Offline file security management method and system based on quantum key and national secret algorithm

Abstract

The present invention discloses an offline file security management and control method and system based on quantum keys and national secret algorithms, relating to the technical field of file security management and control, including setting a number of simulated quantum key update frequencies, dividing the time before the quantum key update of the simulation process in the simulation software into equal parts, and obtaining security data of the equally divided intervals and key update performance impact data; constructing a key update frequency effect evaluation model based on a BP neural network according to the security data of the equally divided intervals and the key update performance impact data; drawing a frequency-effect curve diagram according to the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency; performing data analysis on the frequency-effect curve diagram to obtain a screened quantum key update frequency, and applying it as the final quantum key update frequency to offline file security management; the present application solves the problem of lack of balance in the selection of quantum key update frequency in the prior art, which is usually set through experience.

Description

Technical Field

[0001] The present invention relates to the technical field of offline file security management and control. More specifically, the present invention relates to an offline file security management and control method and system based on quantum key and national secret algorithm. Background Art

[0002] In the field of file security management and control, existing technologies mainly rely on symmetric encryption and asymmetric encryption methods to protect the confidentiality of data transmission; these traditional encryption technologies are often insufficient when facing man-in-the-middle attacks and data tampering; in addition, authentication mechanisms usually rely on static passwords, which cannot effectively prevent unauthorized access; access control mainly adopts role-based strategies and lacks detailed user and device binding management; in terms of data integrity protection, despite the use of hash algorithms, it is still vulnerable to attacks and lacks strong protection measures; at the same time, the key management process is complex and prone to leakage, and the audit and monitoring mechanisms also lack real-time and comprehensiveness; these shortcomings show the urgent need to improve file security and management efficiency.

[0003] For example, the invention patent announcement with announcement number CN104715168B discloses a method and system for file security management and traceability based on digital fingerprints. The method includes a file upload process: when a user uploads a file to a file server, the file server starts a natural language analysis program, performs full-text scanning and matching analysis on the file text, finds the location where the digital fingerprint can be inserted, generates a fingerprint feature position table, and stores it together with the original file in the file server. At the same time, an upload and download record table is generated to record the information uploaded this time; a file download process: when a user initiates a file download request to the file server, the file server generates a binary random code of the same length based on the length of the fingerprint feature position table. When the corresponding bit of the random code is 1, the digital fingerprint is inserted into the text at the corresponding position, a new file is generated and sent to the user, and the information downloaded this time is recorded in the upload and download record table. The present invention can achieve traceability and security management of text files.

[0004] For example, the invention patent with publication number CN116303293A discloses a file circulation control method based on secure documents. In order to achieve permission control of secure documents during the circulation process, it is first necessary to encrypt the document content and perform circulation control in terms of permission levels. The document authorization types are divided into organization, role, and user, and the permissions of the three are divided into user>organization>role. The document confidentiality levels are divided into: top secret, confidential, secret, and ordinary. In order to prevent the occurrence of high-density and low-flow situations, only users with a confidentiality level greater than or equal to this level can open the document for operation. The present invention can effectively solve the problems of secure document circulation operations while still retaining the convenience of document forwarding and use, and has the advantages of high practicality.

[0005] The above-mentioned disclosed technical solution has at least the following technical problems: Although quantum keys provide theoretical security, they may still face new security threats such as side-channel attacks and quantum computing attacks. In order to improve security performance, the file management system needs to update the quantum keys. However, if the frequency of quantum key updates is too high, it may lead to management burden and resource consumption, while if it is too low, it may increase the risk of key attacks. The existing technology usually lacks balance in the selection of quantum key update frequency through empirical settings.

[0006] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an offline file security management method and system based on quantum keys and national secret algorithms. By analyzing the optimal quantum key update frequency, the problem of lack of balance in the selection of quantum key update frequency in the prior art is solved.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] An offline file security management and control method and system based on quantum keys and national secret algorithms include the following steps: obtaining historical data on quantum key distribution and reception during offline file security management, and simulating the process of quantum key distribution and reception during offline file security management based on the historical data and simulation software; setting several simulated quantum key update frequencies, dividing the time before the quantum key update of the simulation process in the simulation software into equal parts, and obtaining security data of the equally divided intervals and key update performance impact data; constructing a key update frequency effect evaluation model based on a BP neural network according to the security data of the equally divided intervals and the key update performance impact data; drawing a frequency-effect curve diagram according to the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency; performing data analysis on the frequency-effect curve diagram to obtain a screened quantum key update frequency, and applying it as the final quantum key update frequency to offline file security management.

