A multidimensional encryption method for secure data output

Abstract

This invention discloses a multi-dimensional encryption method for secure data output, comprising: acquiring the data to be encrypted; establishing a quantum-resistant encryption computational model, considering the resource consumption problem that may occur during the encryption process; constructing a side effect impact model; analyzing the resource consumption contribution of the quantum-resistant encryption model; designing a resource consumption contribution mitigation strategy and a model feedback mechanism; optimizing and monitoring the side effect impact model in real time; the optimized model can not only reduce the time and space complexity in the encryption process, but also significantly reduce the computational resource consumption without sacrificing encryption strength, thereby improving encryption efficiency while ensuring data security.

Description

Technical Field

[0001] This invention relates to the field of data security encryption technology, and in particular to a multi-dimensional encryption method for secure data output. Background Technology

[0002] In recent years, with the rapid development of information technology and the increasing demand for data privacy protection, data encryption technology, as an important means of information security, has been widely used in various communication and storage systems. Early encryption methods were mainly symmetric and asymmetric encryption, with symmetric encryption (such as the Data Encryption Standard (DES) and the Advanced Encryption Standard (AES)) being widely used in most applications due to its high processing efficiency and relatively simple computational structure. However, with the increase in data processing scale and security requirements, the limitations of traditional encryption algorithms in dealing with large-scale data and complex computing environments have gradually become apparent. Especially after increasing encryption strength, high time and space complexity are often encountered, limiting their application in high-performance computing, big data environments, and low-resource devices. Furthermore, with the rise of quantum computing technology, the security of classical encryption algorithms is facing unprecedented challenges. At this point, quantum-resistant encryption technology emerged, providing a solution to quantum attacks. However, algorithms in this emerging field still face challenges in computational overhead and performance optimization in practical applications, especially under conditions of high security requirements and limited computing resources, where their performance bottlenecks have not yet been fully overcome.

[0003] While existing quantum-resistant encryption algorithms theoretically possess the ability to resist the threat of quantum computing, they often exhibit inefficiency and excessive resource consumption in practical deployments, especially in scenarios with high data traffic and high-frequency encryption. Specifically, quantum-resistant encryption algorithms typically require long encryption times and significant computational resources, making their performance unsatisfactory in large-scale concurrent data processing and highly dynamic environments. Meanwhile, although some existing encryption algorithms have considered the impact of data volume and encryption strength on performance, they have not effectively optimized their computational paths or reduced the computational burden when handling unstructured and dynamically changing data, resulting in the performance bottleneck in the encryption process not being fundamentally overcome. Therefore, finding a reasonable balance between high encryption strength and computational resource consumption, and providing more efficient encryption methods, has become an urgent problem to be solved in the current encryption field. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the aforementioned existing problems, this invention is proposed. Therefore, this invention provides a multi-dimensional encryption method for secure data output to address the problems raised in the background art.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a multi-dimensional encryption method for secure data output, comprising:

[0007] Obtain the data to be encrypted, consider the resource consumption problem in the encryption process, and establish a quantum-resistant encryption computing model;

[0008] Based on the calculation results of the quantum-resistant encryption computing model, a side effect impact model is established, the resource consumption contribution of the quantum-resistant encryption computing model is analyzed, and a resource consumption contribution mitigation strategy and model feedback mechanism are designed to optimize and monitor the side effect impact model.

[0009] The optimized side effect impact model was applied to the quantum resistance encryption computation model for verification, resulting in an encryption method that balances high security and high encryption efficiency.

[0010] As a preferred embodiment of the multi-dimensional encryption method for secure data output described in this invention, the method includes: acquiring the data to be encrypted, considering the resource consumption issues during the encryption process, and establishing a quantum-resistant encryption computation model, including:

[0011] The data to be encrypted is converted into a string, the frequency of each symbol in the string is counted, and the probability distribution of the frequency of each symbol is calculated based on the total frequency of the symbols and the number of times the current symbol appears. The entropy of the data to be encrypted is obtained using the entropy calculation formula.

[0012] If the frequency of each symbol is uniform during the process of counting the frequency of each symbol, it means that the entropy of the final encrypted data is high; otherwise, if the frequency of each symbol is non-uniform, it means that the entropy of the final encrypted data is low.

[0013] Record the low-entropy encrypted data and back it up as sub-data of the corresponding encrypted data.

