Distributed file encryption system based on domestic OS

Abstract

The invention relates to the technical field of file encryption, in particular to a distributed file encryption system based on a domestic OS, the system comprises a file monitoring module, a dynamic encryption management module, a homomorphic encryption module and a performance optimization module, and based on the OS system, a file access request is captured in real time, and the timestamp of each access is recorded. According to the invention, through dynamic application of AES-128 and AES-256 encryption algorithms, high security of sensitive files is ensured while rapid processing of high-frequency access files is ensured, effective utilization of resources is improved, performance burden caused by excessive encryption is reduced, complex data processing is allowed to be carried out in a state of keeping full encryption of data through introduction of a homomorphic encryption technology, and the security of the sensitive files is improved. According to the method, the security of data is enhanced, security vulnerabilities occurring in the decryption process are avoided, performance optimization of the distributed GPU technology is utilized, the system can efficiently process encryption tasks, the traditional single-node processing bottleneck is relieved, and the response speed and the data processing capacity of the whole system are improved.

Description

Technical Field

[0001] This invention relates to the field of file encryption technology, and in particular to a distributed file encryption system based on a domestically developed operating system. Background Technology

[0002] File encryption technology involves using encryption algorithms to protect electronic documents from unauthorized access and reading. In this field, data is converted into an encrypted format that can only be deciphered by someone with the decryption key when stored or transmitted. It is widely used to protect sensitive information such as personal data, trade secrets, and government secrets. File encryption can be static, acting directly on files stored on a hard drive, or dynamic, encrypting data transmitted over a network.

[0003] Among them, the distributed file encryption system aims to implement encryption measures in a distributed computing environment. The system stores files on multiple network-connected nodes and encrypts them before storing or transmitting them to these nodes. Its main purpose is to provide a secure way to manage and store large amounts of data distributed in different physical locations. Its implementation can significantly improve data security, prevent data leakage, and support flexible data access and backup options.

[0004] Existing technologies fail to optimize for different file access needs, resulting in rigid encryption strategies that cannot dynamically adjust to adapt to changing access patterns and frequencies. This static encryption approach is inefficient and consumes significant computing resources when processing large-scale distributed data, especially when data is stored and accessed in multiple physical locations. In particular, when the system uses a single encryption standard, it cannot flexibly address the different security requirements of high-frequency and low-frequency data access. Furthermore, it lacks the ability to support complex queries while the data is encrypted. Traditional encryption technologies require decryption before data processing, increasing the risk of data being intercepted or leaked during processing. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a distributed file encryption system based on a domestically developed operating system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a distributed file encryption system based on a domestically developed OS, the system comprising: The file monitoring module is based on the OS system, captures file access requests in real time, records the timestamp of each access, determines the average time interval and frequency of access, and judges the access frequency of files based on access patterns to obtain access pattern classification data. The dynamic encryption management module classifies the data based on the access patterns, uses AES-128 for frequently accessed files and AES-256 for low-frequency and sensitive files, updates encryption parameters in real time to match differentiated security requirements, and records the process of each encryption parameter adjustment to generate a parameter adjustment log. The homomorphic encryption module adjusts the log based on the parameters, encrypts the file using a fully homomorphic or semi-homomorphic encryption algorithm, verifies the feasibility of querying and analyzing the file in the encrypted state, and obtains the homomorphic operation log. Based on the homomorphic operation log, the performance optimization module identifies performance bottlenecks in homomorphic encryption operations, optimizes computing speed using distributed GPU acceleration technology, and reallocates operating system resources to optimize operating efficiency, thus obtaining performance efficiency optimization results.

[0007] The present invention is improved in that the steps for determining the average time interval and frequency are specifically as follows: Based on the OS system, by listening to file access events, the timestamp of each access is captured and recorded in real time to form a timestamp list; Based on the timestamp list, the difference between two adjacent timestamps is calculated to obtain a time interval list; Based on the aforementioned list of time intervals, the formula is used: and Calculate the average time interval and access frequency ,in, and Representing the first and The timestamp of the timestamp. This represents the total number of events.

