural planning and construction data.

[0137] A sharing module 303, configured to perform secure sharing of the urban and rural planning and construction data based on the confidentiality level of the urban and rural planning and construction data.

[0138] In summary, a secure sharing system for urban and rural planning and construction data provided by an embodiment of the present invention can effectively identify the levels and sensitivities of urban and rural planning and construction data, so that key data is protected in a timely manner, non-key data is protected correspondingly, overprotection of non-key data is avoided, and the sharing efficiency is improved.

[0139] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the advantages or disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0140] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for securely sharing urban and rural planning and construction data, characterized in that: The method comprises: Acquire urban and rural planning and construction data, the urban and rural planning and construction data including basic geographic information data, socio-economic data, land use data, environmental resource data, infrastructure data, and building and housing data, and different types of urban and rural planning and construction data include data files with text data and non-text data; According to the sensitivity of the urban and rural planning and construction data, classify the urban and rural planning and construction data into confidentiality levels and determine the confidentiality level of the urban and rural planning and construction data; Based on the confidentiality level of the urban and rural planning and construction data, securely share the urban and rural planning and construction data; Determining the confidentiality level of urban and rural planning and construction data includes: The confidentiality level of historical urban and rural planning and construction data is obtained, including top secret and extremely sensitive, confidential and highly sensitive, secret and moderately sensitive, internally disclosed, and externally disclosed; Determining the confidential relevance between the urban and rural planning and construction data and the historical urban and rural planning and construction data; Determining the confidentiality level of the urban and rural planning and construction data based on the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data and the confidentiality level of the historical urban and rural planning and construction data; The determining of the confidential correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data includes: Extracting text data from each data file in the urban and rural planning and construction data; Determine the word vector of the text data of each data file in the urban and rural planning and construction data; According to the following formula, the confidential representativeness of the word vector of the text data of each data file in the urban and rural planning and construction data is determined: ； In the formula, Indicates the urban and rural planning and construction data word vectors for data files Confidential representation of Indicates the urban and rural planning and construction data word vectors for data files The inverse document frequency of represents the function of finding similarity, Representing existence and word vectors The same word vector of historical urban and rural planning and construction data The word vector set of text data in the data file, The historical urban and rural planning and construction data The confidentiality level corresponding to the data file is , Indicates the urban and rural planning and construction data The text data of the data file The word vector corresponding to the word, Indicates the urban and rural planning and construction data The ordinal number of the word in the text data of the data file, Indicates the number of data files in urban and rural planning and construction data. Indicates the number of data files in the historical urban and rural planning and construction data; Based on the confidentiality representativeness of the word vector of the text data of each data file in the urban and rural planning and construction data, the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data is determined.

2. A method for securely sharing urban and rural planning and construction data according to claim 1, characterized in that: Determine the confidential relevance between the urban and rural planning and construction data and historical urban and rural planning and construction data, including: According to the following formula, based on the storage ratio of the text data of each data file in the urban and rural planning and construction data to the data file, the number of confidential representative words in each data file in the urban and rural planning and construction data is determined: ； In the formula, Indicates the urban and rural planning and construction data The number of confidential representative words in the data file, Indicates the urban and rural planning and construction data The storage ratio of text data in a data file, Indicates the urban and rural planning and construction data The number of word vectors contained in the data file, represents a hyperparameter, with a value of 20; The confidentiality representativeness of the word vectors of the text data of each data file in the urban and rural planning and construction data is sorted from large to small, and the words corresponding to the confidentiality representativeness of the word vectors having the same number as the number of confidential representative words of a data file are selected from the sorted sequence from large to small as the confidential representative words of the data file; The confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data is determined according to the following formula: ； In the formula, Indicates the urban and rural planning and construction data The data files and historical urban and rural planning and construction data confidential correlation between data files, represents a function that finds the cosine similarity between two vectors. Indicates the urban and rural planning and construction data The number of confidential representative words in the data file, The historical urban and rural planning and construction data The word vector corresponding to the lth word of the text data of the data file, l represents the lth word of the historical urban and rural planning and construction data The ordinal number of the word in the text data of a data file.