[0014] As a preferred embodiment of the multi-dimensional encryption method for secure data output described in this invention, it further includes:

[0015] For high-entropy encrypted data, considering the structural complexity and data redundancy of the high-entropy encrypted data in the encryption process, the relationships between time complexity and structural complexity, space complexity and structural complexity, time complexity and data redundancy, and space complexity and data redundancy are obtained respectively, and a quantum-resistant encryption computation model is established.

[0016] As a preferred embodiment of the multi-dimensional encryption method for secure data output described in this invention, the method includes: establishing a side effect impact model based on the calculation results of the quantum-resistant encryption calculation model, and analyzing the resource consumption contribution of the quantum-resistant encryption calculation model, including:

[0017] This study analyzes the contributions of data size, key length, and encryption strength to encryption time in a quantum-resistant encryption computation model.

[0018] As a preferred embodiment of the multi-dimensional encryption method for secure data output described in this invention, it further includes:

[0019] This study analyzes the contributions of data size, key length, and encryption strength to memory consumption in a quantum-resistant encryption computation model.

[0020] As a preferred embodiment of the multi-dimensional encryption method for data security output described in this invention, the resource consumption contribution mitigation strategy includes:

[0021] According to the side effect model, the corresponding first threshold, second threshold and third threshold are set. The first threshold is the combined threshold of encryption time and memory resource consumption of data size, the second threshold is the combined threshold of encryption time and memory resource consumption of key length, and the third threshold is the combined threshold of encryption time and memory resource consumption of encryption strength.

[0022] The range of values ​​for each threshold is set based on the minimum and maximum contributions of encryption time and memory consumption obtained from the quantum resistance encryption calculation model.

[0023] As a preferred embodiment of the multi-dimensional encryption method for secure data output described in this invention, it further includes:

[0024] When the impact of a side effect on the model exceeds the maximum value of the first threshold range, the data size is divided into blocks; otherwise, when the impact of a side effect on the model is less than the minimum value of the first threshold range, the data size is incremented.

[0025] When the side effects affect the model beyond the range of the second threshold, the key length is adjusted using a balancing factor.

[0026] When the side effect impact on the model is less than the minimum value of the third threshold range, the current encryption strength is gradually increased.

[0027] As a preferred embodiment of the multi-dimensional encryption method for data security output described in this invention, the model feedback mechanism includes:

[0028] Using the first threshold, the second threshold, and the third threshold as the comprehensive threshold, calculate the difference between the comprehensive threshold and the comprehensive threshold of each parameter after the resource consumption contribution mitigation strategy is adjusted, and update the current comprehensive threshold with the maximum threshold as the target.

[0029] As a preferred embodiment of the multi-dimensional encryption method for secure data output described in this invention, the optimized side-effect model is applied to the quantum-resistant encryption computation model for verification, including:

[0030] The verification method uses the improvement rate of encryption efficiency before and after encryption time as the comparison result.

[0031] Compared with existing technologies, the beneficial effects of the invention are as follows:

[0032] 1. This invention intelligently adjusts the data size, encryption strength, and key length by utilizing the entropy, structural complexity, and redundancy characteristics of the data, thereby effectively reducing the computation time and space resources required during the encryption process. Compared with traditional encryption algorithms, it can maintain high-efficiency encryption performance when processing large-scale data, and is particularly suitable for application scenarios with high frequency and large data volume.

[0033] 2. To address the needs of large-scale data encryption and high-frequency encryption, this invention optimizes the resource consumption problem of traditional encryption algorithms. When processing large amounts of data, this invention can dynamically adjust the encryption strategy according to the characteristics of the data, thereby avoiding the excessive resource consumption of traditional encryption algorithms when processing large-scale data, and enhancing the processing capacity and stability of the system. At the same time, by aiming to balance encryption strength and computational resource consumption, the optimized quantum-resistant encryption is more practical in resource-constrained scenarios. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0035] Figure 1 This is a flowchart illustrating the overall process of a multi-dimensional encryption method for secure data output according to an embodiment of the present invention.

[0036] Figure 2 This is a graph showing the relationship between encryption time and data size for a multi-dimensional encryption method for secure data output according to an embodiment of the present invention.

[0037] Figure 3 This is a graph showing the relationship between memory consumption and data size for a multi-dimensional encryption method for secure data output according to an embodiment of the present invention. Detailed Implementation

[0038] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0039] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0040] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0041] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0042] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for 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. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0043] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0044] Example 1

[0045] Reference Figure 1 This is the first embodiment of the present invention, which provides a multi-dimensional encryption method for secure data output, including:

[0046] S1. Obtain the data to be encrypted, consider the resource consumption problem in the encryption process, and establish a quantum-resistant encryption computing model;

[0047] Specifically, the process involves acquiring the data that needs to be encrypted, which can be text, images, or videos, and converting it into a string format.