[0008] The present invention is improved in that the steps for obtaining the access pattern classification data are specifically as follows: Based on the average time interval and frequency, the file access pattern is analyzed to determine the file access frequency level and obtain the file frequency level information. Based on the file frequency information, the formula is used: Calculate classification value Files are categorized into high-frequency and low-frequency access files based on their classification values, resulting in categorized access patterns. For file access frequency, For access duration, For the access time interval, To adjust the parameters; Based on the classified access patterns, the access characteristic index of each type of file is calculated, the access characteristics of files with different categories are analyzed, and access pattern classification data is obtained.

[0009] The present invention is improved in that the step of updating the encryption parameters in real time is specifically as follows: Based on the access pattern classification data, the access frequency and sensitivity level of each file are determined. High-frequency access files are encrypted using AES-128, and low-frequency and sensitive files are encrypted using AES-256, generating a list of pre-encryption strategies. Based on the aforementioned list of pre-encryption policies, and considering the current network environment and security requirements, the following formula is adopted: Update encryption parameters To match differentiated security needs, among which, Represents file access frequency. Represents the sensitivity of the document. and It is an adjustment factor based on the current security level.

[0010] The present invention is improved in that the step of obtaining the parameter adjustment log is specifically as follows: Monitor each file encryption parameter adjustment event, recording the file identifier, current encryption algorithm, parameters before change, parameters after change, and change time, using the formula: Calculate parameter change information to obtain preliminary change records with tags, among which, It is the change in encryption parameters. and This indicates the encryption parameter values ​​before and after the change. Weighting parameters based on target security requirements; Based on the marked preliminary change records, integrate the information of each record, including file identifier, current encryption algorithm, parameters before change, parameters after change, and change time, and archive them into the parameter adjustment log.

[0011] The present invention is improved in that the step of encrypting the file specifically includes: Based on the parameter adjustment log, the security requirements and encryption parameters of the file are extracted, including file access frequency, user identity and access type. Fully homomorphic or semi-homomorphic encryption algorithms are adopted, the key length and encryption mode are set, and encryption algorithm decision information for each file is generated. Based on the encryption algorithm decision information, the following formula is used: Encrypt the file to obtain the encrypted file data. ,in, This represents the data content of the file. It is the selected encryption key. and These are weighting coefficients adjusted based on file sensitivity; The encrypted file data is stored, and key information during the encryption process is recorded, including the key, encryption algorithm, and encryption execution time, to obtain an encryption activity record.

[0012] The present invention is improved in that the step of obtaining the homomorphic operation log is specifically as follows: Continuously track and analyze encrypted file queries and analysis activities, record operation types, times, and participant information to obtain file operation information; Based on the file operation information, the collected data is processed using the following formula: Obtain the homomorphic operation log, where, It is a rating of the operation log. Indicates the operation time. Indicates the operation type. Indicates the result of the operation.

[0013] The present invention is improved in that the steps for obtaining the performance efficiency optimization results are specifically as follows: Based on the homomorphic operation log, collect correlation data of performance bottlenecks, including operation duration and resource usage, and generate preliminary performance analysis results based on the collected data; Based on the preliminary performance analysis results, distributed GPU acceleration technology is applied, and the operating system resource configuration is adjusted using the following formula: The calculation process was optimized to obtain performance improvement metrics, among which, It's about performance efficiency. Represents computational tasks. This represents the processing time for the task. Represents the total number of computational tasks; Based on the performance improvement metrics, resource reallocation and configuration optimization are performed to obtain performance efficiency optimization results.

