User database information anti-divulging protection system and device based on big data

Abstract

The invention discloses a user database information anti-leakage protection system and device based on big data, and relates to the technical field of data processing. The device comprises a data acquisition module used for acquiring current to-be-transmitted data; the importance analysis module is used for determining the data importance of the current to-be-transmitted data according to the current data type of the current to-be-transmitted data; the necessity analysis module is used for determining the key updating necessity of the current to-be-transmitted data according to the data importance and the historical updating moment of each key; and the key updating module is used for updating the key of the current to-be-transmitted data under the condition that the key updating necessity is greater than a preset necessity threshold. According to the big data-based user database information anti-leakage protection equipment provided by the invention, the information security can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a user database information anti-disclosure protection system and device based on big data. Background Art

[0002] Currently, data encryption transmission is adopted to protect the information security of user databases. During the data encryption transmission process, the security and reliability of data transmission are often ensured by updating keys.

[0003] However, currently, the selection of the time point for key update is often based on a set time period. The set period has the risk of being eavesdropped and cracked, resulting in an increased risk coefficient during data transmission of the user database and poor information security. Summary of the Invention

[0004] Embodiments of the present invention provide a user database information anti-disclosure protection system and device based on big data, which can improve information security.

[0005] In a first aspect of embodiments of the present invention, a user database information anti-disclosure protection device based on big data is provided, including: A data acquisition module, configured to acquire currently to-be-transmitted data; An importance analysis module, configured to determine the data importance of currently to-be-transmitted data according to the current data type of currently to-be-transmitted data; A necessity analysis module, configured to determine the key update necessity of currently to-be-transmitted data according to the data importance and each key historical update time; A key update module, configured to update the key of currently to-be-transmitted data when the key update necessity is greater than a preset necessity threshold.

[0006] In some possible implementation manners, the importance analysis module specifically includes the following units: A first type importance determination unit, configured to determine the target type importance corresponding to the current data type of currently to-be-transmitted data based on a target matching relationship, where the target matching relationship is used to represent the matching relationship between various data types and the corresponding type importance; An element acquisition unit, configured to acquire the first transmission data volume of currently to-be-transmitted data, and the type difference degree between currently to-be-transmitted data and the first historical transmission data within the corresponding historical time window, where the type difference degree is used to represent the difference between the current data type and the first historical data type of the first historical transmission data within the historical time window; A data importance determination unit, configured to determine the data importance of currently to-be-transmitted data by using the target type importance, the first transmission data volume, and the type difference degree.

[0007] In some possible implementations, the importance analysis module further includes the following units: A data acquisition unit, configured to acquire second historical transmission data within a target historical time period; A second type importance determination unit, configured to determine the type importance corresponding to the second historical data type of each batch of second historical transmission data according to the second historical transmission data within the target historical time period; A relationship generation unit, configured to generate a target matching relationship based on the type importance corresponding to each second historical data type.

[0008] In some possible implementations, the second type importance determination unit specifically includes the following subunits: A transmission performance determination subunit, configured to determine the transmission performance of each second historical data type according to the start transmission time and the end transmission time of each batch of second historical transmission data within the target historical time period; A type importance determination subunit, configured to perform, for each second historical data type: determine the type importance of the target historical data type according to the transmission performance of the target historical data type, and the single transmission duration and the second transmission data volume of the target second historical transmission data, where the target historical data type is any one of the second historical data types, and the target second historical transmission data is each batch of second historical transmission data corresponding to the target historical data type.

[0009] In some possible implementations, the transmission performance determination subunit is specifically configured to: Determine the total transmission duration of the target historical data type and the transmission interval duration between adjacent target second historical transmission data according to the start transmission time and the end transmission time of each batch of second historical transmission data within the target historical time period; Determine the transmission performance of the target historical data type by using the total transmission duration of the target historical data type, the transmission interval duration between adjacent target second historical transmission data, and the historical total duration of the target historical time period.

[0010] In some possible implementations, the type importance determination subunit is specifically configured to: Determine the call frequency of the target historical data type according to the single transmission duration and the second transmission data volume of the target second historical transmission data; Determine the type importance of the target historical data type by using the transmission performance and the call frequency of the target historical data type.

[0011] In some possible implementations, the necessity analysis module specifically includes the following units: An interval duration determination unit, configured to determine an average interval duration for key update according to each key history update moment; A necessity determination unit, configured to determine the key update necessity of the currently to-be-transmitted data according to the data importance, and the magnitude relationship between the current interval duration of the currently to-be-transmitted data and the average interval duration, where the current interval duration is the interval duration between the current moment and the moment when the key was last updated.

[0012] In some possible implementation manners, the interval duration determination unit is specifically configured to: For each key update time period, perform respectively: Based on the third data transmission volume within the key update time period, and the type importance of each data type within the key update time period, determine the duration weight of the key update time period, where the key update time period is the time period formed between adjacent key history update moments; Use the time period duration of each key update time period and the corresponding duration weight to determine the average interval duration for key update.