[0048] It's important to explain that since the text data to be encrypted is itself a string, its corresponding string form can be obtained using common encoding formats (such as UTF-8, ASCII, Base64, etc.). For image data to be encrypted, the RGB or grayscale values ​​of each pixel in the image can be extracted and converted into a string. For example, for a 2×2 image, assuming its RGB values ​​are: [(255,0,0),(0,255,0),(0,0,255),(255,255,0)], the image can be converted into the string: "255,0,0;0,255,0;0,0,255;255,255,0". Alternatively, image data can be processed in the same way as text data, using encoding formats, but this is typically used for embedding images on the web. For video data to be encrypted, since video data consists of a series of image frames (image sequences), the video must first be decomposed into image frames, and each frame must be processed using an encoding format before it can be converted into the corresponding string.

[0049] Furthermore, after obtaining the string form of the data to be encrypted, the frequency of each symbol in the string is counted, and the probability distribution of the frequency of each symbol is calculated based on the total frequency of the symbols and the number of times the current symbol appears. Using the entropy calculation formula, the entropy of the data to be encrypted is obtained.

[0050] It should be noted that when designing and optimizing encryption algorithms, it is very important to consider the entropy of the encrypted data, especially when dealing with encryption tasks involving big data or complex data structures, because data entropy reflects the randomness and uncertainty of the data, which has a direct impact on the computational efficiency and security of the encryption process.

[0051] Specifically, taking the string S = "D7A9F1E6B2A5E3C4D8F1C2D9" as an example, the frequency of each character is counted as follows: D: 3 times, 7: 1 time, A: 2 times, 9: 2 times, F: 2 times, 1: 2 times, E: 2 times, 6: 1 time, B: 1 time, 2: 2 times, 5: 1 time, 3: 1 time, C: 2 times, 4: 1 time, 8: 1 time. Based on the total frequency of the symbols and the number of times the current symbol appears, the probability distribution of the frequency of each symbol is calculated (rounded to three decimal places). And so on, until the calculation is completed. So far, the probability distribution of each symbol frequency is output, and then the entropy value of the entire encrypted data is obtained by using the entropy value calculation formula;

[0052] Specifically, the entropy value is calculated using the following formula:

[0053]

[0054] Where H(D) is the entropy value of the encrypted data, m is the total frequency of the symbol, and p i This represents the probability distribution value of the selected character in string S;

[0055] Furthermore, if the frequency of each symbol is uniform during the process of counting the frequency of each symbol, it indicates that the entropy of the final encrypted data is high; otherwise, if the frequency of each symbol is non-uniform, it indicates that the entropy of the final encrypted data is low.

[0056] Specifically, a uniform frequency of symbol occurrence means that the frequency of each symbol is close to equal, i.e., the frequency of occurrence is close to 10%; while a non-uniform frequency of symbol occurrence means that the frequency of each symbol is different, with a large deviation.

[0057] Furthermore, the low-entropy encrypted data is recorded and backed up as sub-data of the corresponding encrypted data;

[0058] It should be noted that high-entropy encrypted data usually lacks compressible redundant information, resulting in the need for more memory and computation time during encryption. Low-entropy encrypted data, on the other hand, is easily compressed, thus reducing the computational burden during encryption. Therefore, it is necessary not only to distinguish between high-entropy and low-entropy encrypted data, but also to store low-entropy encrypted data and mark its corresponding original encrypted data. For example, in the case of backup storage, duplicate parts in low-entropy data can be deduplicated or compressed, making it easy to restore when needed. This not only saves system storage space, but also avoids the repeated encryption of redundant data during restoration, thereby improving encryption efficiency.

[0059] Furthermore, for high-entropy encrypted data, considering the structural complexity and data redundancy of high-entropy encrypted data in the encryption process, the relationships between time complexity and structural complexity, space complexity and structural complexity, time complexity and data redundancy, and space complexity and data redundancy are obtained respectively, and a quantum-resistant encryption computing model is established.

[0060] It should be explained that during the encryption process, data with high structural complexity requires more computational steps to process. For example, for data with complex structures such as images or videos, the encryption algorithm may need additional steps such as block processing, image transformation, and distributed computing, which increases the amount of computation in the encryption process.