[0010] In a preferred embodiment, the historical data of quantum key distribution and reception during offline file security management includes the number of quantum key distributions, channel noise level, signal strength, time synchronization error, historical security events, user behavior data and environmental factors; the equally divided interval security data includes multi-period cracking risk coefficients; the key update performance impact data includes the update load impact coefficient and the key validity coefficient.

[0011] In a preferred embodiment, the specific method for obtaining the multi-period cracking risk coefficient is as follows: the time required for key distribution, the time the key is stored in the file management system, the time required for the user to access the key, and the file management interaction time related to the key are obtained to establish a quantum key life cycle model; based on an exponential growth function, the process of gradually increasing cracking risk with time is simulated to obtain a cracking probability function; according to the quantum key life cycle model and the cracking probability function, a preset dynamically adjusted attack capability coefficient is introduced to perform data analysis to calculate the cracking risk coefficient of each equal partition; the cracking risk coefficients of all equal partitions are added to obtain a multi-period cracking risk coefficient.

[0012] In a preferred embodiment, the specific method for obtaining the update load influence coefficient is as follows: obtain in real time the computing resource consumption rate and key update delay time corresponding to the interval E time period within the P time period when the key is distributed and received during offline file security management, and number the computing resource consumption rate and key update delay time corresponding to the interval E time period within the P time period when the key is distributed and received during offline file security management; calculate the update load standard deviation and the update load average value through the computing resource consumption rate and key update delay time corresponding to the interval E time period within the P time period when the key is distributed and received during offline file security management; calculate the update load variation coefficient based on the update load standard deviation and the update load average value; calculate the update load influence coefficient based on the update load variation coefficient.

[0013] In a preferred embodiment, the specific method for obtaining the key validity coefficient is as follows: performing quantum bit error rate measurement in a simulation scenario to obtain quantum bit error rate intensity data for each frequency range, performing spectral analysis on the measured quantum bit error rate data, identifying the frequency range in which the channel noise generated by the system is located, and calculating the channel noise density; integrating the channel noise density in all frequency ranges to obtain the total channel noise density; calculating the noise interference power based on the total channel noise density combined with the area around the system; measuring the voltage and current of the simulation scenario to calculate the input power, and combining the input power with the noise interference power calculation to obtain the key validity coefficient.

[0014] In a preferred embodiment, the key update frequency effect evaluation model is constructed based on the BP neural network according to the equally divided interval security data and the key update performance impact data, specifically: the obtained multi-period cracking risk coefficient, update load impact coefficient and key validity coefficient are used to construct a line key update frequency effect evaluation model to generate a key update frequency effect evaluation coefficient.

[0015] In a preferred embodiment, the frequency-effect curve is drawn according to the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency, specifically as follows: obtaining the key update frequency effect evaluation coefficient of the output under different key update frequency settings from the key update frequency effect evaluation model; establishing a rectangular coordinate system, marking the horizontal axis as the line key update frequency value, and the vertical axis as the key update frequency evaluation coefficient; marking the key update frequency evaluation coefficient corresponding to each key update frequency value on the rectangular coordinate system to form data points; and connecting the data points to form a frequency-effect curve.

[0016] In a preferred embodiment, the data analysis of the frequency-effect curve is performed to obtain the screened quantum key update frequency, specifically: several peaks of the frequency-effect curve are obtained, and the change slope analysis of each peak is performed respectively. The key update frequency corresponding to the peak with the change slope closest to 1 is taken as the optimal key update frequency, and it is used as the final quantum key update frequency for offline file security management.