[0013] In some possible implementation manners, the necessity determination unit is specifically configured to: Determine the necessity weight of the currently to-be-transmitted data according to the magnitude relationship between the current interval duration of the currently to-be-transmitted data and the average interval duration; Use the data importance, the necessity weight, and the proportion of abnormal data between the current moment and the moment when the key was last updated to determine the key update necessity of the currently to-be-transmitted data.

[0014] In a second aspect of the embodiments of the present invention, a user database information anti-disclosure protection system based on big data is provided, including: A data encryption subsystem, configured to perform encryption processing on the to-be-transmitted data of the user database; A behavior detection subsystem, configured to detect the health and abnormality of the data transmission behavior of the user database; A permission control subsystem, configured to manage the access permissions of the to-be-transmitted data of the user database; The user database information anti-disclosure protection device provided in any of the above aspects, where the user database information anti-disclosure protection device communicates with the data encryption subsystem, the behavior detection subsystem, and the permission control subsystem respectively.

[0015] The beneficial effects of the present invention are as follows: In the anti-disclosure protection device for user database information based on big data provided by the embodiments of the present invention, the data importance of the currently to-be-transmitted data is analyzed according to the current data type of the currently to-be-transmitted data. Then, according to the transmission importance of the currently to-be-transmitted data and each key historical update time, the key update necessity of the currently to-be-transmitted data is determined. When the key update necessity is greater than the preset necessity threshold, the key of the currently to-be-transmitted data is updated. In this way, the present invention determines the transmission importance of the currently to-be-transmitted data according to the current data type of the currently to-be-transmitted data, and combines the interval duration of key updates in the historical situation to analyze the necessity of key updates at the current moment, so as to determine whether to perform key updates according to the necessity of key updates. In this way, the wiretapping risk and cracking risk of key updates in a set time period can be avoided, and the information security can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 FIG. is a schematic structural diagram of an anti-disclosure protection device for user database information based on big data provided by an embodiment of the present invention; Figure 2 FIG. is a schematic structural diagram of a necessity analysis module provided by an embodiment of the present invention; Figure 3 FIG. is a schematic structural diagram of an anti-disclosure protection system for user database information based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the drawings and preferred embodiments, describe in detail the specific implementation manners, structures, features and effects of an anti-disclosure protection system and device for user database information based on big data proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0019] 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.

[0020] It should be noted that in the technical solution of the present invention, the acquisition, storage, use, processing, etc. of data all comply with the relevant regulations of laws and regulations.

[0021] Current mainstream solutions focus on building a data protection barrier through encrypted transmission. As a key link in this system, the key management update mechanism directly affects the protection efficiency. Although the traditional periodic key update strategy has the convenience of implementation, the update mode with a fixed time window objectively forms a predictable security rhythm. This deterministic mode provides potential breakthrough points for attackers: through long-term monitoring and pattern analysis, attackers are expected to carry out precise attacks on the eve of key expiration, resulting in periodic vulnerable windows in the protection system and significantly increasing the risk of data leakage.

[0022] The purpose of the present invention is to provide a big data-based user database information anti-disclosure protection system and device. In the big data-based user database information anti-disclosure protection device provided by the embodiments of the present invention, according to the current data type of the currently to-be-transmitted data, the data importance of the currently to-be-transmitted data is analyzed. Then, according to the transmission importance of the currently to-be-transmitted data and each key historical update time, the key update necessity of the currently to-be-transmitted data is determined. When the key update necessity is greater than the preset necessity threshold, the key of the currently to-be-transmitted data is updated. In this way, the present invention determines the transmission importance of the currently to-be-transmitted data according to the current data type of the currently to-be-transmitted data, and combines the historical interval duration of key updates to analyze the necessity of key updates at the current moment, so as to determine whether to update the key according to the necessity of key updates. In this way, the eavesdropping risk and cracking risk of key updates under a set time period can be avoided, and the information security can be improved.

[0023] The following introduces specific embodiments of a big data-based user database information anti-disclosure protection system and device provided by the embodiments of the present invention.

[0024] As Figure 1 shown, a structural schematic diagram of a big data-based user database information anti-disclosure protection device is provided. The big data-based user database information anti-disclosure protection device 100 includes a data acquisition module 110, an importance analysis module 120, a necessity analysis module 130, and a key update module 140.

[0025] The data acquisition module 110 is used to acquire the currently to-be-transmitted data.

[0026] In this embodiment, the currently to-be-transmitted data can come from various sources, such as environmental data collected by sensors (such as temperature, humidity, pressure, etc.), business data generated by business systems (such as transaction records, customer information, etc.), file data uploaded by users, etc.

[0027] The acquisition method of the current data to be transmitted can be any one of real-time acquisition, regular collection, and event-triggered collection. Real-time acquisition: For data with high real-time requirements, such as the device operation status data in industrial production, the data acquisition module 110 acquires it in real time through sensors; Regular collection: For some periodically generated data, such as daily sales data, the data acquisition module 110 can set up a scheduled task to collect data at specific time points; Event-triggered collection: When a specific event occurs, it triggers the data acquisition module 110 to collect data. For example, after a user completes a transaction, the system automatically collects the relevant data of this transaction.