[0061] Specifically, the relationship between time complexity and structural complexity is expressed as follows:

[0062] T(n,k,S(D))=n·k α ·f(S(D))

[0063] Where T is the time complexity of the encryption process, n is the data size, k is the key length, α is the encryption strength, S(D) represents the structural complexity of the high-entropy encrypted data, and f(S(D)) is the function of the influence of structural complexity on time complexity. When the structural complexity is high, f(S(D)) will show a large increase, which means that more time is needed for encryption.

[0064] It should be explained that space complexity describes the storage space required during the encryption process. The structural complexity of the data has a direct impact on space complexity, especially when multi-level data storage and large data block processing are involved. Data with high structural complexity (such as multidimensional arrays, image data, or complex databases) usually requires more memory to store its structural information and the encrypted data. For example, when encrypting highly complex image data, more advanced memory management is required to handle its multidimensional characteristics and the relationships between different data blocks, leading to increased memory consumption.

[0065] Specifically, the relationship between space complexity and structural complexity is expressed as follows:

[0066] S(n,k,S(D))=n·k α ·g(S(D))

[0067] Where S is the space complexity of the encryption process, and g(S(D)) is the function of the influence of structural complexity on space complexity. When the structural complexity is high, the memory requirement increases, which causes g(S(D)) to increase with the increase of structural complexity.

[0068] It should be explained that data redundancy describes the degree of repetition and redundancy of information in data. In the encryption process, data with low redundancy requires more computation time to encrypt because its unpredictable nature makes the encryption algorithm need more iterations and hash calculations to ensure the security of the encryption.

[0069] Specifically, the relationship between time complexity and data redundancy is expressed as follows:

[0070] T(n,k,R(D))=n·k α ·h(R(D))

[0071] Here, h(R(D)) is the function that affects the time complexity of data redundancy. If the data redundancy is low, h(R(D)) will be large, which means that more time is needed in the encryption process; if the data redundancy is high, h(R(D)) will be small, which means that the encryption process is more efficient.

[0072] It should be explained that space complexity is also affected by data redundancy. Data with high redundancy requires more space to store intermediate results during encryption, especially when temporary caches or intermediate data structures are used in the encryption process.

[0073] Specifically, the relationship between space complexity and data redundancy is expressed as follows:

[0074] S(n,k,R(D))=n·k α ·r(R(D))

[0075] Here, r(R(D)) is the function that affects the space complexity of data redundancy. When the data redundancy is high, the data can be effectively compressed, resulting in a smaller encrypted data size. In this case, r(R(D)) will decrease as the redundancy increases, thereby reducing the space complexity. When the data redundancy is low, since there is no obvious redundant part in the data, its storage requirements are usually high. Because the encrypted data cannot be effectively compressed or deduplicated, the space consumption is large. In this case, r(R(D)) will show a gradual upward trend.

[0076] It should be noted that, based on the above four relationships, we can conclude that the lower the structural complexity in terms of space and time complexity, the better, and the higher the data redundancy in terms of space and time complexity, the better.

[0077] Specifically, the quantum-resistant cryptographic computation model can be represented as:

[0078] T total (n,k,S(D),R(D))=n·k α ·f(S(D))·h(R(D))

[0079] S total (n,k,S(D),R(D))=n·k α ·g(S(D))·r(R(D))

[0080] Among them, T total S represents the total time complexity required for the entire encryption process. total This represents the total space complexity required for the entire encryption process;

[0081] S2. Based on the calculation results of the quantum-resistant encryption computing model, establish a side effect impact model, analyze the resource consumption contribution of the quantum-resistant encryption computing model, and design resource consumption contribution mitigation strategies and model feedback mechanisms to optimize and monitor the side effect impact model.

[0082] Furthermore, the contribution of data size, key length, and encryption strength to encryption time in the quantum-resistant encryption computation model are analyzed.

[0083] It's important to explain that the data size *n* directly affects the computational complexity of encryption. For symmetric encryption, the computational complexity is typically linearly related to the data size, expressed as O(n); while for asymmetric encryption, the computational complexity is typically O(n·k). α Furthermore, it is subject to wireless and nonlinear limitations.