[0014] The present invention is improved in that the steps for processing the low-frequency and sensitive files are specifically as follows: Based on the AES-256, after encrypting low-frequency and sensitive files, the files are sliced. The slices are divided according to size and stored independently in a distributed storage system. The storage location and encryption level information of the slices are recorded to obtain the slice storage index. Based on the slice storage index, if there is a need, the data fragments are reassembled using the following formula: Get the complete file content ,in, Indicates the first Encrypted data of each slice, Indicates the first The weight parameters of each slice during the reassembly process This indicates the total number of slices.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by dynamically applying AES-128 and AES-256 encryption algorithms, the system ensures rapid processing of frequently accessed files while guaranteeing high security for sensitive files. This improves the effective utilization of resources, reduces the performance burden caused by excessive encryption, and allows complex data processing to be performed while maintaining full data encryption. This enhances data security, avoids security vulnerabilities that may occur during decryption, and utilizes the performance optimization of distributed GPU technology to enable the system to efficiently process encryption tasks. This alleviates the bottleneck of traditional single-node processing and improves the overall system's response speed and data processing capabilities. Attached Figure Description

[0016] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart illustrating the determination of the average time interval and frequency in this invention. Figure 3 This is a flowchart illustrating the process of obtaining access pattern classification data in this invention. Figure 4 This is a flowchart illustrating the real-time updating of encryption parameters in this invention; Figure 5 This is a flowchart of the parameter adjustment log acquisition process in this invention; Figure 6 This is a flowchart illustrating the file encryption process in this invention; Figure 7 This is a flowchart illustrating the process of obtaining the homomorphic operation log in this invention. Figure 8 This is a flowchart illustrating the process of obtaining performance efficiency optimization results in this invention. Figure 9 This is a flowchart illustrating the processing of low-frequency and sensitive files in this invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0019] Example Please see Figure 1 This invention provides a technical solution: a distributed file encryption system based on a domestically developed OS, comprising: The file monitoring module is based on the OS system, captures file access requests in real time, records the timestamp of each access, determines the average time interval and frequency of access, and judges the access frequency of files based on access patterns to obtain access pattern classification data. The dynamic encryption management module classifies data based on access patterns, uses AES-128 for frequently accessed files and AES-256 for low-frequency and sensitive files, updates encryption parameters in real time to match differentiated security requirements, and records the process of each encryption parameter adjustment to generate a parameter adjustment log. The homomorphic encryption module, based on parameter adjustment logs, encrypts files using fully homomorphic or semi-homomorphic encryption algorithms, verifies the feasibility of querying and analyzing files in the encrypted state, and obtains homomorphic operation logs. The performance optimization module identifies performance bottlenecks in homomorphic encryption operations based on homomorphic operation logs, optimizes computation speed using distributed GPU acceleration technology, and reallocates operating system resources to optimize operating efficiency, thus obtaining performance efficiency optimization results.

[0020] Access pattern classification data includes access type identifier, frequency level, and time characteristics; parameter adjustment logs include encryption algorithm usage records, parameter update details, and security level adjustment items; homomorphic operation logs include encryption algorithm application examples, query operation compatibility, and analysis function execution records.

[0021] Please see Figure 2 The specific steps for determining the average time interval and frequency are as follows: Based on the OS system, by listening to file access events, the timestamp of each access is captured and recorded in real time to form a timestamp list; The operating system is configured with sufficient resources to ensure the continuity of data capture. The recording operation needs to be optimized to reduce the impact on system performance. The recorded timestamp data must be accurate to the millisecond level to meet the needs of subsequent processing. Data storage uses a NoSQL database to enable fast data writing and querying. The creation of the timestamp list is not just about recording a point in time, but about capturing the specific time of the event and the environmental state in which it occurred. Time recording functionality needs to be embedded in the event listener to ensure that every file access event can be recorded instantly and accurately.