[0028] The data to be transmitted acquired by the data acquisition module 110 is usually first stored in a temporary buffer or a data queue, waiting for subsequent processing. This buffer can be a data structure in memory or a temporary table in a database.

[0029] The importance analysis module 120 is used to determine the data importance of the current data to be transmitted according to the current data type of the current data to be transmitted.

[0030] In this embodiment, the current data type is used to represent the type to which the current data to be transmitted belongs. Exemplarily, the data type can include sensitive data type, general business data type, and non-critical data type. Among them, the sensitive data type can include personal identity information (name, ID number, bank card number, etc.), medical data, enterprise core confidential data (business plan, R & D results, etc.). Once these data are leaked, they may cause serious losses to individuals or enterprises; The general business data type can include ordinary business reports, daily operation records, etc. These data have certain reference value for business, but the leakage risk is relatively low; The non-critical data type can include some temporary data for testing, or log data with little impact on business.

[0031] The data importance is used to represent the importance degree of the data. Exemplarily, the importance analysis module 120 can preset the corresponding importance levels according to various data types. For example, it is stipulated that the data importance of the sensitive data type is 3 (the highest level), the data importance of the general business data type is 2, and the data importance of the non-critical data type is 1.

[0032] Thus, after the data acquisition module 110 acquires the current data to be transmitted, the importance analysis module 120 can directly determine the data importance of the current data to be transmitted according to the current data type of the current data to be transmitted and the preset matching relationship.

[0033] The necessity analysis module 130 is used to determine the key update necessity of the current data to be transmitted according to the data importance and each key historical update time.

[0034] In this embodiment, the key history update moment is used to represent the moment when the key is updated during the historical process, and the key update necessity is used to represent the degree of necessity for updating the key corresponding to the currently to-be-transmitted data.

[0035] As an example, the necessity analysis module 130 calculates the average interval duration for key update according to each key history update moment. Then, the interval duration between the current moment and the moment when the key was last updated is divided by the average interval duration to obtain a target ratio. And according to this target ratio and the data importance, the quantization value of the key update necessity is calculated through weighted summation.

[0036] The key update module 140 is used to update the key of the currently to-be-transmitted data when the key update necessity is greater than the preset necessity threshold.

[0037] In this embodiment, the key update module 140 presets the necessity threshold for key update based on factors such as security requirements and service characteristics. For example, the threshold is set to 0.7, and when the calculated key update necessity is greater than 0.7, the key update operation is triggered.

[0038] Specifically, first, a secure key generation algorithm is used, such as generating a new key based on a random number generator, a cryptographic hash function, etc. The new key should have sufficient randomness and complexity to ensure its security. Then, the new key is securely distributed to the data sender and the data receiver. The distribution process can use technologies such as encrypted channels and digital certificate authentication to ensure the confidentiality and integrity of the key during transmission. After the data sender and the data receiver successfully obtain the new key, the new key is used to replace the old key. For the currently to-be-transmitted data, a key smooth transition mechanism can be adopted to ensure the continuity of data transmission. For example, both the old key and the new key are supported for decryption within a certain period of time, and gradually transition to using only the new key. Finally, after confirming that the new key has been used normally and the old key is no longer needed, the old key is securely destroyed to prevent the old key from being maliciously exploited. The destruction method can be physically destroying the storage medium (such as destroying the hardware security module containing the old key), or at the software level, ensuring that the old key cannot be recovered through operations such as overwriting and clearing.

[0039] As an alternative embodiment, the importance analysis module 120 specifically includes the following units: The first type importance determination unit is used to determine the target type importance corresponding to the current data type of the currently to-be-transmitted data based on the target matching relationship, and the target matching relationship is used to represent the matching relationship between various data types and the corresponding type importance; An element acquisition unit is configured to acquire a first transmission data volume of current data to be transmitted, and a type difference degree between the current data to be transmitted and first historical transmission data within a corresponding historical time window, where the type difference degree is used to characterize a difference between the current data type and a first historical data type of the first historical transmission data within the historical time window; A data importance determination unit is configured to determine the data importance of the current data to be transmitted by using a target type importance, the first transmission data volume, and the type difference degree.

[0040] In this embodiment, the target matching relationship describes a matching relationship between various data types and corresponding type importances. Exemplarily, the data type and the corresponding type importance can be stored using a hash table structure, where the key is the data type and the value is the corresponding type importance.

[0041] The first transmission data volume is used to characterize the total amount of data that needs to be transmitted in the user database this time, that is, the total amount of data included in the current data to be transmitted.

[0042] The first historical transmission data is used to characterize the historical transmission data within the historical time window, and the first historical data type is used to characterize the data type corresponding to the first historical transmission data.

[0043] The type difference degree is a difference between the current data type of the current data to be transmitted and the first historical data type of the first historical transmission data within the corresponding historical time window. Exemplarily, the size of the historical time window can be 5 batches of data to be transmitted before the current data to be transmitted.