[0084] Specifically, the contribution of data size to encryption time is expressed as follows:

[0085] T time (n,k,α)=O(n·k α ·j(n))

[0086] Among them, T time For encryption time, j(n) is the function of the influence of data size n on computational complexity, which can be expressed as a linear relationship or a relationship without linear and nonlinear constraints;

[0087] It should be explained that the key length k usually affects the computational strength in the encryption process. The longer the key length, the more exponentially or polynomially the computational complexity of the encryption algorithm will increase, resulting in an increase in encryption time.

[0088] Specifically, the contribution of key length to encryption time is expressed as follows:

[0089] T time (n,k)=O(n·k α ·j(k))

[0090] Where j(k) represents the effect function of key length k on computational complexity;

[0091] It should be explained that encryption strength (such as the number of iterations or encryption rounds) directly affects the computational complexity of the encryption algorithm. Increasing the encryption strength usually requires more computing resources, which is reflected in an increase in the algorithm's computational complexity O(·).

[0092] Specifically, the contribution of encryption strength to encryption time is expressed as follows:

[0093] T time (n,k,α)=O(n·k α ·j(α))

[0094] Where j(α) is the function that affects the computational complexity by increasing the encryption strength;

[0095] Furthermore, the contribution of data size, key length, and encryption strength to memory consumption in the quantum-resistant encryption computation model are analyzed.

[0096] It should be explained that the data size n directly affects the memory usage during the encryption process, especially when using block encryption or parallel encryption. When the data volume is large, more memory is needed to store intermediate results and cache.

[0097] Specifically, the contribution of data size to memory consumption is expressed as follows:

[0098] S space (n,k,α)=O(n·k α ·l(n))

[0099] Among them, S space Let l(n) be the memory consumption, and l(n) be the function that affects the data size n on the memory consumption. Generally, the memory consumption increases linearly with the data size n.

[0100] It should be explained that the key length k affects the cache required for key storage and computation during the encryption process;

[0101] Specifically, the contribution of key length to memory consumption is expressed as follows:

[0102] S space (n,k)=O(n·k α ·l(k))

[0103] Where l(k) is the function of the influence of key length k on memory consumption. Generally, the longer the key length, the higher the growth rate of memory required.

[0104] It should be explained that encryption strength affects memory usage, because high-strength encryption usually leads to more intermediate data storage and temporary caching requirements, especially in distributed computing or parallel encryption scenarios.

[0105] Specifically, the contribution of encryption strength to memory consumption is expressed as follows:

[0106] S space (n,k,α)=O(n·k α ·l(α))

[0107] Where l(α) is the function that affects the encryption strength on memory consumption. Typically, as the encryption strength increases, memory consumption increases exponentially.

[0108] Specifically, based on the above contribution formula, the side effect impact model is established as follows:

[0109] T t_time (n,k,α)=O(n·K α ·j(n,k,α))

[0110] S t_space (n,k,α)=O(n·k α ·l(n,k,α))

[0111] Among them, T t_time S represents the total encryption time. t_space This is expressed as total memory consumption;

[0112] It should be noted that the influence function in the above formula can be set according to the actual amount of data processed by the system. Its form is a combination of polynomials other than the explicit parameters considered in the present invention.

[0113] Furthermore, based on the contribution considered in the side effect model, corresponding first threshold, second threshold, and third threshold are set;

[0114] Specifically, in the side effect impact model of the present invention, three parameters are considered: data size, key length, and encryption strength, which can be added or removed according to the actual situation.

[0115] Specifically, the first threshold is the combined threshold of encryption time and memory resource consumption for data size, the second threshold is the combined threshold of encryption time and memory resource consumption for key length, and the third threshold is the combined threshold of encryption time and memory resource consumption for encryption strength.

[0116] Specifically, the combined threshold of encryption time and memory resource consumption is expressed as addition or multiplication. For example, the combined threshold of encryption time and memory resource consumption for key length is expressed as: Combined Threshold = S space (n,k)·T time (n,k) or combined threshold = S space (n,k)+T time (n,k); It should be noted that addition is used only when encryption time and memory resource consumption are independent of each other and their impact on the total consumption is linearly additive; multiplication is used only when encryption time and memory resource consumption have a synergistic effect (they are interdependent and grow synergistically), and in resource-constrained or high-concurrency scenarios.

[0117] The range of values ​​for each threshold is set based on the minimum and maximum contributions of encryption time and memory consumption obtained from the quantum resistance encryption calculation model.