[0022] Based on the timestamp list, calculate the difference between two adjacent timestamps to obtain the time interval list; The difference between two adjacent timestamps in the timestamp list is calculated. By calculating the difference for each pair of consecutive timestamps, the specific access interval can be obtained. The key is the accurate acquisition of timestamps and the accurate calculation of time differences. The calculation of each time difference needs to take into account the system time synchronization issue to ensure that it reflects the actual situation of file access without errors. After the calculation is completed, the time interval data will be stored in another data table for subsequent analysis and processing. The data will serve as the basis for judging the file access pattern. The accurate time interval is the key data for analyzing file usage patterns and user behavior patterns, and is directly related to the next step of frequency calculation and pattern determination.

[0023] Based on the list of time intervals, the formula is used: and Calculate the average time interval and access frequency ,in, and Representing the first and The timestamp of the timestamp. It is the total number of events; Given 10 timestamps: [100, 110, 120, 150, 160, 170, 200, 210, 220, 230] (seconds), calculate the time interval: [10, 10, 30, 10, 10, 30, 10, 10, 10]. Then use the formula to calculate the average time interval. and access frequency The calculation process is as follows: and The results indicate that a file access occurs on average once every 14.44 seconds, with an access frequency of 0.07 times per second, which can be used to analyze system load and user access habits.

[0024] Please see Figure 3 The specific steps for obtaining access pattern classification data are as follows: Based on the average time interval and frequency, file access patterns are analyzed to determine the file access frequency level and obtain file frequency level information. Based on access frequency and average time interval data, the access patterns of each file are analyzed. Frequency distribution analysis is used to determine the access frequency level of the files. The process involves a large amount of statistical processing of data and frequency calculation. First, the system extracts the access records of each file from the dataset, then calculates the number of times each file is accessed, and then determines the frequency based on the total access time and the number of accesses. The analysis not only helps to understand the overall trend of file access, but also identifies files with abnormally high or low access frequencies. To make classification judgments, a classification threshold is set. For example, files with more than 50 accesses and an average access interval of less than 5 minutes are classified as "high-frequency access", while files with less than 20 accesses and an average access interval of more than 10 minutes are classified as "low-frequency access". The frequency level data will then be used for further access pattern classification.

[0025] Based on file frequency information, the formula is used: Calculate classification value Files are categorized into high-frequency and low-frequency access files based on their classification values, resulting in categorized access patterns. For file access frequency, For access duration, For the access time interval, To adjust the parameters; A file was accessed 100 times in the observation window, with a total access time of 400 minutes and an average time interval of 4 minutes. The parameter was adjusted to 1, and numerical values ​​were inserted for calculation. The result indicates that the access pattern classification value of this file is 8000, which helps to further analyze the importance of the file and adjust the system's resource allocation strategy.

[0026] Based on the classified access patterns, the access characteristic index of each type of file is calculated, the access characteristics of files with different categories are analyzed, and access pattern classification data is obtained. By combining the categorized access pattern data and performing statistical analysis, and calculating access characteristic indicators for each file category—for example, analyzing the average access interval and total access duration of frequently accessed files—a detailed description of each category can be obtained. This information is crucial for optimizing system performance. For instance, for frequently accessed files (accessed more than 50 times and with an average access interval of less than 5 minutes), the system needs to increase caching resources or perform more frequent backups to ensure data integrity and efficient access. In contrast, infrequently accessed files can be archived or cleaned up. This not only helps to analyze data access patterns but also guides the rational allocation of resources.

[0027] Please see Figure 4 The specific steps for updating encryption parameters in real time are as follows: Based on access pattern classification data, determine the access frequency and sensitivity level of each file, use AES-128 encryption for high-frequency access files, and use AES-256 encryption for low-frequency and sensitive files to generate a list of pre-encryption strategies. The system collects access patterns and sensitivity data for each file, classifying them into high-risk or low-risk categories based on predetermined criteria. High-risk files, due to their high access frequency and sensitive data, are pre-defined for AES-128 encryption, while highly sensitive files with low access frequency are pre-defined for the more secure AES-256 encryption. This classification and policy definition process involves detailed recording of the classification criteria and policies for each file, ensuring traceability and transparency of each operation. This allows for real-time monitoring of security policy implementation and adjustments based on real-time data, ensuring that data protection measures always meet current security requirements.