[0044] As an example, the first type importance determination unit uses the constructed target matching relationship, takes the current data type as the key, and looks up the corresponding value in the hash table to obtain the target type importance.

[0045] Then, the element acquisition unit uses a database query statement to count the first transmission data volume of the current data to be transmitted in the user database; at the same time, it obtains 5 batches of data to be transmitted before the current data to be transmitted (i.e., the first historical transmission data) from the user database, then determines the number of overlapping types that are the same as the current data type among the data types of these 5 batches of data to be transmitted (i.e., the first historical data type), and finally divides 5 by the number of overlapping types to obtain the type difference degree between the current data to be transmitted and the first historical transmission data within the historical time window.

[0046] Finally, the data importance determination unit uses the target type importance, the first transmission data volume, and the type difference degree to determine the data importance of the current data to be transmitted through the following formula 1: Formula 1 In Formula 1, is used to characterize the data importance of the j-th batch of data to be transmitted, is used to characterize the type importance of the j-th batch of data to be transmitted, is used to characterize the first transmission data volume of the j-th batch of data to be transmitted, is used to characterize the type difference degree between the j-th batch of data to be transmitted and the first historical transmission data within the corresponding historical time window.

[0047] Among them, Formula 1 mainly uses the type importance of the current data to be transmitted in the user database, combines the first transmission data volume of the current data to be transmitted and whether this type of data has been transmitted at the previous moment of the current data to be transmitted, and comprehensively determines the data importance of the current data to be transmitted. The greater the type importance of the current data to be transmitted, or the greater the first transmission data volume of the current data to be transmitted, or the less this type of data has been transmitted at the previous moment of the current data to be transmitted, the greater the data importance of the current data to be transmitted.

[0048] Through this embodiment, by combining the first transmission data volume of the current data to be transmitted, the type difference degree between the current data to be transmitted and the first historical transmission data within the corresponding historical time window, and the target type importance corresponding to the current data type, the data importance of the current data to be transmitted is comprehensively determined, which can improve the accuracy of the data importance of the current data to be transmitted, thereby improving information security.

[0049] As an optional embodiment, the importance analysis module 120 further includes the following units: The data acquisition unit is used to acquire the second historical transmission data within the target historical time period; The second type importance determination unit is used to determine the type importance corresponding to the second historical data type of each batch of second historical transmission data according to the second historical transmission data within the target historical time period; The relationship generation unit is used to generate a target matching relationship based on the type importance corresponding to each second historical data type.

[0050] In this embodiment, the target historical time period can be determined according to service requirements, data feature analysis, relevant rules, etc. For example, if it is necessary to analyze the reference significance of the recent data transmission mode for the current data processing, the target historical time period can be set as the past month; if long-term trends are concerned, it can be set as the past year.

[0051] The second historical transmission data is used to characterize the historical transmission data within the target historical time period, and the second historical data type is used to characterize the data type corresponding to the second historical transmission data.

[0052] As an example, according to the target historical time period, the data acquisition unit uses the corresponding query statement or data extraction tool to obtain the second historical transmission data from the user database.

[0053] Then, the second type importance determination unit analyzes each batch of the second historical transmission data to identify its data type. Specifically, this may involve data parsing. For example, for structured data (such as tabular data in a database), the data type can be determined by examining the field definitions and data content; for unstructured data (such as text, images, audio), specific algorithms or tools may be required for feature extraction and classification, such as using natural language processing techniques to identify the topic type of text data and using image recognition techniques to determine the category of image data.

[0054] Then, the second type importance determination unit assigns type importance to different data types according to business requirements and importance evaluation criteria. For example, in the medical data transmission scenario, the type importance of the patient's medical record information data type may be set to 9 (out of 10), while the type importance of general health promotion materials data type may be set to 3.

[0055] Finally, the relationship generation unit selects a suitable data structure to store the target matching relationship, and thus stores each second historical data type and its corresponding type importance into the selected data structure. Specifically, common data structures can include hash tables, dictionaries, or tables in relational databases.

[0056] Through this embodiment, the second historical transmission data within the target historical time period is obtained, the type importance of each data type is determined, and a target matching relationship is generated to represent the matching relationship between the data type and the type importance. In this way, directly according to the target matching relationship, the target type importance corresponding to the current data type of the currently to-be-transmitted data can be determined, which can improve the determination efficiency of the target type importance.

[0057] As an alternative embodiment, the second type importance determination unit specifically includes the following sub-units: The transmission performance determination sub-unit is used to determine the transmission performance of each second historical data type according to the start transmission time and the end transmission time of each batch of the second historical transmission data within the target historical time period; The type importance determination sub-unit is used to perform, for each second historical data type, respectively: according to the transmission performance of the target historical data type, and the single data transmission duration and the second transmission data volume of the target second historical transmission data, determine the type importance of the target historical data type, where the target historical data type is any one of the second historical data types, and the target second historical transmission data is each batch of the second historical transmission data corresponding to the target historical data type.