[0118] Specifically, minimum contribution and maximum contribution refer to the minimum and maximum values ​​of the results obtained by combining thresholds. The range of threshold values ​​is obtained based on these minimum and maximum values. The larger the contribution, the greater the impact of the parameters under the combined threshold on the consumption in the encryption process. Conversely, the smaller the contribution, the smaller the impact of the parameters under the combined threshold on the consumption in the encryption process.

[0119] Furthermore, taking the three parameter combinations and thresholds considered in the side effect impact model of the present invention as examples, the three threshold rules for data size, key length, and encryption strength are obtained respectively, and are used to adjust the quantum resistance encryption calculation model. The threshold rules are as follows:

[0120] Rule 1: When the impact of a side effect on the model is greater than the maximum value of the first threshold range, the data size is divided into blocks; otherwise, when the impact of a side effect on the model is less than the minimum value of the first threshold range, the data size is incremented.

[0121] Specifically, the range setting in Rule 1 is explained as follows: If too much time or memory is consumed during the encryption process, block operations are adopted to reduce the computational burden of a single encryption task; if the time or memory consumed during the encryption process is small, the amount of data is increased incrementally to improve encryption efficiency.

[0122] Rule 2: When the side effects affect the model beyond the range of the second threshold, the key length is adjusted using a balancing factor.

[0123] Specifically, longer keys usually mean higher encryption strength, but also more computational cost and memory usage. At the same time, shorter keys mean lower encryption strength, and computational cost and memory usage are reduced. In this case, the security of encryption will face a severe test, so a balance factor is needed to balance the key length.

[0124] Rule 3: When the impact of side effects on the model is less than the minimum value of the third threshold range, the current encryption strength is increased gradually.

[0125] Specifically, in addition to the key length balance in rule 2, the anti-attack resistance of encryption strength also needs to be considered. When the computational consumption and memory usage decrease, the encryption strength will also decrease. Therefore, it is necessary to gradually increase the encryption strength, that is, the key length is proportional to the encryption strength, so as to ensure that the encryption process is secure while the resource consumption is relatively even.

[0126] It should be explained that since the above parameters (data size, key length, and encryption strength) are dynamically updated, the range of values ​​for the updated thresholds also needs to be changed accordingly to ensure that the subsequent side effects affect the effectiveness of the model applied to the quantum-resistant encryption computing model.

[0127] Furthermore, using the first threshold, the second threshold, and the third threshold as a comprehensive threshold, the difference between the comprehensive threshold and the comprehensive threshold of each parameter after the adjustment of the resource consumption contribution mitigation strategy is calculated, and the current comprehensive threshold is updated with the maximum threshold as the target.

[0128] Specifically, targeting the maximum threshold means updating the current comprehensive threshold based on the difference between the comprehensive threshold and the comprehensive thresholds of each parameter (data size, key length, and encryption strength) after the resource consumption contribution mitigation strategy adjustment. For example, if the difference between the comprehensive threshold and the comprehensive thresholds of each parameter (data size, key length, and encryption strength) after the resource consumption contribution mitigation strategy adjustment is negative, it indicates that the comprehensive threshold is small, while the comprehensive thresholds of each parameter (data size, key length, and encryption strength) after the resource consumption contribution mitigation strategy adjustment are large, and the larger comprehensive threshold is used to update the current comprehensive threshold. Conversely, if the difference is positive, it indicates that the comprehensive threshold is large, while the comprehensive thresholds of each parameter (data size, key length, and encryption strength) after the resource consumption contribution mitigation strategy adjustment are small. If the difference is 0, the current comprehensive threshold is not updated.

[0129] S3. The optimized side effect model is applied to the quantum resistance encryption calculation model for verification, resulting in an encryption method that balances high security and high encryption efficiency.

[0130] Furthermore, the verification method uses the ratio of the improvement in encryption efficiency before and after encryption time as the comparison result, that is, comparing the impact of side effects on the encryption time of the model before optimization with the impact of side effects on the encryption time of the model after optimization, to obtain the ratio of the improvement in encryption efficiency.

[0131] Specifically, its mathematical formula can be expressed as:

[0132]

[0133] Where η is the percentage increase in encryption efficiency, and T afger T represents the impact of optimized side effects on the encryption time of the model. before This represents the impact of side effects on the encryption time of the model before optimization.

[0134] Specifically, when the improvement rate of encryption efficiency is greater than 0, it means that the optimized side effect model is more effective in the quantum-resistant encryption computing model; when the improvement rate of encryption efficiency is less than 0, it means that the optimized side effect model is less effective in the quantum-resistant encryption computing model; when the improvement rate of encryption efficiency is equal to 0, it means that the optimized side effect model has no effect in the quantum-resistant encryption computing model.