[0017] A system for offline file security management and control based on quantum keys and national secret algorithms is characterized in that it includes a process simulation module, a data acquisition module, a data analysis module, a frequency analysis module and a frequency screening module, and there are connections between the modules; the process simulation module is used to obtain historical data of quantum key distribution and reception during offline file security management, and simulate the process of quantum key distribution and reception during offline file security management based on the historical data and simulation software; the data acquisition module is used to set several simulated quantum key update frequencies, divide the time before the quantum key update of the simulation process in the simulation software into equal parts, and obtain security data of the equally divided intervals and key update performance impact data; the data analysis module is used to construct a key update frequency effect evaluation model based on the BP neural network according to the security data of the equally divided intervals and the key update performance impact data; the frequency analysis module is used to draw a frequency-effect curve diagram according to the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency; the frequency screening module is used to perform data analysis on the frequency-effect curve diagram to obtain the screened quantum key update frequency, and use it as the final quantum key update frequency for offline file security management.

[0018] The technical effects and advantages of the offline file security management method and system based on quantum key and national secret algorithm of the present invention are as follows:

[0019] 1. The present invention simulates the quantum key distribution and reception process, effectively optimizes the update frequency of the quantum key, and improves the security and performance of the system. Through the analysis of historical data and the use of simulation software, the present invention can quantify the cracking risk coefficient, update load impact coefficient and key validity coefficient in different time periods, and realize dynamic adjustment based on these coefficients to ensure the accuracy and timeliness of key updates. At the same time, the present invention has significant advantages in reducing cracking risks, optimizing resource utilization, and improving system responsiveness, effectively balancing security and performance requirements, making key updates more intelligent and efficient. In addition, by monitoring the system load and key validity, the present invention can achieve optimized management of the key life cycle, avoid security risks or performance bottlenecks caused by premature or late updates, and thus provide strong technical support and protection for the security management and control of offline files.

[0020] 2. The present invention realizes the scientific evaluation and optimization of the key update frequency based on the key update frequency effect evaluation model constructed based on the BP neural network. The model comprehensively considers the multi-period cracking risk coefficient, the update load impact coefficient and the key validity coefficient, and effectively generates the key update frequency effect evaluation coefficient, thereby providing data support for key updates; specifically, by drawing a frequency-effect curve, the effect evaluation under different key update frequency settings can be intuitively displayed, thereby identifying the optimal quantum key update frequency. Finally, through the analysis of the frequency-effect curve, the peak value with the change slope closest to 1 is selected as the optimal update frequency, ensuring the security management of offline files. This method not only improves the efficiency and security of key updates, but also reduces the potential risks caused by frequent updates or untimely updates, and has significant application value and practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a structural diagram of the offline file security management method based on quantum keys and national secret algorithms of the present invention.

[0022] Figure 2 This is a structural diagram of the offline file security management and control system based on quantum keys and national secret algorithms of the present invention. DETAILED DESCRIPTION

[0023] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0024] Example 1, Figure 1The present invention provides an offline file security management method based on quantum key and national secret algorithm, which includes the following steps:

[0025] S1, obtain the historical data of quantum key distribution and reception during offline file security management, and simulate the process of quantum key distribution and reception during offline file security management based on the historical data and simulation software.

[0026] In this embodiment, the historical data of quantum key distribution and reception during offline file security management includes but is not limited to the number of quantum key distributions, channel noise level, signal strength, time synchronization error, historical security events, user behavior data, and environmental factors.

[0027] It should be noted that the number of quantum key distributions is: the number of successful and failed distributions in each time period;

[0028] Channel noise level: the channel noise intensity measured during the key distribution process;

[0029] Signal strength: Signal strength data during distribution to assess channel quality;

[0030] Time synchronization error: Time synchronization deviation between devices affects the timeliness of key distribution;

[0031] User behavior data: frequency and patterns of user key requests;

[0032] Historical security events: Records of security events related to key distribution, including attack attempts and failures;

[0033] Environmental factors: The impact of environmental changes such as temperature and humidity on channel performance.

[0034] In this embodiment, the process of quantum key distribution and reception during offline file security management and control is simulated based on historical data and simulation software, specifically:

[0035] Simulation software is software that can process variables such as signal strength, noise level and time synchronization accuracy to optimize the frequency of quantum key updates, such as QuTiP, NetSquid, GMSim, etc.

