A data security management method and system for a server

Abstract

The application relates to the technical field of electric digital data processing, in particular to a data security management method and system for a server, which comprises the following steps: collecting a plurality of storage data of each user in the server in a plurality of historical time periods, a usage frequency of the plurality of storage data, a time length of the usage frequency, and a time difference value between an end time point corresponding to the usage frequency and a current time point; obtaining an activity degree and an access sensitivity of the plurality of storage data of each user, taking the product of the access sensitivity and the activity degree as a risk coefficient of the plurality of storage data of each user, and performing hierarchical encryption on the plurality of storage data of each user based on the risk coefficient. The application solves the problem that the static encryption strategy in the prior art cannot dynamically adjust the encryption strength.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology. More specifically, this invention relates to a data security management method and system for servers. Background Technology

[0002] In the context of rapid informatization and digitalization, servers, as the core infrastructure for enterprises, institutions, and platforms to process and store data, are increasingly attracting attention due to their data security issues. With the expansion of business and the increase in the number of users, servers store a large amount of user data, including documents, logs, database records, configuration files, and user behavior information. This data is often highly sensitive and valuable. Data breaches, tampering, loss, or unauthorized access can not only cause economic losses but also trigger serious privacy and legal risks. Therefore, establishing a scientific and efficient data security management method has become an important issue in ensuring server operational security and the integrity of user data.

[0003] In existing data security management solutions, static encryption strategies are commonly used, which pre-determine the encryption level based on the initial label of the data (such as sensitivity level, business type, or user identity). For example, high-level encryption standards (such as AES-256) are used for data marked as "highly sensitive," while low-strength encryption (such as AES-128) or no encryption is used for "low-sensitivity" data.

[0004] While existing technologies have improved data security to some extent, they overlook the dynamic changes in user behavior and data usage frequency. In practical applications, some data may face higher risks due to frequent user access and high activity levels in a short period. If low-level encryption methods are still used, it is difficult to effectively prevent potential attacks or abnormal operations. Furthermore, attackers often capture active data to steal information, implant malicious commands, or initiate illegal requests; static strategies lack the ability to respond to such dynamic risks. Secondly, when facing advanced persistent threats (APT attacks) or internal data misuse, static strategies struggle to identify data exposure risks caused by abnormal behavior, thus failing to dynamically adjust encryption strength. Summary of the Invention

[0005] To address the problem of the inability to dynamically adjust encryption strength as mentioned in the background art, the present invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a data security management method for a server, comprising: collecting multiple stored data of each user in the server within multiple historical time periods, the usage frequency of the multiple stored data, the duration of the usage frequency, and the time difference between the end time point corresponding to the usage frequency and the current time point; obtaining the activity level of the multiple stored data of each user, wherein the activity level is positively correlated with the usage frequency and the duration of the usage frequency, and negatively correlated with the time difference between the end time point corresponding to the usage frequency and the current time point; obtaining the access sensitivity of the multiple stored data of each user, wherein the access sensitivity characterizes the importance of the multiple stored data; using the product of the access sensitivity and the activity level as the risk coefficient of the multiple stored data of each user, and performing hierarchical encryption on the multiple stored data of each user based on the risk coefficient.

[0007] The above technical solution achieves intelligent hierarchical encryption of data by reflecting the risk coefficient of the overall risk level of the data. This not only improves the security protection capability of high-risk data, but also takes into account encryption efficiency, making the encryption strategy more accurate and flexible, and effectively enhancing the pertinence of data protection.

[0008] Furthermore, the first User's stored data activity level for: , For the first The user in the first Data stored for each historical time period Frequency of use Storing data for multiple historical time periods Total usage frequency For the first The user in the first The duration of each usage frequency, Total usage frequency For the first The usage frequency corresponds to the time difference between the end time and the current time. natural constant An exponential function with base 0.

[0009] The above technical solution effectively characterizes the actual activity of users with the data by comprehensively considering the frequency and duration of user use of stored data in multiple time periods, as well as the distance from the current time. It also uses a time decay function to reduce the impact of historical behaviors, making the activity level assessment closer to the current reality and improving the responsiveness to dynamic changes in user behavior.

[0010] Furthermore, the first User's stored data Access sensitivity for: , For the first Individual user access to stored data Number of times, To express summation, The total number of data stored. For storing data The degree of sensitivity, which characterizes the level of privacy of the stored data.