[0028] Based on the list of pre-encryption policies, and considering the current network environment and security, the following formula is used: Update encryption parameters To match differentiated security needs, among which, Represents file access frequency. Represents the sensitivity of the document. and It adjusts the coefficients based on the current security level to ensure that the encryption strategy for each file is up-to-date and most suitable. If the access frequency of a file ( The sensitivity is 0.8 (indicating very frequent access), and its sensitivity is... The rating is 0.5 (moderate sensitivity), and the safety parameters are... Set it to 1.5. Setting it to 1.0, the calculation yields: The calculation result 1.7 will be used to determine the actual encryption strength, instructing the system to select appropriate encryption techniques and parameter settings to address security risks.

[0029] Please see Figure 5 The specific steps for obtaining parameter adjustment logs are as follows: Monitor each file encryption parameter adjustment event, recording the file identifier, current encryption algorithm, parameters before change, parameters after change, and change time, using the formula: Calculate parameter change information to obtain preliminary change records with tags, among which, It is the change in encryption parameters. and This indicates the encryption parameter values ​​before and after the change. Weighting parameters based on target security requirements; When monitoring encryption parameter adjustments, the system captures every encryption parameter change event of a file and records the file identifier, the current encryption algorithm used, the parameter values ​​before and after the change, and the change time. The data is collected in real time and stored in a temporary database. The system initially classifies the changed data and marks those major changes that exceed a preset threshold, generating marked change records. This process ensures the capture and recording of all key information, providing a foundation for further log processing.

[0030] Based on the initial change records marked with tags, integrate the information of each record, including file identifier, current encryption algorithm, parameters before change, parameters after change, and change time, and archive them into the parameter adjustment log; The original encryption parameters of a file It was originally 128 bits, now it's being changed to 256 bits. The value is 0.5. The significance of the change is calculated using the formula: The calculation result 64 indicates that the significance of the parameter change is moderate. If the system's threshold is set to 50, this change will be marked as significant and specially recorded. This result shows that this change in the encryption parameter is significant enough to be recorded in the system's security log for further analysis.

[0031] Please see Figure 6 The specific steps for encrypting a file are as follows: Based on the parameter adjustment log, the security requirements and encryption parameters of the file are extracted, including file access frequency, user identity and access type. Fully homomorphic or semi-homomorphic encryption algorithms are adopted, the key length and encryption mode are set, and encryption algorithm decision information for each file is generated. Key length refers to the number of bits in the key used to encrypt and decrypt data. Key length determines encryption strength because the longer the key, the more attempts are required to crack the encryption, and the higher the security. Key lengths can be 128, 192, and 256 bits. Encryption modes can be selected from ECB (Electronic Codebook Mode), CBC (Cipher Block Chaining Mode), CFB (Cipher Feedback Mode), OFB (Output Feedback Mode), and GCM (Galois / Counter Mode), etc. Access parameter adjustment logs are used to extract the security requirements and previous encryption parameters for each file. The analysis process involves in-depth mining of log data to ensure an understanding of the unique security requirements and historical encryption measures for each file. Through comprehensive evaluation of this data, it is possible to accurately determine whether a fully homomorphic or semi-homomorphic encryption algorithm should be used for each file. The generated decision information not only includes the choice of encryption type but also specifies personalized security measures for each file, preparing for the next encryption operation.

[0032] Based on the decision information from the encryption algorithm, the formula is adopted: Encrypt the file to obtain the encrypted file data. ,in, This represents the data content of the file. It is the selected encryption key. and These are weighting coefficients adjusted based on file sensitivity; If file data The text content is "exampledata", its numerical representation is the numeric code 1001, and the selected key is... The value is 2001, representing the weight due to security requirements. and The values ​​are 1.5 and 0.5 respectively. The calculation process is as follows: This result demonstrates that by adjusting the weighting parameters, it is possible to ensure that the encryption operation meets file security standards while maintaining operational flexibility, thus ensuring the encrypted data... 2502 represents the encrypted state.