[0036] Furthermore, the simulation of the quantum key distribution and reception process during offline file security management specifically includes the following aspects:

[0037] Quantum channel model: simulates the characteristics of quantum channels, including channel noise, signal attenuation, and interference, and analyzes their impact on quantum key distribution;

[0038] Quantum state transfer: Simulate the generation, transmission, and measurement of quantum states, and evaluate the fidelity and security of quantum states under different conditions;

[0039] Key generation rate: Based on the characteristics of the quantum channel and the transmission efficiency of the quantum state, the key generation rate in different scenarios is simulated to select the optimal parameters;

[0040] Security analysis: Simulate potential attacks (such as eavesdropping and channel interference) during quantum key distribution to evaluate the system's anti-attack capabilities;

[0041] Update strategy evaluation: Based on historical data and simulation results, the impact of different key update frequencies on system security and performance is evaluated to provide data support for subsequent frequency optimization;

[0042] Through simulations in these aspects, we can fully understand the performance of quantum key distribution in practical applications and optimize the security management strategy of offline files. I will not go into details here.

[0043] S2, setting the quantum key update frequency of several simulations, dividing the time before the quantum key update of the simulation process in the simulation software into equal parts, and obtaining the security data and key update performance impact data of the divided intervals.

[0044] In this embodiment, the equally divided interval security data includes multi-period cracking risk coefficients; the key update performance impact data includes an update load impact coefficient and a key validity coefficient.

[0045] The multi-period cracking risk factor assesses the attack risk of existing keys over different time periods, primarily analyzing an attacker's ability to guess and crack the key. By monitoring key security changes at different stages, this factor reveals when keys are likely to be cracked, providing data support for optimizing the frequency of quantum key updates. By comprehensively considering these risks, the probability of system attack can be effectively reduced, ensuring an optimal balance between key update frequency and security requirements.

[0046] Analyzing the cracking risk coefficient over multiple time periods has the following advantages for evaluating system effectiveness and selecting the optimal quantum key update frequency:

[0047] Time-specific targeting: By segmenting different time periods, the vulnerability of keys within specific time periods can be clearly identified, helping decision-makers to update encryption during high-risk periods.

[0048] Quantifying attack risk: Providing a specific cracking risk coefficient provides a quantitative basis for security assessment, facilitates comparison with historical data, and optimizes update frequency.

[0049] Performance optimization: Evaluate the performance of keys in actual operations to ensure that updates do not cause system delays or insufficient bandwidth due to excessive frequency.

[0050] Lifecycle management: By monitoring the validity and usage cycle of keys, it is possible to accurately determine when to update them and prevent expired keys from being used;

[0051] Attack pattern recognition: Analyze attacker behavior patterns to help identify possible attack strategies, thereby adjusting update frequency to respond to potential threats;

[0052] Data-driven decision-making: Based on multi-period data, the decision-making process is supported, so that the update strategy is not based solely on experience, but on actual data and risk assessment;

[0053] Flexibility and adaptability: Based on real-time risk assessment results, the update frequency can be quickly adjusted to adapt to the ever-changing attack environment and techniques;

[0054] In summary, analyzing the multi-period cracking risk coefficient is of great significance for evaluating the system effect and selecting the optimal quantum key update frequency.

[0055] The specific method for obtaining the multi-period cracking risk coefficient is as follows:

[0056] The time required for key distribution, the time the key is stored in the file management system, the time required for users to access the key, and the file management interaction time related to the key are obtained to establish a quantum key lifecycle model;

[0057] Based on the exponential growth function, we simulate the process of cracking risk gradually increasing over time and obtain the cracking probability function;

[0058] Based on the quantum key lifecycle model and cracking probability function, a preset dynamically adjusted attack capability coefficient is introduced to perform data analysis and calculate the cracking risk coefficient of each equal partition.

[0059] The cracking risk coefficients of all equal partitions are added together to obtain the multi-period cracking risk coefficients.

[0060] The specific calculation formula of the quantum key life cycle model is as follows:

[0061]

[0062] The specific calculation formula of the cracking probability function is as follows:

[0063]

[0064] The specific calculation formula of the attack capability coefficient is as follows:

[0065]

[0066] The specific calculation formula of the multi-period cracking risk coefficient is as follows:

[0067]

[0068] Where, is the key lifecycle, is the time the key is stored in the file management system, The time required for the user to access the key, is the time required for key distribution, Manage interaction time for files associated with keys, is the probability that the key is cracked at time t, is the attack capability coefficient, is the preset initial attack capability. is the attacker's technology iteration rate, For computing power, is the strength of quantum key agreement, To crack the risk factor in multiple time periods, is the key lifecycle between the i-th equal partitions, is the total number of equally divided intervals, is the attack capability coefficient between the i-th equal partitions, is the label between equal partitions.