[0135] It should be noted that when a value less than or equal to 0 occurs, the side effect impact model should be re-established and the current side effect impact model should be discarded. Only the initial quantum resistance encryption calculation model should be used for data encryption.

[0136] Example 2

[0137] Reference Figure 2 and Figure 3 This is the second embodiment of the present invention, which provides a multi-dimensional encryption method for secure data output, including: verifying the beneficial effects of the present invention through simulation experiments;

[0138] The experimental environment and test platform were deployed on a high-performance computing cluster. The hardware configuration included an Intel Xeon Gold 6348 processor (2.6GHz, 28 cores), 512GB DDR4 memory, and NVMe SSD storage (read / write speed 7GB / s). The operating system was Ubuntu 22.04LTS. The software environment included Python 3.10 and OpenSSL 3.0.8 (supporting quantum resistance algorithm libraries). The system in this invention was named QR-MEv1.0. The comparison group used two mainstream quantum resistance encryption algorithms: NTRU-768 (a traditional lattice-based algorithm) and McEliece-6960 (an error-correcting code-based algorithm).

[0139] The test dataset contains three types of data: low-entropy data: repetitive text files (1GB, 5GB, 10GB), the content of which is a random repetitive sequence of characters converted into string form (such as "A1e4FhCd…"); high-entropy data: uncompressed medical images (DICOM format, 1GB, 5GB, 10GB), with highly random pixel distribution; and mixed data: a mixed text and image file (5GB), simulating a real-world business scenario.

[0140] The experimental steps are as follows:

[0141] Text data is converted to UTF-8 strings, and pixel RGB values ​​of image data are extracted and serialized into strings. Symbol frequencies are statistically analyzed and entropy values ​​are calculated: the entropy value of low-entropy text is 2.1–2.5 bits / symbol (nearly uniform distribution), and the entropy value of high-entropy images is 7.8–8.0 bits / symbol. Based on the entropy value, low-entropy data is labeled as "compressible," and high-entropy data is labeled as "high complexity."

[0142] For high-entropy data larger than 5GB, encryption is performed in 512MB blocks; low-entropy data is incrementally compressed (LZ77 algorithm) and then encrypted as a whole; the initial key is set to 256 bits, and when memory consumption exceeds the threshold (1GB), the key is adjusted to 224 bits by a balance factor β = 0.8, and the encryption strength α is increased from 12 rounds to 14 rounds to maintain security; encryption time and memory usage are collected by Prometheus, and if resource limits are exceeded three times consecutively (time > 120s or memory > 1.5GB), the model feedback mechanism is triggered to recalibrate the threshold;

[0143] The performance metrics are recorded in Table 1, where: encryption time: the total time from data loading to encryption completion; memory consumption: peak memory usage (MB); encryption efficiency improvement percentage: the percentage difference in encryption time before and after optimization.

[0144] Table 1 Comparison of Test Data

[0145]

[0146] Table 1 shows that in the 10GB high-entropy data test, QR-MEv1.0's encryption time was 89 seconds, which is 58.6% and 77.6% faster than NTRU-768 (215 seconds) and McEliece-6960 (398 seconds), respectively (reference). Figure 2 Furthermore, QR-ME v1.0 consumes only 920MB of memory, a 62.4% reduction compared to NTRU-768 and a 70.3% reduction compared to McEliece-6960 (reference). Figure 3 The core advantages lie in the dynamic block partitioning strategy and key length optimization: as the data volume increases, block encryption reduces the computational load per operation, while the key length is dynamically adjusted from a fixed 768 / 6960 bits to 224-256 bits, reducing computational complexity; secondly, in a 5GB mixed data test, QR-MEv1.0 consumed only 480MB of memory, which is only 40% of NTRU-768 (1200MB) and 32% of McEliece-6960 (1500MB), meaning that when memory usage... When the threshold is approached, the system automatically compresses low-entropy data and releases redundant cache. In addition, traditional algorithms use long keys (such as 6960 bits in McEliece-6960) in pursuit of security, which leads to a serious imbalance between resource consumption and encryption efficiency. QR-MEv1.0, on the other hand, dynamically adjusts its strategy to control the key length between 224 and 256 bits while ensuring resistance to quantum attacks (security equivalent to NTRU-768 when α≥14 rounds), thus achieving dual optimization of encryption security and efficiency.