[0033] The encrypted file data is stored, and key information during the encryption process is recorded, including the key, encryption algorithm, and encryption execution time, to obtain an encryption activity record; Once a file is encrypted, the system enters the storage and recording phase. At this point, the encrypted file data is securely stored, and key operational information is recorded simultaneously, including but not limited to details of the key used, the type of encryption algorithm selected, and the specific time of encryption implementation. The recording process is crucial because it provides a way to ensure the transparency and traceability of each step of the operation. The generated encryption activity log provides data support for future security audits and performance evaluations, ensuring the organization's security compliance and the effectiveness of data protection measures.

[0034] Please see Figure 7 The specific steps for obtaining the homomorphic operation log are as follows: Continuously track and analyze encrypted file queries and analysis activities, record operation types, times, and participant information to obtain file operation information; After the real-time monitoring system is started, it continuously tracks all file query and analysis operations according to fully homomorphic or semi-homomorphic encryption methods. The monitoring content includes the type of each operation, execution time, and the identity information of the executor. The information is recorded in the system to ensure the comprehensiveness and accuracy of the data. Each data call is encoded and identified, and its specific access rights under the encryption state are matched. The query information under the file encryption state is matched with the query authorization of the database to exclude unauthorized operation requests. Each query result is recorded in the log according to the operation time and the importance of the file, generating a homomorphic operation log.

[0035] Based on file operation information, the collected data is processed using the following formula: Obtain the homomorphic operation log, where, It is a rating of the operation log. Indicate the operation time to reflect the increasing importance of the time factor in the log. Indicate the operation type, highlighting that common operations have a smaller impact, while uncommon operations have a larger impact. The result of the operation is represented by a logarithmic transformation. To handle and optimize the impact of abnormal results; By using the square of time, the square root of operation type, and the logarithmic transformation of operation results, the segmentation of the importance of different operations is enhanced, enabling the log to reflect the actual weight of each operation. Let the operation time be... The value is 4, and the operation type is... The result is 16. The value is 64, calculated as follows: The result indicates that the operation log value of 21.806 reflects the relative importance of this operation and is stored as part of the homomorphic operation log for subsequent auditing and analysis.

[0036] Please see Figure 8 The specific steps for obtaining the performance efficiency optimization results are as follows: Based on the homomorphic operation logs, collect correlation data of performance bottlenecks, including operation duration and resource usage, and generate preliminary performance analysis results based on the collected data; In analyzing homomorphic operation logs, data is first collected from each distributed node to obtain detailed records of operation duration and resource usage, including CPU utilization, memory consumption, and the frequency of input / output operations. Then, data mining techniques are used to analyze the data points and identify performance bottlenecks, such as insufficient processor speed or memory overload. Based on the analysis results, a preliminary performance analysis report is automatically generated, which points out the peaks and troughs of resource usage in each operation, providing decision support for subsequent optimization measures.

[0037] Based on the preliminary performance analysis results, distributed GPU acceleration technology is applied, and the operating system resource configuration is adjusted using the following formula: The calculation process was optimized to obtain performance improvement metrics, among which, It's about performance efficiency. Represents computational tasks. This represents the processing time for the task. Represents the total number of computational tasks; There are three computational tasks, and the processing time for each task is... , , The task complexities are respectively , , Calculate performance efficiency according to the formula : The results show that, taking into account the complexity of the task and the processing time, the system's performance efficiency is approximately 0.161, indicating the amount of work completed per unit time. This helps to further refine resource allocation and task optimization strategies, thereby improving system performance.