[0069] The update load impact factor is used to assess the system's performance load during quantum key updates to determine the optimal update frequency. This factor quantifies the consumption of system resources (such as computing power, network bandwidth, and processing time) by key updates, specifically the potential performance bottlenecks caused by overly frequent updates, such as increased latency or reduced bandwidth utilization. By analyzing the update load impact factor, the system can find a balance between update frequency and system performance while ensuring security, avoiding the impact of either excessively frequent or infrequent updates on system stability and security.

[0070] Analyzing the update load impact coefficient has the following advantages for evaluating system performance and selecting the optimal quantum key update frequency:

[0071] Balancing security and performance: By analyzing the update load impact coefficient, we can avoid system performance degradation caused by overly frequent updates, such as increased latency or excessive bandwidth usage, while ensuring timely key updates to address security threats.

[0072] Optimize resource utilization: This coefficient helps the system evaluate resource consumption during key updates, thereby optimizing the utilization of computing, storage, and network bandwidth, and avoiding wasting system resources on unnecessary frequencies.

[0073] Improve system responsiveness: By controlling the update load impact coefficient, the system can maintain a low performance burden during the key update process, thereby improving system response speed under high load or emergency conditions and maintaining good performance;

[0074] Preventing performance bottlenecks: Analyzing this coefficient can help identify update frequencies that may lead to system performance bottlenecks, avoid overly frequent updates when the system is under high load, and ensure the overall stability and continuity of the system.

[0075] Dynamic adaptability: Based on changes in system load in different environments, updating the load impact coefficient can help the system dynamically adjust the frequency of quantum key updates, thereby flexibly adapting to needs in different usage scenarios and ensuring the optimal key update strategy;

[0076] In summary, the update load impact coefficient is of great significance for evaluating the system effect and selecting the optimal quantum key update frequency.

[0077] The specific method for obtaining the updated load influence coefficient is as follows:

[0078] Obtain in real time the computing resource consumption rate and key update delay time corresponding to the interval E time period during key distribution and acceptance during offline file security management, and number the computing resource consumption rate and key update delay time corresponding to the interval E time period during key distribution and acceptance during offline file security management;

[0079] The update load standard deviation and update load average are calculated by calculating the resource consumption rate and key update delay time corresponding to the interval E time period during key distribution and reception during offline file security management.

[0080] Calculate the update load variation coefficient based on the update load standard deviation and the update load average;

[0081] The update load influence coefficient is calculated based on the update load variation coefficient.

[0082] The specific calculation formula for the updated load standard deviation is as follows:

[0083]

[0084] The specific calculation formula for updating the load average value is as follows:

[0085]

[0086] The specific calculation formula for the update load influence coefficient is as follows:

[0087]

[0088] Where, To update the load standard deviation, To update the load average, The total number of numbers for calculating resource consumption rate and key update delay time, The number for calculating resource consumption rate and key update delay time, is the computing resource consumption rate, is the key update delay time, To update the load influence coefficient.

[0089] The key validity coefficient is used to evaluate the effectiveness and security performance of quantum keys throughout their entire life cycle. By monitoring the applicability of keys in different application scenarios and time periods, it ensures that they will not become invalid during use due to decreased security or increased attack risk. This coefficient can reflect the service life of the key in a specific environment and its impact on system security, helping to identify when the key needs to be updated to maintain high security. By comprehensively analyzing the key validity coefficient, the system can determine the optimal quantum key update frequency to avoid overly frequent updates while ensuring key security.

[0090] Analyzing the key validity coefficient has the following advantages for evaluating system performance and selecting the optimal quantum key update frequency:

[0091] Dynamic adaptability: By monitoring the validity of keys in different application scenarios in real time, the key validity coefficient can dynamically reflect the key usage status, helping the system quickly identify when key updates are needed, thereby adapting to changing security requirements in different environments;

[0092] Optimizing key lifecycle: The key validity coefficient helps balance key lifespan and security, avoiding security risks or resource waste caused by updating keys too early or too late, and ensuring that key updates occur at their most effective time.

[0093] Preventing security vulnerabilities: By continuously evaluating the validity of keys, the system can identify potential security vulnerabilities or outdated keys in advance and update them in a timely manner, reducing the chances of attackers exploiting old keys and thus improving the overall security of the system.