[0058] In this embodiment, the transmission performance degree is used to characterize the overall transmission performance of the second historical data type within the target historical time period. Exemplarily, the transmission performance degree can comprehensively consider factors such as transmission stability and timeliness. For example, the transmission performance degree can be defined as the ratio of the standard deviation of the transmission duration to the average transmission duration to measure the transmission stability, and the timeliness can be evaluated in combination with whether the transmission is completed within the specified time.

[0059] The single - data - transmission duration is used to characterize the duration required to transmit a batch of target second - historical transmission data, and the second - transmission data volume is used to characterize the total amount of data included in the target second - historical transmission data.

[0060] As an example, the transmission - performance - degree determination subunit first obtains the start - transmission moment and end - transmission moment of each batch of second - historical transmission data within the target historical time period, and calculates the transmission duration for each batch of second - historical transmission data.

[0061] Then, calculate the average transmission duration and the standard deviation of the transmission duration of all second - historical transmission data of the same data type within the target historical time period. According to the defined index, use the average transmission duration and the standard deviation of the transmission duration of the second - historical transmission data of the same data type to calculate the transmission performance degree of the corresponding second - historical data type.

[0062] Then, for the target historical data type, the type - importance - determination subunit obtains the single - data - transmission duration of each batch of corresponding target second - historical transmission data, and calculates the average value of the single - data - transmission duration; and obtains the second - transmission data volume of each batch of corresponding target second - historical transmission data, and calculates the average value of the second - transmission data volume.

[0063] Finally, based on the transmission performance degree, the average value of the single - data - transmission duration, and the average value of the second - transmission data volume of the target historical data type, calculate the type importance of the target historical data type through weighted summation.

[0064] Through this embodiment, according to the transmission performance degree of the target historical data type, as well as the single - data - transmission duration and the second - transmission data volume of the target second - historical transmission data, accurately determine the type importance of the target historical data type. In this way, the type importance of each data type can be accurately calculated, thereby further improving information security.

[0065] As an optional embodiment, the transmission - performance - degree determination subunit is specifically used for: According to the start - transmission moment and end - transmission moment of each batch of second - historical transmission data within the target historical time period, determine the total transmission duration of the target historical data type and the transmission - interval duration between adjacent target second - historical transmission data; Determine the transmission performance degree of the target historical data type by using the total transmission duration of the data of the target historical data type, the transmission interval duration between adjacent target second historical transmission data, and the total historical duration of the target historical time period.

[0066] In this embodiment, the total transmission duration is used to represent the sum of the transmission durations of each batch of target second historical transmission data of the target historical data type, and the total historical duration is used to represent the time length of the target historical time period.

[0067] The transmission interval duration is used to represent the duration of the interval between adjacent target second historical transmission data. For example, the start transmission time of the (a + 1)-th batch of target second historical transmission data minus the end transmission time of the a-th batch of target second historical transmission data is the transmission interval duration between the a-th batch of target second historical transmission data and the (a + 1)-th batch of target second historical transmission data.

[0068] As an example, specifically, the transmission performance degree of the target historical data type can be determined by the following formula 2: Formula 2 In formula 2, is used to represent the transmission performance degree corresponding to the data type of the j-th batch of data to be transmitted, is used to represent the total transmission duration of the data type corresponding to the j-th batch of data to be transmitted, is used to represent the total historical duration of the target historical time period, is used to represent the average value of the transmission interval durations between adjacent target second historical transmission data corresponding to the data type of the j-th batch of data to be transmitted.

[0069] Among them, is used to represent the duration proportion of the total transmission duration of the data type corresponding to the j-th batch of data to be transmitted in the total historical duration. The larger this duration proportion is, or the shorter the time interval for each data transmission of the same data type is, it indicates that this type of data is frequently called in the user database, that is, the corresponding transmission performance degree is larger.

[0070] Through this embodiment, by using the total transmission duration of the target historical data type, the transmission interval duration between adjacent target second historical transmission data, and the total historical duration of the target historical time period, the transmission performance degree of the target historical data type can be accurately determined, thereby improving the calculation accuracy of the type importance.

[0071] As an optional embodiment, the type importance determination subunit is specifically configured to: Determine the call frequency of the target historical data type according to the single transmission duration and the second transmission data volume of the target second historical transmission data; Determine the type importance of the target historical data type by using the transmission performance and call frequency of the target historical data type.

[0072] In this embodiment, the type importance of the target historical data type can be specifically determined by the following formula 3: Formula 3 In formula 3, represents the type importance of the data type corresponding to the j-th batch of data to be transmitted, represents the transmission performance of the data type corresponding to the j-th batch of data to be transmitted. represents the number of batches of the target second historical transmission data corresponding to the data type of the j-th batch of data to be transmitted, represents the single data transmission duration of the i-th batch of target second historical transmission data corresponding to the data type of the j-th batch of data to be transmitted, represents the second transmission data volume of the i-th batch of target second historical transmission data corresponding to the data type of the j-th batch of data to be transmitted.