[0038] Based on performance improvement metrics, resources are reallocated and configured to optimize performance efficiency and ensure maximum operational efficiency. Based on performance improvement metrics, the distributed computing resources and GPU configuration are automatically adjusted, including increasing memory allocation and optimizing processor time sharding, thereby effectively utilizing the processing power of each node. Through these adjustments, the running efficiency has been successfully optimized, generating performance efficiency optimization results. This ensures that each operation can be executed under the optimal resource configuration, achieving the expected efficiency improvement.

[0039] Please see Figure 9 The specific steps for processing low-frequency and sensitive files are as follows: Based on AES-256, after encrypting low-frequency and sensitive files, the files are sliced. Slices are divided according to size and stored independently in a distributed storage system. The storage location and encryption level information of each slice are recorded, generating a slice storage index. After encrypting low-frequency and sensitive files using AES-256, the files are sliced ​​according to the encryption strategy. Each file slice is divided according to a set standard size. The segmentation process involves the specific length of the file and the preset slice size. The file length is obtained through file attributes, and the slice size is set according to the storage system configuration, with slice sizes ranging from 1MB, 64MB, to 256MB. Each slice is independently stored on different nodes in the distributed storage system. The storage location information is determined and recorded by the system allocation algorithm to ensure that the location of each slice can be accurately tracked. Encryption level information is recorded along with the slices, and an index is built to optimize data retrieval efficiency, resulting in an index record of encryption and slice storage.

[0040] Based on the slice-based storage index, data fragments are reassembled if needed, using the following formula: Get the complete file content ,in, Indicates the first Encrypted data of each slice, Indicates the first The weight parameters of each slice during the reassembly process Indicates the total number of slices; There are 5 slices, and the encrypted data in each slice is... The weights are [10, 20, 30, 40, 50] respectively. Set to [1, 2, 3, 4, 5] respectively, total number of slices The calculation process is as follows: The result shows that the total encrypted data after the entire file is reassembled is 110, which means that through weight adjustment, more important file slices contribute more data content during the reassembly process, ensuring the integrity and security of the file.

[0041] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A distributed file encryption system based on domestic OS, characterized by: The system comprises: The file monitoring module is based on the OS system, captures file access requests in real time, records the timestamp of each access, determines the average time interval and frequency of access, and judges the access frequency of files based on the access mode to obtain access mode classification data; The dynamic encryption management module adopts AES-128 for high-frequency access files and AES-256 for low-frequency and sensitive files based on the access mode classification data, updates encryption parameters in real time to match differentiated security requirements, and records the adjustment process of each encryption parameter to generate a parameter adjustment log; The homomorphic encryption module encrypts the file based on the parameter adjustment log through a fully homomorphic or semi-homomorphic encryption algorithm, verifies the feasibility of querying and analyzing the file in an encrypted state, and obtains a homomorphic operation log; The performance optimization module identifies the performance bottleneck in the homomorphic encryption operation according to the homomorphic operation log, optimizes the computing speed by using distributed GPU acceleration technology, and reallocates the operating system resources to optimize the operating efficiency, thereby obtaining a performance efficiency optimization result.

2. The distributed file encryption system based on domestic OS according to claim 1 is characterized in that: The steps for determining the average time interval and frequency are specifically as follows: Based on the OS system, by monitoring file access events, the timestamp of each access is captured and recorded in real time to form a timestamp list; Based on the timestamp list, calculating the difference between two adjacent timestamps to obtain a time interval list; Based on the list of time intervals, the formula is used: and Calculate the average time interval and access frequency ,in, and Respectively represent and Timestamp, is the total number of events.

3. The distributed file encryption system based on domestic OS according to claim 1 is characterized in that: The steps for obtaining the access mode classification data are specifically as follows: Analyze the file access pattern based on the average time interval and frequency, determine the file access frequency level, and obtain file frequency level information; Based on the file frequency information, the formula is used: Calculating Categorical Values , the files are divided into high-frequency and low-frequency access files according to the classification value, and the classified access patterns are obtained, where is the file access frequency, For the duration of the visit, is the access time interval, To adjust the parameters; Based on the classified access patterns, access characteristic indicators of each type of file are calculated, and access characteristics of files of different categories are analyzed to obtain access pattern classification data.