[0094] Reducing unnecessary update overhead: This coefficient can effectively identify whether a key remains secure within a specific period of time, avoiding the burden of overly frequent updates on the system, thereby improving resource utilization efficiency and reducing system load;

[0095] Support for multiple application scenarios: The key validity coefficient can be adjusted according to different application environments (such as high-security scenarios or low-risk scenarios), providing the system with a more flexible update frequency scheme to ensure the best balance of security and performance in various application scenarios;

[0096] In summary, the key validity coefficient is of great significance for evaluating the system effect and selecting the optimal quantum key update frequency.

[0097] The specific method for obtaining the key validity coefficient is as follows:

[0098] Perform qubit error rate measurements in a simulation scenario to obtain qubit error rate intensity data for each frequency range. Perform spectrum analysis on the measured qubit error rate data to identify the frequency range of the channel noise generated by the system and calculate the channel noise density.

[0099] Integrate the channel noise density in all frequency ranges to obtain the total channel noise density;

[0100] Calculate the noise interference power based on the total channel noise density and the area around the system;

[0101] The voltage and current of the simulated scenario are measured to calculate the input power, and the input power is combined with the noise interference power to calculate the key validity coefficient.

[0102] The specific calculation formula of the channel noise density is as follows:

[0103]

[0104] The specific calculation formula of the key validity coefficient is as follows:

[0105]

[0106] Where, is the channel noise density, is the key validity coefficient, is the maximum frequency, is the minimum frequency, is the channel noise amplitude at frequency f, is the key loss rate detected, is the key usage frequency, is the voltage of the simulated scenario, is the current of the simulated scenario.

[0107] This implementation effectively optimizes the update frequency of quantum keys and improves the security and performance of the system by simulating the quantum key distribution and reception process. Through the analysis of historical data and the use of simulation software, the present invention can quantify the cracking risk coefficient, update load impact coefficient and key validity coefficient in different time periods, and implement dynamic adjustment based on these coefficients to ensure the accuracy and timeliness of key updates. At the same time, the present invention has significant advantages in reducing cracking risks, optimizing resource utilization, and improving system responsiveness, effectively balancing security and performance requirements, making key updates more intelligent and efficient. In addition, by monitoring the system load and key validity, the present invention can achieve optimized management of the key life cycle, avoid security risks or performance bottlenecks caused by premature or late updates, and thus provide strong technical support and protection for the security management and control of offline files.

[0108] In embodiment 2, S3, a key update frequency effect evaluation model is constructed based on a BP neural network according to the equally divided interval security data and the key update performance impact data.

[0109] The key update frequency effect evaluation model is constructed based on the BP neural network according to the equally divided interval security data and the key update performance impact data, specifically:

[0110] The obtained multi-period cracking risk coefficient, update load impact coefficient and key validity coefficient are used to construct a line key update frequency effect evaluation model to generate a key update frequency effect evaluation coefficient;

[0111] The specific calculation formula of the key update frequency effect evaluation coefficient is as follows:

[0112]

[0113] Where, is the key update frequency effect evaluation coefficient, The preset proportional coefficient for multi-period risk factor cracking, To update the preset proportional coefficient of the load influence coefficient, is the preset proportional coefficient of the key validity coefficient, To crack the risk factor in multiple time periods, To update the load influence coefficient, is the key validity coefficient

[0114] S4. Draw a frequency-effect curve based on the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency.

[0115] According to the output of the line key update frequency effect evaluation model and the corresponding quantum key update frequency, a frequency-effect curve is drawn, specifically:

[0116] Obtaining key update frequency effect evaluation coefficients output under different key update frequency settings from a key update frequency effect evaluation model;

[0117] Establish a rectangular coordinate system, with the horizontal axis representing the line key update frequency value and the vertical axis representing the key update frequency evaluation coefficient;

[0118] Mark the key update frequency evaluation coefficient corresponding to each key update frequency value on the rectangular coordinate system to form a data point;

[0119] Connect the data points to form a frequency-effect curve.

[0120] S5, perform data analysis on the frequency-effect curve to obtain the screened quantum key update frequency, and use it as the final quantum key update frequency for offline file security management.

