CN-121984718-A - Smart city data classification and encryption method, device, equipment and medium

CN121984718ACN 121984718 ACN121984718 ACN 121984718ACN-121984718-A

Abstract

The application relates to the technical field of data security, in particular to a smart city data classification and encryption method, device, equipment and medium, wherein the method comprises the steps of acquiring multi-source heterogeneous data from a plurality of government service systems through a data interface, wherein the multi-source heterogeneous data comprises at least one of population basic information, legal registration information and space geographic information; the method comprises the steps of carrying out real-time classification grading treatment on multi-source heterogeneous data, calculating a data sensitivity score S based on a weighted scoring model, dynamically determining encryption intensity E according to the sensitivity score S and an environment threat index T, wherein E=k.S+gamma.T, k and gamma are scaling coefficients, selecting a corresponding encryption algorithm based on E values, storing the encrypted data into a layered storage architecture, and integrating the data into a full life cycle process of data management, wherein the full life cycle process comprises data standardization, quality inspection and asset operation links. The application has the effect of improving the encryption flexibility when encrypting data in the smart city during data transmission.

Inventors

LI YAN
ZHANG CHENYU
HONG JIAJIE
Pi Mengyuan
CHEN YUFENG
LAN ZEYONG
Lian Yirong
Zhou Liehui
Situ jie
Jia Hongman

Assignees

中通服中睿科技有限公司

Dates

Publication Date: 20260505
Application Date: 20251231

Claims (10)

1. A smart city data classification and encryption method, characterized in that the smart city data classification and encryption method comprises: acquiring multi-source heterogeneous data from a plurality of government service systems through a data interface, wherein the multi-source heterogeneous data comprises at least one of population basic information, legal registration information and space geographic information; carrying out real-time classification grading treatment on the multi-source heterogeneous data, and calculating a data sensitivity score S based on a weighted scoring model, wherein S=w 1 ·A+w 2 ·B+w 3 .C, wherein A is a field sensitivity base value, B is an access frequency factor, C is a compliance weight, and w 1 、w 2 、w 3 is an adjustable weight coefficient; dynamically determining encryption strength E according to the sensitivity score S and the environment threat index T, wherein E=k.S+gamma.T, k and gamma are scaling coefficients, and selecting a corresponding encryption algorithm based on the E value; the encrypted data is stored in a layered storage architecture and integrated into a full life cycle flow of data management, wherein the full life cycle flow comprises data standardization, quality inspection and asset operation links.
2. The smart city data classification and encryption method according to claim 1, wherein the real-time classification and classification process is performed on the multi-source heterogeneous data, and a data sensitivity score S is calculated based on a weighted scoring model, where s=w 1 ·A+w 2 ·B+w 3 ·c, where a is a field sensitivity base value, B is an access frequency factor, C is a compliance weight, and w 1 、w 2 、w 3 is an adjustable weight coefficient, and specifically comprising: Determining a field sensitivity basic value A through matching a predefined sensitive field dictionary with a data field, wherein the field sensitivity basic value A comprises an identity card number field, a name field and an address field; calculating an access frequency factor B based on the historical access log using the formula b=log 10 (1+F) ×20, where F is the number of daily accesses; And automatically distributing compliance weight C according to the business subject to which the data belongs, wherein the compliance weight C comprises legal sensitive data and general data.
3. The classification, classification and encryption method according to claim 2, wherein after the automatic allocation of compliance weights C according to the service subject to which the data belongs, the compliance weights C include legal sensitive data and general data, the classification, classification and encryption method further comprises: introducing a time decay factor D (t) =e (-0.1 t) to adjust the sensitivity score, wherein t is the number of days of data storage; And calculating an association risk value R based on the data blood-edge relationship, and automatically improving the sensitivity level when the data are associated with the high-sensitivity table.
4. The smart city data classification grading and encrypting method according to claim 1, wherein the encryption strength E is dynamically determined according to the sensitivity score S and the environmental threat index T, wherein e=k·s+γ·t, k and γ are scaling coefficients, and the corresponding encrypting algorithm is selected based on the E value, specifically comprising: Monitoring a system security log in real time, and calculating an environment threat index T, wherein T= (abnormal access times/total access times) multiplied by 100; And acquiring the encryption intensity E value and a corresponding algorithm judgment threshold value, and mapping an encryption algorithm according to the encryption intensity E value, wherein the ChaCha20-128 algorithm is selected when E is less than or equal to a first threshold value, the AES-256 algorithm is selected when the first threshold value 0<E is less than or equal to a second threshold value, and the RSA-3072 algorithm is selected when E is more than or equal to a second threshold value.
5. The smart city data classification and encryption method according to claim 1, wherein the storing the encrypted data in a hierarchical storage architecture is integrated into a data governance full life cycle process, including data standardization, quality inspection, asset operation links, specifically including: In the data standardization, adding a security metadata tag to the encrypted data, wherein the security metadata tag comprises a sensitivity level, an encryption algorithm type and a key version number; in the quality inspection, the integrity of the encrypted field is verified, and the encrypted field is compared through a hash algorithm.
6. A classification, classification and encryption device for smart city data is characterized in that, the smart city data classification and encryption device comprises: The system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring multi-source heterogeneous data from a plurality of government service systems through a data interface, and the multi-source heterogeneous data comprises at least one of population basic information, legal registration information and space geographic information; The data classification and grading module is used for carrying out real-time classification and grading processing on the multi-source heterogeneous data, and calculating a data sensitivity score S based on a weighted scoring model, wherein S=w 1 ·A+w 2 ·B+w 3 .C, A is a field sensitivity base value, B is an access frequency factor, C is a compliance weight, and w 1 、w 2 、w 3 is an adjustable weight coefficient; The data encryption module is used for dynamically determining encryption intensity E according to the sensitivity score S and the environment threat index T, wherein E=k.S+gamma.T, k and gamma are scaling coefficients, and a corresponding encryption algorithm is selected based on the E value; the encryption transmission storage module is used for storing the encrypted data into the layered storage architecture and integrating the data into a full life cycle flow of data management, and comprises data standardization, quality inspection and asset operation links.
7. The smart city data sort ranking and encrypting apparatus of claim 6, wherein said data sort ranking module comprises: The data field matching sub-module is used for matching data fields through a predefined sensitive field dictionary and determining a field sensitivity basic value A, wherein the field sensitivity basic value A comprises an identity card number field, a name field and an address field; an access factor calculation sub-module for calculating an access frequency factor B based on the historical access log, using the formula b=log 10 (1+F) ×20, where F is the number of daily accesses; the weight setting sub-module is used for automatically distributing compliance weight C according to the business subject to which the data belongs, wherein the compliance weight C comprises legal sensitive data and general data.
8. The smart city data sort ranking and encrypting apparatus of claim 6, wherein said data sort ranking module further comprises: The attenuation factor calculation sub-module is used for introducing a time attenuation factor D (t) =e (-0.1 t) to adjust the sensitivity score, wherein t is the number of days of data storage; and the risk value calculation sub-module is used for calculating an associated risk value R based on the data blood-edge relationship, and automatically improving the sensitivity level when the data are associated with the high-sensitivity table.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the steps of the smart city data classification ranking and encryption method according to any one of claims 1 to 5 are implemented by the processor when executing the computer program.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the smart city data classification ranking and encryption method of any one of claims 1 to 5.