[0073] Wherein, represents the call frequency of the data type corresponding to the j-th batch of data to be transmitted. That is, when the data of a certain data type shows multiple transmissions during the transmission process, and the time for each data transmission is long and the amount of transmitted data is large, it indicates that the data of this type is called frequently. The greater the call frequency or the greater the transmission performance, the greater the type importance.

[0074] Through this embodiment, by using the transmission performance of the target historical data type and the call frequency of the target historical data type, the type importance of the target historical data type can be accurately determined, thereby improving the calculation accuracy of the type importance and further improving the information security.

[0075] As an optional embodiment, as Figure 2 shown, the necessity analysis module 130 specifically includes the following units: The interval duration determination unit 131 is used to determine the average interval duration for the key to be updated according to each key historical update moment; The necessity determination unit 132 is used to determine the key update necessity of the current data to be transmitted according to the size relationship between the data importance, the current interval duration of the current data to be transmitted and the average interval duration, where the current interval duration is the interval duration between the current moment and the moment when the key was last updated.

[0076] In this embodiment, the average interval duration is used to represent the average interval duration between adjacent update moments during the historical update of the key; the current interval duration is used to represent the interval duration between the current moment and the moment when the key was last updated.

[0077] As an example, the interval duration determination unit 131 obtains the historical update time information of the key from the log record, organizes the collected historical update time information, and arranges it in chronological order for subsequent calculation.

[0078] Then, traverse the organized historical update time information, calculate the time interval between every two adjacent updates. Then add up all the time intervals between adjacent updates, and divide by the number of intervals to obtain the average interval duration for key updates.

[0079] Then, the necessity determination unit 132 obtains the current interval duration of the currently to-be-transmitted data, and compares the size relationship between the current interval duration and the average interval duration. According to the size relationship between the current interval duration and the average interval duration, determine the calculation rule for the key update necessity.

[0080] Finally, according to the calculation rule for the key update necessity, use the data importance of the currently to-be-transmitted data to determine the key update necessity of the currently to-be-transmitted data.

[0081] Through this embodiment, by using the data importance of the currently to-be-transmitted data and combining the size relationship between the current interval duration of the currently to-be-transmitted data and the average interval duration, accurately calculate the key update necessity of the currently to-be-transmitted data. In this way, the calculation accuracy of the key update necessity of the currently to-be-transmitted data can be improved, which helps to accurately determine whether to update the key according to the key update necessity subsequently, thereby improving information security.

[0082] As an alternative embodiment, the interval duration determination unit 131 is specifically configured to: For each key update time period, respectively execute: Based on the third transmission data volume within the key update time period and the type importance of each data type within the key update time period, determine the duration weight of the key update time period, where the key update time period is the time period formed between adjacent key historical update times; Use the time period duration of each key update time period and the corresponding duration weight to determine the average interval duration for key updates.

[0083] In this embodiment, the third transmission data volume is used to represent the total amount of data transmitted within the key update time period, that is, the total amount of data included within the key update time period.

[0084] The key update time period is a time period formed between adjacent key historical update times, and the time period duration of the key update time period is the subsequent key historical update time corresponding to the key update time period minus the previous key historical update time.

[0085] As an example, the duration weight of the key update time period can be specifically determined by the following formula 4: Formula 4 In formula 4, is used to represent the duration weight of the c-th key update time period, is used to represent the third transmission data volume within the c-th key update time period, is used to represent the type importance of the data type corresponding to the j-th batch of data to be transmitted, is used to represent the number of data types within the c-th key update time period.

[0086] Among them, the more the transmission data volume within the key update time period, or the greater the average type importance within the key update time period, the greater the duration weight of the key update time period. That is, for the duration weight of the key update time period, when the data volume transmitted within the key update time period is larger and the type importance within the key update time period is greater, a larger weight value is given to this key update time period.

[0087] Furthermore, the average interval duration for key update can be specifically determined by the following formula 5: Formula 5 In formula 5, is used to represent the average interval duration for key update, is used to represent the time duration of the c-th key update time period, is used to represent the duration weight of the c-th key update time period, is used to represent the number of key update time periods, is used to represent the accumulation of the duration weights of each key update time period, That is, it is used to represent the proportion of the duration weight of the c-th key update time period in the cumulative value of the duration weights of each key update time period. Through this embodiment, the average interval duration for key update is determined by using the time duration of each key update time period and the corresponding duration weight. In this way, based on the scale of data transmission and the importance degree of data types in each key update time period as weights, the average interval duration for key update can be determined more accurately, thereby improving the accuracy of the subsequent key update necessity.

[0088] As an alternative embodiment, the necessity determination unit 132 is specifically configured to: Determine the necessity weight of the current data to be transmitted according to the magnitude relationship between the current interval duration of the current data to be transmitted and the average interval duration; Determine the key update necessity of the currently to-be-transmitted data by using the data importance, necessity weight, and the proportion of abnormal data between the current moment and the moment when the previous key was updated.