[0011] The above technical solution effectively measures the degree of sensitive behavior of users in the process of using data by combining the frequency of users' access to various types of stored data with the privacy sensitivity of each type of data itself, making the access sensitivity more in line with the actual use scenario.

[0012] Furthermore, the sensitivity level is: , For the first Individual user access to stored data Number of times, To express summation, For the total number of users, To access stored data The The activity level of individual users natural constant An exponential function with base 0.

[0013] The above technical solution combines the frequency of user access to data with their activity level, and uses an exponential function to enhance the impact of high-frequency and high-activity user behavior on sensitivity, thereby more accurately reflecting the privacy importance of data in actual use.

[0014] Furthermore, the hierarchical encryption specifically involves: if the risk coefficient is higher than a preset threshold, then AES-256 is used to encrypt the stored data; if the risk coefficient is lower than or equal to the preset threshold, then AES-128 is used to encrypt the stored data.

[0015] The above technical solution achieves a reasonable allocation of encryption resources and a balance between security protection by employing encryption algorithms of varying strengths based on the data's risk level. High-risk data uses stronger encryption methods, enhancing security capabilities, while low-risk data uses lighter encryption methods, reducing system load, improving overall encryption efficiency and system performance, and enhancing the flexibility and practicality of data protection.

[0016] Furthermore, the preset threshold is obtained using the Otsu's method.

[0017] Furthermore, the stored data includes: user information, transaction records, images, and operation logs.

[0018] Furthermore, the activity level and access sensitivity are standardized.

[0019] Furthermore, the standardization is Z-score standardization.

[0020] In a second aspect, the present invention provides a data security management system for a server, including a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the data security management method for a server described in any one of the above claims is implemented.

[0021] The beneficial effects of this invention are as follows:

[0022] This invention comprehensively analyzes the frequency, duration, and time decay of user usage of various types of stored data across different historical time periods to accurately calculate data activity levels. Combined with data privacy sensitivity, it forms a comprehensive indicator reflecting data risk levels, enabling scientific quantification and dynamic assessment of stored data risk. Furthermore, it employs a tiered encryption strategy, using higher-level encryption algorithms to enhance security for high-risk data and relatively lightweight encryption for low-risk data. This approach ensures data security while improving system efficiency, making the entire data security management process more intelligent, flexible, and efficient. Ultimately, it enhances the security protection and management capabilities of multi-user, multi-type stored data on servers. Attached Figure Description

[0023] Figure 1 This is a schematic flowchart illustrating a data security management method for a server according to an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram illustrating the structural block diagram of a data security management system for a server according to an embodiment of the present invention. Detailed Implementation

[0025] An embodiment of a data security management method for servers.

[0026] like Figure 1 As shown, a flowchart of a data security management method for a server according to an embodiment of the present invention includes the following steps:

[0027] S1: Collect multiple stored data points of each user in the server within multiple historical time periods, the usage frequency of multiple stored data points, the duration of the usage frequency, and the time difference between the end time point corresponding to the usage frequency and the current time point.

[0028] In one embodiment, the multiple stored data include, but are not limited to, user information, transaction records, images, and operation logs. For each piece of stored data, its usage frequency, duration of each use, and the time difference between the corresponding end time and the current time are further collected. These data not only reflect the frequency and duration of data access but also reveal the temporal distribution characteristics of data usage. By introducing the time difference between the end time and the current time, it is possible to effectively capture whether data has been frequently accessed recently, improving the timeliness and accuracy of activity calculation.

[0029] S2: Obtain the activity level of multiple stored data for each user.

[0030] In one embodiment, the first User's stored data activity level for: , For the first The user in the first Data stored for each historical time period Frequency of use Storing data for multiple historical time periods Total usage frequency For the first The user in the first The duration of each usage frequency, Total usage frequency For the first The usage frequency corresponds to the time difference between the end time and the current time. natural constant An exponential function with base 0.

[0031] By comprehensively considering the frequency of user access to specific stored data across multiple historical time periods, as well as the time difference between each access duration and the current time, an evaluation metric is constructed that dynamically reflects the activity level of user data. Compared to traditional methods that assess activity solely based on access frequency or duration, this approach considers not only the quantity of usage behaviors but also the timeliness of those behaviors, resulting in a more comprehensive and accurate model of user behavior activity. In particular, by introducing an exponential function with a time decay factor, the impact of earlier accesses on current activity assessment results is effectively mitigated, highlighting the importance of recent usage behavior and enhancing sensitivity and responsiveness to changes in data access trends. This facilitates refined management and intelligent scheduling of storage resources.