3. A method for securely sharing urban and rural planning and construction data according to claim 1, characterized in that: Determining the confidentiality level of the urban and rural planning and construction data based on the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data and the confidentiality level of the historical urban and rural planning and construction data includes: Acquire the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data, wherein the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data includes the confidentiality correlation between each data file in the urban planning and construction data and each data file in the historical urban and rural planning and construction data; By using DBSCAN clustering method, cluster analysis is performed on the confidentiality correlation between each data file in the urban planning and construction data and each data file in the historical urban and rural planning and construction data, wherein the distance between any two data files is the inverse of the confidentiality correlation between the two data files, the scanning radius of the cluster analysis is 5, and the minimum number of included points of the cluster analysis is 20; Determine, based on the cluster analysis result, the comparability of the confidentiality level of a data file in the historical urban and rural planning and construction data to the confidentiality level of each data file in the urban planning and construction data; Based on the comparability, determine the probability that the confidentiality level of a data file in the historical urban and rural planning and construction data is the confidentiality level of each data file in the urban planning and construction data; The confidentiality level corresponding to the probability of obtaining the maximum value is used as the confidentiality level of the data file in the urban and rural planning and construction data.

4. A method for securely sharing urban and rural planning and construction data according to claim 3, characterized in that: The confidentiality level of a data file in the historical urban and rural planning and construction data is determined to be comparable to the confidentiality level of each data file in the urban planning and construction data according to the following formula: ； In the formula, The first data file set corresponding to the non-outlier point in the cluster of the cluster analysis result Comparability of confidentiality levels of data files, A set of data files corresponding to non-outlier points in the clusters representing the cluster analysis results, represents the normalization function, The first data file set corresponding to the non-outlier point in the cluster of the cluster analysis result The sequence number of the data file, z represents the sequence number of the zth data file in the data file set corresponding to the non-outlier point in the cluster of the cluster analysis result, represents the maximum value function, The first data file set corresponding to the non-outlier point in the cluster of the cluster analysis result The confidential correlation between the zth data file and the zth data file.

5. A method for securely sharing urban and rural planning and construction data according to claim 4, characterized in that: The probability that the confidentiality level of a data file in the historical urban and rural planning and construction data is the confidentiality level of each data file in the urban planning and construction data is determined according to the following formula: ； In the formula, represents the probability that the confidentiality level of the p-th data file in the data file set corresponding to the non-outlier point in the cluster of the cluster analysis result belongs to the k-th confidentiality level, Indicates the minimum number of points included in the cluster analysis, the value is 20. Indicates A collection of confidential data files. The first data file set corresponding to the non-outlier point in the cluster of the cluster analysis result The confidentiality correlation between the pth data file and the pth data file, k represents the labels of the five confidentiality levels.

6. A method for securely sharing urban and rural planning and construction data according to claim 5, characterized in that: According to the following formula, the confidentiality level corresponding to the probability of obtaining the maximum value is used as the confidentiality level of the data file in the urban and rural planning and construction data: ； In the formula, Indicates the urban and rural planning and construction data The confidentiality level of the data files, Represents the function of finding the maximum point, and k takes values ​​of 1, 2, ..., 5.

7. A method for securely sharing urban and rural planning and construction data according to claim 1, characterized in that: The confidentiality levels of urban and rural planning and construction data include top secret, extremely sensitive, confidential, highly sensitive, secret, moderately sensitive, internally disclosed, and externally disclosed. Based on the confidentiality level of urban and rural planning and construction data, the urban and rural planning and construction data are securely shared, including: For data files with high confidentiality levels in urban and rural planning and construction, they are encrypted using high-strength encryption algorithms and then shared securely to ensure that the data files cannot be cracked. The high confidentiality levels include top secret, extremely sensitive, and confidential, highly sensitive; For data files with medium confidentiality level in urban and rural planning and construction, they are encrypted with medium-strength encryption algorithm and then securely shared to balance security and sharing efficiency. The medium confidentiality level includes secret medium sensitivity; For data files with a low confidentiality level in urban and rural planning and construction, a lightweight encryption algorithm is used to encrypt them and then share them securely to improve sharing efficiency. The low confidentiality level includes internal and external disclosure.

8. A system for securely sharing urban and rural planning and construction data, the system being used to implement the method of claim 1, characterized in that: The system comprises: An acquisition module is used to acquire urban and rural planning and construction data, wherein the urban and rural planning and construction data includes basic geographic information data, social and economic data, land use data, environmental resource data, infrastructure data, and building and housing data. Different types of urban and rural planning and construction data include data files with text data and non-text data. A determination module, used to classify the urban and rural planning and construction data into confidentiality levels according to the sensitivity of the urban and rural planning and construction data, and determine the confidentiality level of the urban and rural planning and construction data; The sharing module is used to securely share the urban and rural planning and construction data based on the confidentiality level of the urban and rural planning and construction data.

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