[0147] In summary, by employing dynamic block segmentation, key length optimization, and compression strategies, encryption time and memory consumption are significantly reduced, making it suitable for high-frequency, large-data-volume application scenarios. Compared with traditional encryption algorithms, this invention significantly improves encryption efficiency and resource utilization while ensuring high security.

[0148] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0149] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0150] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0151] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0152] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0153] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A multi-dimensional encryption method for secure data output, characterized in that, include: Obtain the data to be encrypted, consider the resource consumption problem in the encryption process, and establish a quantum-resistant encryption computing model; Based on the calculation results of the quantum-resistant encryption computing model, a side effect impact model is established, the resource consumption contribution of the quantum-resistant encryption computing model is analyzed, and a resource consumption contribution mitigation strategy and model feedback mechanism are designed to optimize and monitor the side effect impact model. The resource consumption contribution mitigation strategies include: According to the side effect model, the corresponding first threshold, second threshold and third threshold are set. The first threshold is the combined threshold of encryption time and memory resource consumption of data size, the second threshold is the combined threshold of encryption time and memory resource consumption of key length, and the third threshold is the combined threshold of encryption time and memory resource consumption of encryption strength. The range of values ​​for each threshold is set based on the minimum and maximum contributions of encryption time and memory consumption obtained from the quantum resistance encryption calculation model. The optimized side effect impact model was applied to the quantum resistance encryption computation model for verification, resulting in an encryption method that balances high security and high encryption efficiency.

2. The multi-dimensional encryption method for secure data output as described in claim 1, characterized in that, To obtain the data to be encrypted, considering the resource consumption issues during the encryption process, a quantum-resistant encryption computation model is established, including: The data to be encrypted is converted into a string, the frequency of each symbol in the string is counted, and the probability distribution of the frequency of each symbol is calculated based on the total frequency of the symbols and the number of times the current symbol appears. The entropy of the data to be encrypted is obtained using the entropy calculation formula. If the frequency of each symbol is uniform during the process of counting the frequency of each symbol, it means that the entropy of the final encrypted data is high; otherwise, if the frequency of each symbol is non-uniform, it means that the entropy of the final encrypted data is low. Record the low-entropy encrypted data and back it up as sub-data of the corresponding encrypted data.

3. The multi-dimensional encryption method for secure data output as described in claim 2, characterized in that, Also includes: For high-entropy encrypted data, considering the structural complexity and data redundancy of the high-entropy encrypted data in the encryption process, the relationships between time complexity and structural complexity, space complexity and structural complexity, time complexity and data redundancy, and space complexity and data redundancy are obtained respectively, and a quantum-resistant encryption computation model is established.

4. The multi-dimensional encryption method for secure data output as described in claim 3, characterized in that, Based on the calculation results of the quantum-resistant encryption computing model, a side effect impact model is established to analyze the resource consumption contribution of the quantum-resistant encryption computing model, including: This study analyzes the contributions of data size, key length, and encryption strength to encryption time in a quantum-resistant encryption computation model.

5. The multi-dimensional encryption method for secure data output as described in claim 4, characterized in that, Also includes: This study analyzes the contributions of data size, key length, and encryption strength to memory consumption in a quantum-resistant encryption computation model.

6. The multi-dimensional encryption method for secure data output as described in claim 1, characterized in that, Also includes: When the impact of a side effect on the model exceeds the maximum value of the first threshold range, the data size is divided into blocks; otherwise, when the impact of a side effect on the model is less than the minimum value of the first threshold range, the data size is incremented. When the side effects affect the model beyond the range of the second threshold, the key length is adjusted using a balancing factor. When the side effect impact on the model is less than the minimum value of the third threshold range, the current encryption strength is gradually increased.

7. The multi-dimensional encryption method for secure data output as described in claim 1, characterized in that, Model feedback mechanisms include: Using the first threshold, the second threshold, and the third threshold as the comprehensive threshold, calculate the difference between the comprehensive threshold and the comprehensive threshold of each parameter after the resource consumption contribution mitigation strategy is adjusted, and update the current comprehensive threshold with the maximum threshold as the target.

8. The multi-dimensional encryption method for secure data output as described in claim 1, characterized in that, The optimized side-effect model was applied to the quantum-resistant cryptographic computation model for verification, including: The verification method uses the improvement rate of encryption efficiency before and after encryption time as the comparison result.

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