[0032] S3: Obtain the access sensitivity of multiple stored data for each user.

[0033] In one embodiment, the first User's stored data Access sensitivity for: , For the first Individual user access to stored data Number of times, To express summation, The total number of data stored. For storing data The degree of sensitivity;

[0034] By normalizing user access behavior to various types of stored data and combining it with the overall sensitivity of each type of stored data, a sensitivity index is constructed to measure the sensitivity of a single user's access behavior in a multi-data scenario. This index can effectively reflect the degree of correlation and potential risk level between the user and sensitive data in the overall system.

[0035] Compared to traditional methods that rely solely on single dimensions like access frequency or duration to analyze user behavior, the aforementioned approach places greater emphasis on the distribution characteristics of user access behavior within the global data sensitivity structure. It comprehensively considers both the importance of the data itself and the weight of user behavior, thereby giving the evaluation results stronger contextual relevance and behavioral guidance. This weighted fusion approach can accurately identify which users are more likely to frequently access highly sensitive data, providing more detailed and targeted support for functions such as dynamic permission adjustments and risk warning responses. This significantly improves the system's intelligence and responsiveness in areas such as data security protection, abnormal behavior detection, and resource allocation optimization.

[0036] Furthermore, the above-mentioned solution is designed with good scalability and adaptability, and is also applicable in complex environments with multiple users and multiple data sources. It can provide an important technical foundation for subsequent data classification and protection strategies, user access behavior auditing, compliance assessment and intelligent decision-making, and promote the evolution of storage management systems towards a more efficient, secure and intelligent direction.

[0037] The sensitivity level is: , For the first Individual user access to stored data Number of times, To express summation, For the total number of users, To access stored data The The activity level of individual users natural constant An exponential function with base 0.

[0038] By weighting and fusing the access frequency of multiple users to the same stored data with their corresponding activity levels, and combining this with an exponential function, an indicator for measuring the overall sensitivity of stored data can be constructed. This approach can more comprehensively and accurately reflect the importance and access frequency of the data in a multi-user environment. The above scheme not only considers the intensity and timeliness of individual user behavior, but also effectively avoids interference from the overall evaluation results caused by the behavior of a few extreme users by introducing normalization processing of the overall access structure, thus improving the stability and fairness of sensitivity calculation. By using an exponential function to non-linearly amplify the weighted sum, the ability to identify highly sensitive data can be further enhanced, making data frequently accessed by a majority of highly active users show higher importance in the evaluation results. This helps to accurately implement differentiated strategies in areas such as data hierarchical management, access control, encryption protection, and transmission priority ranking.

[0039] Meanwhile, the introduction of the above technical solutions provides basic support for building a more user-behavior-oriented data management system, enabling real-time perception of the dynamic popularity and potential value of data, thereby improving the processing efficiency and protection capabilities of key data, optimizing overall resource allocation, reducing storage costs, and enhancing business continuity and data security. It has significant technical advantages and application prospects for supporting intelligent data management in a multi-user collaborative environment.

[0040] S4: The product of the access sensitivity and the activity level is used as the risk coefficient of multiple stored data of each user, and the multiple stored data of each user are encrypted in a hierarchical manner based on the risk coefficient.

[0041] In one embodiment, the activity level and access sensitivity are standardized, and the standardization is Z-score standardization;

[0042] By applying a standardized scale transformation to the activity levels and access sensitivity of different users, the differences in the units and distributions of the original data can be effectively eliminated, making various indicators comparable within the same evaluation system, thereby improving the scientific rigor and reliability of the overall data analysis. After standardization, the system can more accurately identify user behaviors that are abnormal relative to the overall average level. Whether it's excessively high activity or significantly deviating access sensitivity, these can be more clearly characterized, greatly enhancing the ability to identify abnormal patterns, potential risks, or high-value behaviors.

[0043] The hierarchical encryption specifically involves: if the risk coefficient is higher than a preset threshold, then AES-256 is used to encrypt the stored data; if the risk coefficient is lower than or equal to the preset threshold, then AES-128 is used to encrypt the stored data, and the preset threshold is obtained using the maximum inter-class variance method. The maximum inter-class variance method is an existing technology and will not be described in detail in this solution.