[0121] The frequency-effect curve is analyzed to obtain the selected quantum key update frequency, specifically:

[0122] Obtain several peaks of the frequency-effect curve graphs, analyze the change slope of each peak, and take the key update frequency corresponding to the peak with the change slope closest to 1 as the optimal key update frequency. This is used as the final quantum key update frequency for offline file security management.

[0123] This embodiment achieves scientific evaluation and optimization of the key update frequency through a key update frequency effect evaluation model constructed based on a BP neural network. The model comprehensively considers the multi-period cracking risk coefficient, the update load impact coefficient, and the key validity coefficient, and effectively generates a key update frequency effect evaluation coefficient, thereby providing data support for key updates; specifically, by drawing a frequency-effect curve, the effect evaluation under different key update frequency settings can be intuitively displayed, thereby identifying the optimal quantum key update frequency. Finally, through the analysis of the frequency-effect curve, the peak with the change slope closest to 1 is selected as the optimal update frequency, ensuring the security management of offline files. This method not only improves the efficiency and security of key updates, but also reduces the potential risks caused by frequent updates or untimely updates, and has significant application value and practical significance.

[0124] Example 3, Figure 2 The present invention provides an offline file security management and control system based on quantum key and national secret algorithm, including process simulation module, data acquisition module, data analysis module, frequency analysis module and frequency screening module, and there are connections between the modules;

[0125] The process simulation module is used to obtain historical data on quantum key distribution and reception during offline file security management and control, and simulate the process of quantum key distribution and reception during offline file security management and control based on historical data and simulation software;

[0126] The data acquisition module is used to set the quantum key update frequency of several simulations, divide the time before the quantum key update of the simulation process in the simulation software into equal parts, and obtain security data and key update performance impact data of the equal parts;

[0127] The data analysis module is used to construct a key update frequency effect evaluation model based on the BP neural network according to the equally divided interval security data and the key update performance impact data;

[0128] The frequency analysis module is used to draw a frequency-effect curve based on the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency;

[0129] The frequency screening module is used to perform data analysis on the frequency-effect curve to obtain the screened quantum key update frequency, which is used as the final quantum key update frequency for offline file security management.

[0130] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0131] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0132] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0133] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0134] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0135] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Offline file security management method based on quantum key and national secret algorithm, characterized by: The steps include: Obtain historical data on quantum key distribution and reception during offline file security management and control, and simulate the process of quantum key distribution and reception during offline file security management and control based on historical data and simulation software; Set the quantum key update frequency for several simulations, divide the time before the quantum key update in the simulation process in the simulation software into equal parts, and obtain security data and key update performance impact data for the equal-divided intervals; According to the equally divided interval security data and key update performance impact data, a key update frequency effect evaluation model is constructed based on the BP neural network; Draw a frequency-effect curve based on the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency; Data analysis is performed on the frequency-effect curve to obtain the screened quantum key update frequency, which is used as the final quantum key update frequency for offline file security management.

2. The offline file security management method based on quantum key and national secret algorithm according to claim 1 is characterized in that: The historical data of quantum key distribution and reception during offline file security management includes the number of quantum key distributions, channel noise level, signal strength, time synchronization error, historical security events, user behavior data and environmental factors; the equally divided interval security data includes multi-period cracking risk coefficients; the key update performance impact data includes the update load impact coefficient and the key validity coefficient.

3. The offline file security management method based on quantum key and national secret algorithm according to claim 2 is characterized in that: The specific method for obtaining the multi-period cracking risk coefficient is as follows: The time required for key distribution, the time the key is stored in the file management system, the time required for users to access the key, and the file management interaction time related to the key are obtained to establish a quantum key lifecycle model; Based on the exponential growth function, we simulate the process of cracking risk gradually increasing over time and obtain the cracking probability function; Based on the quantum key lifecycle model and cracking probability function, a preset dynamically adjusted attack capability coefficient is introduced to perform data analysis and calculate the cracking risk coefficient of each equal partition. The cracking risk coefficients of all equal partitions are added together to obtain the multi-period cracking risk coefficients.