[0089] In this embodiment, the proportion of abnormal data is used to represent the proportion of abnormal data in the total transmitted data during the time period formed by the current moment and the moment when the previous key was updated. Among them, abnormal data refers to data with abnormal phenomena such as data theft.

[0090] As an example, the necessity weight of the currently to-be-transmitted data can be specifically determined by the following formula 6: Formula 6 In formula 6, represents the necessity weight of the currently to-be-transmitted data, represents the current interval duration of the currently to-be-transmitted data, represents the average interval duration for key update. s represents a preset constant parameter to avoid the denominator being zero.

[0091] Among them, when the current interval duration is greater than the average interval duration, the necessity weight of the currently to-be-transmitted data is set to the difference between the current interval duration and the average interval duration; when the current interval duration is less than or equal to the average interval duration, the necessity weight of the currently to-be-transmitted data is set to the reciprocal of the sum of the absolute value of the difference between the current interval duration and the average interval duration and the preset constant parameter.

[0092] The key update necessity of the currently to-be-transmitted data can be specifically determined by the following formula 7: Formula 7 In formula 7, represents the key update necessity of the currently to-be-transmitted data, represents the data importance of the currently to-be-transmitted data, represents the necessity weight of the currently to-be-transmitted data, and norm represents the normalization operation. represents the amount of abnormal data between the current moment and the moment when the previous key was updated, represents the total amount of transmitted data between the current moment and the moment when the previous key was updated, represents the proportion of abnormal data between the current moment and the moment when the previous key was updated. It should be noted that to ensure the meaningfulness of the calculation result, in the present invention embodiment, when performing fractional operations and encountering the case where the denominator is 0, a tuning factor greater than 0 needs to be added to the denominator to prevent the denominator from being 0. The value of the tuning factor is set by the implementer according to the actual situation, and this application does not make special restrictions.

[0093] Through this embodiment, by using the data importance, the necessity weight, and the proportion of abnormal data between the current moment and the moment when the previous key was updated, the necessity degree of key update for the currently to-be-transmitted data can be accurately determined, so as to determine whether to perform key update according to the necessity degree of key update. In this way, the eavesdropping risk and cracking risk of key update under the set time period can be avoided, and the information security can be improved.

[0094] According to the user database information anti-disclosure protection device based on big data provided by the present invention. Correspondingly, the present invention also provides a specific embodiment of the user database information anti-disclosure protection system based on big data.

[0095] As Figure 3 shown, there is provided a structural schematic diagram of a user database information anti-disclosure protection system based on big data. The user database information anti-disclosure protection system based on big data includes: A data encryption subsystem 310, configured to encrypt the to-be-transmitted data of the user database; A behavior detection subsystem 320, configured to detect the health and abnormality of the data transmission behavior of the user database; A permission control subsystem 330, configured to manage the access permissions of the to-be-transmitted data of the user database; The user database information anti-disclosure protection device 100 provided in any of the above aspects, and the user database information anti-disclosure protection device 100 communicates with the data encryption subsystem 310, the behavior detection subsystem 320, and the permission control subsystem 330 respectively.

[0096] In this embodiment, the data encryption subsystem 310 uses an advanced encryption algorithm to encrypt the sensitive data in the user database. The selection of the encryption algorithm should be based on the importance of the data, the transmission requirements, and the availability of computing resources.

[0097] The behavior detection subsystem 320 uses big data analysis and machine learning technologies to monitor the data transmission behavior of the user database, including the time, frequency, etc. of data transmission.

[0098] The permission control subsystem 330 assigns specific roles to different users or user groups, and each role has different data access permissions, so as to achieve fine-grained control of data access, such as read-only, write, delete, etc. permissions, to ensure that only authorized users can perform specific operations. At the same time, according to the changes in business requirements, user behavior analysis results, or security policies, the user permissions are dynamically adjusted.

[0099] The user database information anti-disclosure protection device 100 based on big data serves as the central control node and communicates with the data encryption subsystem 310, the behavior detection subsystem 320, and the permission control subsystem 330 through a secure and efficient communication protocol to execute the user database information anti-disclosure protection process in the user database information anti-disclosure protection device 100 provided in any of the above aspects.

[0100] In the user database information anti-disclosure protection system provided by the embodiments of the present invention, according to the current data type of the currently to-be-transmitted data, the data importance of the currently to-be-transmitted data is analyzed. Then, according to the transmission importance of the currently to-be-transmitted data and each key historical update time, the key update necessity of the currently to-be-transmitted data is determined. When the key update necessity is greater than the preset necessity threshold, the key of the currently to-be-transmitted data is updated. Thus, the present invention determines the transmission importance of the currently to-be-transmitted data according to the current data type of the currently to-be-transmitted data, and combines the interval duration of key updates in the historical situation to analyze the necessity of key updates at the current moment, so as to determine whether to update the key according to the necessity of key updates. In this way, the eavesdropping risk and cracking risk of key updates under the set time period can be avoided, and the information security can be improved.

[0101] It should be clear that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, the detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps after understanding the spirit of the present invention.