[0044] By introducing a tiered encryption mechanism, different encryption algorithms with varying strengths are used based on the risk level. This ensures data security while maintaining efficient system resource utilization, enhancing the overall flexibility and practicality of the encryption strategy. In particular, the use of the maximum inter-class variance method to analyze the risk coefficients determines the preset thresholds that best distinguish between high-risk and low-risk data. This ensures that the encryption level classification has strong discriminative power and data-driven scientific rigor, thus avoiding the subjective bias and inadequacy that may result from manually setting thresholds.

[0045] The aforementioned technical solutions not only employ higher-level encryption methods for high-risk data, fundamentally strengthening the protection of sensitive data and preventing leakage and unauthorized access, but also utilize less resource-intensive encryption methods for low-risk data. This optimizes the system's computational burden and response speed, improving overall operational efficiency. These solutions enable dynamic perception of data risk levels and adaptive adjustment of encryption strategies, making the encryption process more intelligent and refined. This provides strong technical support for building a storage management system that combines security, performance, and scalability, and promotes the rational allocation and continuous stable operation of information system resources while ensuring data security and compliance.

[0046] The present invention collects and analyzes the frequency, duration and time interval of each user's use of stored data on the server to accurately calculate the activity level of the data, and combines the data access sensitivity to comprehensively assess the risk coefficient of the data, thereby realizing risk-based encrypted management. By using encryption algorithms of different strengths to protect data of different risk levels, the security of high-risk data is guaranteed, while the encryption efficiency and resource utilization of the system are improved, effectively enhancing the overall data protection capability and management intelligence level of the server.

[0047] An embodiment of a data security management system for servers:

[0048] like Figure 2 As shown in the figure, a structural block diagram of a data security management system for a server according to an embodiment of the present invention includes a processor and a memory.

[0049] This invention also provides a data security management system for servers. For example... Figure 2As shown, the system includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement a data security management method for a server according to the present invention.

[0050] The data security management system for servers also includes other components well known to those skilled in the art, such as communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0051] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented by computer-readable / executable instructions stored or otherwise maintained on such a computer-readable medium.

[0052] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise explicitly specified.

[0053] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A data security management method for a server, characterized by, The method comprises the following steps: collecting a plurality of stored data of each user in the server in a plurality of historical time periods, a plurality of usage frequencies of the stored data, a time length of the usage frequencies, and a time difference value between an end time point corresponding to the usage frequencies and a current time point; The activity level of multiple stored data for each user is obtained. This activity level is positively correlated with usage frequency and duration, and negatively correlated with the time difference between the end time and the current time corresponding to each usage frequency. User's stored data activity level for: , For the first The user in the first Data stored for each historical time period Frequency of use Storing data for multiple historical time periods Total usage frequency For the first The user in the first The duration of each usage frequency, Total usage frequency For the first The usage frequency corresponds to the time difference between the end time and the current time. natural constant An exponential function with base 0; Access sensitivities of a plurality of stored data of each user are acquired, the access sensitivities representing importance degrees of the plurality of stored data; a first access sensitivity of stored data of a first user is an exponential function with a base​​​​​​​​​​​​​​​​​​​​ multiplying the access sensitivity and the activity level to obtain a risk coefficient of the plurality of stored data of each user, and performing hierarchical encryption on the plurality of stored data of each user based on the risk coefficient.

2. The data security management method for a server according to claim 1, wherein, The hierarchical encryption specifically comprises: if the risk coefficient is higher than a preset threshold, the stored data is encrypted by using AES-256; and if the risk coefficient is lower than or equal to the preset threshold, the stored data is encrypted by using AES-128.

3. The data security management method for a server according to claim 2, wherein, The preset threshold is obtained by using the maximum inter-class variance method.

4. The data security management method for a server according to claim 1, wherein, The plurality of stored data comprises user information, transaction flow, pictures, and operation records.

5. The data security management method for a server according to claim 1, wherein, The activity level and the access sensitivity are subjected to standardization processing.

6. The data security management method for a server according to claim 5, wherein, The standardization is Z-score standardization.

7. A data security management system for a server, characterized by, The method comprises a memory and a processor, and the memory stores computer program instructions, which, when executed by the processor, realize the method for data security management of the server according to any one of claims 1 to 6.

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