Description

Smart city data classification and encryption method, device, equipment and medium Technical Field The application relates to the technical field of data security, in particular to a method, a device, equipment and a medium for classifying, grading and encrypting smart city data. Background At present, with the continuous promotion of smart city construction, mass multisource information such as government affair data, enterprise data, personal data and the like is concentrated and gathered into a unified big data platform, which plays an important role in promoting efficient operation of cities, improving public service level and the like, so that city managers can more comprehensively know city operation conditions to make more scientific decisions, more data resource support innovation development is provided for enterprises, and more convenient living experience is brought to citizens. However, these data face increasingly severe security challenges during the full life cycle of collection, storage, exchange and application, and data security management is a critical issue in the development of smart cities. The existing smart city data security management field, the mainstream practice usually adopts the traditional mode. In terms of data classification and classification, a rigid policy based on manually predefined rules is mostly relied on, for example, a specific field like "identification card number" is directly set to a high sensitivity level by a hard coding manner, and common predefined rules have an explicit condition judgment mode, for example, such rule IF field name= "identification card number" THEN sensitivity level=high, IF data subject= "public map" THEN sensitivity level=low. In the data encryption strategy, a static encryption mode of 'one cut' is adopted, and an encryption algorithm with uniform intensity is applied to all data, for example, all the data are encrypted by using an encryption algorithm with certain fixed intensity, and an AES-256 encryption scheme is adopted. Based on a data classification grading mode of manually predefined rules, dynamic changes of data features are difficult to cover, classification accuracy is remarkably reduced along with system complexity improvement, and context information is ignored by single-dimension judgment, so that data classification is inaccurate. Disclosure of Invention In order to improve encryption flexibility in data encryption during data transmission in a smart city, the application provides a smart city data classification and encryption method, device, equipment and medium. The first object of the present application is achieved by the following technical solutions: A smart city data classification and encryption method, the smart city data classification and encryption method comprising: acquiring multi-source heterogeneous data from a plurality of government service systems through a data interface, wherein the multi-source heterogeneous data comprises at least one of population basic information, legal registration information and space geographic information; carrying out real-time classification grading treatment on the multi-source heterogeneous data, and calculating a data sensitivity score S based on a weighted scoring model, wherein S=w 1·A+w2·B+w3.C, wherein A is a field sensitivity base value, B is an access frequency factor, C is a compliance weight, and w 1、w2、w3 is an adjustable weight coefficient; dynamically determining encryption strength E according to the sensitivity score S and the environment threat index T, wherein E=k.S+gamma.T, k and gamma are scaling coefficients, and selecting a corresponding encryption algorithm based on the E value; the encrypted data is stored in a layered storage architecture and integrated into a full life cycle flow of data management, wherein the full life cycle flow comprises data standardization, quality inspection and asset operation links. By adopting the technical scheme, the multi-source heterogeneous data are acquired from a plurality of government service systems, so that population basic information, legal registration information, space geographic information and the like can be comprehensively covered, and abundant and wide data sources are provided for subsequent data processing. The method comprises the steps of carrying out real-time classification grading treatment on multi-source heterogeneous data, calculating a data sensitivity score S by using a weighted scoring model, wherein a field sensitivity basic value A can be determined according to a predefined sensitive field dictionary matched data field to enable classification to have a basic basis, calculating an access frequency factor B based on a historical access log to embody the dynamic condition of data use, automatically distributing a compliance weight C according to a business theme to which the data belongs, and comprehensively considering business compliance. The multiple factors are combined and weigh