Claims

1. A user database information anti-disclosure protection device based on big data, characterized in that, The device includes: A data acquisition module for acquiring the current data to be transmitted; An importance analysis module for determining the data importance of the current data to be transmitted according to the current data type of the current data to be transmitted; A necessity analysis module for determining the key update necessity of the current data to be transmitted according to the data importance and each key historical update moment; A key update module for updating the key of the current data to be transmitted when the key update necessity is greater than a preset necessity threshold.

2. The user database information anti-disclosure protection device based on big data according to claim 1, characterized in that, The importance analysis module specifically includes the following units: A first type importance determination unit for determining the target type importance corresponding to the current data type of the current data to be transmitted based on a target matching relationship, where the target matching relationship is used to represent the matching relationship between various data types and the corresponding type importance; An element acquisition unit for acquiring the first transmission data volume of the current data to be transmitted and the type difference degree between the current data to be transmitted and the first historical transmission data within the corresponding historical time window, where the type difference degree is used to represent the difference between the current data type and the first historical data type of the first historical transmission data within the historical time window; A data importance determination unit for determining the data importance of the current data to be transmitted by using the target type importance, the first transmission data volume, and the type difference degree.

3. The user database information anti-disclosure protection device based on big data according to claim 2, characterized in that, The importance analysis module further includes the following units: A data acquisition unit for acquiring the second historical transmission data within a target historical time period; A second type importance determination unit for determining the type importance corresponding to the second historical data type of each batch of the second historical transmission data according to the second historical transmission data within the target historical time period; A relationship generation unit for generating the target matching relationship based on the type importance corresponding to each of the second historical data types.

4. The user database information anti-disclosure protection device based on big data according to claim 3, characterized in that, The second type importance determination unit specifically includes the following sub-units: A transmission performance determination sub-unit for determining the transmission performance of each of the second historical data types according to the start transmission moment and the end transmission moment of each batch of the second historical transmission data within the target historical time period; A type importance determination sub-unit for, for each of the second historical data types, respectively performing: determining the type importance of the target historical data type according to the transmission performance of the target historical data type, and the data single transmission duration and the second transmission data volume of the target second historical transmission data, where the target historical data type is any one of the second historical data types, and the target second historical transmission data is each batch of the second historical transmission data corresponding to the target historical data type.

5. The information anti-disclosure protection device for user databases based on big data according to claim 4, characterized in that, The transmission performance determination sub-unit specifically is used for: Determining the total data transmission duration of the target historical data type and the transmission interval duration between each adjacent target second historical transmission data according to the start transmission moment and the end transmission moment of each batch of the second historical transmission data within the target historical time period; Determine the transmission performance of the target historical data type by using the total transmission duration of the data of the target historical data type, the transmission interval duration between each adjacent target second historical transmission data, and the total historical duration of the target historical time period.

6. The device for protecting the information of the user database against leakage based on big data according to claim 4, characterized in that, The type importance determination subunit is specifically configured to: Determine the invocation frequency of the target historical data type according to the single - data transmission duration and the second transmission data volume of the target second historical transmission data; Determine the type importance of the target historical data type by using the transmission performance and the invocation frequency of the target historical data type.

7. The user database information anti-disclosure protection device based on big data according to claim 1, characterized in that, The necessity analysis module specifically includes the following units: The interval duration determination unit is used to determine the average interval duration for the key to be updated according to each key historical update time; The necessity determination unit is used to determine the key update necessity of the current data to be transmitted according to the size relationship between the data importance, the current interval duration of the current data to be transmitted and the average interval duration, where the current interval duration is the interval duration between the current time and the time when the key was last updated.

8. The user database information anti-disclosure protection device based on big data according to claim 7, characterized in that, The interval duration determination unit is specifically configured to: For each key update time period, respectively execute: Based on the third transmission data volume within the key update time period and the type importance of each data type within the key update time period, determine the duration weight of the key update time period, where the key update time period is the time period formed between adjacent key historical update times; Determine the average interval duration for the key to be updated by using the time period duration of each key update time period and the corresponding duration weight.

9. The user database information anti-disclosure protection device based on big data according to claim 7, characterized in that The necessity determination unit is specifically configured to: Determine the necessity weight of the current data to be transmitted according to the size relationship between the current interval duration of the current data to be transmitted and the average interval duration; Determine the key update necessity of the current data to be transmitted by using the data importance, the necessity weight, and the proportion of abnormal data between the current time and the time when the key was last updated.

10. A user database information anti-disclosure protection system based on big data, characterized in that, The system includes: A data encryption subsystem for encrypting the data to be transmitted in the user database; A behavior detection subsystem for detecting the health and abnormality of the data transmission behavior in the user database; A permission control subsystem for managing the access permissions of the data to be transmitted in the user database; The big - data - based user database information anti - leakage protection device according to any one of claims 1 - 9, where the big - data - based user database information anti - leakage protection device communicates with the data encryption subsystem, the behavior detection subsystem, and the permission control subsystem respectively.

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