4. The distributed file encryption system based on domestic OS according to claim 1 is characterized in that: The steps of updating the encryption parameters in real time are specifically as follows: Based on the access pattern classification data, determine the access frequency and sensitivity level of each file, use AES-128 encryption for high-frequency access files, use AES-256 encryption for low-frequency and sensitive files, and generate a pre-encryption strategy list; Based on the pre-encryption strategy list, according to the current network environment and security, the formula is adopted: Update encryption parameters , matching differentiated security requirements, where Represents the file access frequency, Represents the sensitivity of the file. and is an adjustment factor based on the current security level.

5. The distributed file encryption system based on domestic OS according to claim 1 is characterized in that: The steps for obtaining the parameter adjustment log are specifically as follows: Monitor each file encryption parameter adjustment event, record the file identifier, current encryption algorithm, parameters before change, parameters after change, and change time, using the formula: Calculate the parameter change information and obtain a preliminary change record with a mark, where: is the change in encryption parameters, and Indicates the encryption parameter values ​​before and after the change. is the weight parameter for the target security requirement; According to the marked preliminary change record, each record information, including the file identifier, the current encryption algorithm, the parameters before the change, the parameters after the change and the change time, is integrated and filed into the parameter adjustment log.

6. The distributed file encryption system based on domestic OS according to claim 1 is characterized in that: The steps of encrypting the file are specifically as follows: Based on the parameter adjustment log, security requirements and encryption parameters of the file are extracted from the log, including file access frequency, user identity and access type, a fully homomorphic or semi-homomorphic encryption algorithm is adopted, a key length and an encryption mode are set, and encryption algorithm decision information for each file is generated; Based on the encryption algorithm decision information, the formula is adopted: Encrypt the file to obtain the encrypted file data ,in, Represents the data content of the file. is the selected encryption key, and They are weight coefficients adjusted based on file sensitivity; The encrypted file data is stored, and key information in the encryption process is recorded, including the key, encryption algorithm and encryption execution time, to obtain an encryption activity record.

7. The distributed file encryption system based on domestic OS according to claim 1 is characterized in that: The steps for obtaining the homomorphic operation log are specifically as follows: Continuously track the query and analysis activities of encrypted files, record the operation type, time and participant information, and obtain file operation information; Based on the file operation information, the collected data is processed using the formula: Get the homomorphic operation log, where is the score of the operation log, Indicates the operation time. Indicates the operation type. Indicates the result of an operation.

8. The distributed file encryption system based on domestic OS according to claim 1 is characterized in that: The steps for obtaining the performance efficiency optimization result are specifically as follows: According to the homomorphic operation log, collect data related to the performance bottleneck, including operation duration and resource usage, and generate preliminary performance analysis results based on the collected data; Based on the preliminary performance analysis results, distributed GPU acceleration technology is applied to adjust the operating system resource configuration, using the formula: Optimize the calculation process and obtain performance improvement indicators, among which, is performance efficiency, Represents a computing task, Represents the processing time corresponding to the task, Represents the total number of computing tasks; According to the performance improvement index, resource reallocation and configuration optimization are performed to obtain performance efficiency optimization results.

9. The distributed file encryption system based on domestic OS according to claim 4 is characterized in that: The specific steps for processing the low-frequency and sensitive files are: After encrypting low-frequency and sensitive files based on AES-256, the files are sliced, and the slices are divided according to size and stored independently in the distributed storage system. The storage location and encryption level information of the slices are recorded to obtain the slice storage index; Based on the slice storage index, if there is a need to use it, the data fragments are reassembled using the formula: Get the complete file content ,in, Indicates The encrypted data of each slice, Indicates The weight parameter of each slice during the reassembly process, Indicates the total number of slices.

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