4. The offline file security management method based on quantum key and national secret algorithm according to claim 3 is characterized in that: The specific method for obtaining the updated load influence coefficient is as follows: Obtain in real time the computing resource consumption rate and key update delay time corresponding to the interval E time period during key distribution and acceptance during offline file security management, and number the computing resource consumption rate and key update delay time corresponding to the interval E time period during key distribution and acceptance during offline file security management; The update load standard deviation and update load average are calculated by calculating the resource consumption rate and key update delay time corresponding to the interval E time period during key distribution and reception during offline file security management. Calculate the update load variation coefficient based on the update load standard deviation and the update load average; The update load influence coefficient is calculated based on the update load variation coefficient.

5. The offline file security management method based on quantum key and national secret algorithm according to claim 4 is characterized in that: The specific method for obtaining the key validity coefficient is as follows: Perform qubit error rate measurements in a simulation scenario to obtain qubit error rate intensity data for each frequency range. Perform spectrum analysis on the measured qubit error rate data to identify the frequency range of the channel noise generated by the system and calculate the channel noise density. Integrate the channel noise density in all frequency ranges to obtain the total channel noise density; Calculate the noise interference power based on the total channel noise density and the area around the system; The voltage and current of the simulated scenario are measured to calculate the input power, and the input power is combined with the noise interference power to calculate the key validity coefficient.

6. The offline file security management method based on quantum key and national secret algorithm according to claim 5 is characterized in that: The key update frequency effect evaluation model is constructed based on the BP neural network according to the equally divided interval security data and the key update performance impact data, specifically: A key update frequency effect evaluation model is constructed based on the obtained multi-period cracking risk coefficient, update load impact coefficient and key validity coefficient, and a key update frequency effect evaluation coefficient is generated.

7. The offline file security management method based on quantum key and national secret algorithm according to claim 6 is characterized in that: According to the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency, a frequency-effect curve is drawn, specifically: Obtaining key update frequency effect evaluation coefficients output under different key update frequency settings from a key update frequency effect evaluation model; Establish a rectangular coordinate system, with the horizontal axis representing the line key update frequency value and the vertical axis representing the key update frequency evaluation coefficient; Mark the key update frequency evaluation coefficient corresponding to each key update frequency value on the rectangular coordinate system to form a data point; Connect the data points to form a frequency-effect curve.

8. The offline file security management method based on quantum key and national secret algorithm according to claim 7 is characterized in that: The frequency-effect curve is analyzed to obtain the selected quantum key update frequency, specifically: Obtain several peaks of the frequency-effect curve graphs, analyze the change slope of each peak, and take the key update frequency corresponding to the peak with the change slope closest to 1 as the optimal key update frequency. This is used as the final quantum key update frequency for offline file security management.

9. The offline file security management method based on quantum key and national secret algorithm according to claim 8 is characterized in that: The specific calculation formula of the quantum key life cycle model is as follows: The specific calculation formula of the cracking probability function is as follows: The specific calculation formula of the attack capability coefficient is as follows: The specific calculation formula of the multi-period cracking risk coefficient is as follows: Where, is the key lifecycle, is the time the key is stored in the file management system, The time required for the user to access the key, is the time required for key distribution, Manage interaction time for files associated with keys, is the probability that the key is cracked at time t, is the attack capability coefficient, is the preset initial attack capability. is the attacker's technology iteration rate, For computing power, is the strength of quantum key agreement, To crack the risk factor in multiple time periods, is the key lifecycle between the i-th equal partitions, is the total number of equally divided intervals, is the attack capability coefficient between the i-th equal partitions, is the label between equal partitions.

10. A system using the offline file security management method based on quantum key and national secret algorithm as described in any one of claims 1 to 9, characterized in that: It includes process simulation module, data acquisition module, data analysis module, frequency analysis module and frequency screening module, and there are connections between the modules; The process simulation module is used to obtain historical data on quantum key distribution and reception during offline file security management and control, and simulate the process of quantum key distribution and reception during offline file security management and control based on historical data and simulation software; The data acquisition module is used to set the quantum key update frequency of several simulations, divide the time before the quantum key update of the simulation process in the simulation software into equal parts, and obtain security data and key update performance impact data of the equal parts; The data analysis module is used to construct a key update frequency effect evaluation model based on the BP neural network according to the equally divided interval security data and the key update performance impact data; The frequency analysis module is used to draw a frequency-effect curve based on the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency; The frequency screening module is used to perform data analysis on the frequency-effect curve to obtain the screened quantum key update frequency, which is used as the final quantum key update frequency for offline file security management.

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