Data protection methods based on genetic algorithms

Abstract

This invention discloses a data protection method based on a genetic algorithm. The method includes predefinition, uniform sampling set initialization, global non-dominated sorting, crossover operation design, mutation operation design, iterative strategy, and data protection. This invention belongs to the field of data protection, specifically referring to a data protection method based on a genetic algorithm. This scheme introduces a uniform sampling set initialization strategy to perform unbiased coverage of the entire feasible space; it introduces an adaptive pairing mechanism based on a small-world network for mate selection, avoiding the inefficiency caused by excessive randomness in later stages, thereby improving the data protection effect; it introduces a dynamic retention probability and perturbation factor to design crossover operations, adaptively retaining excellent data protection strategies and providing a smooth transition of exploration-utilization tradeoffs at different iteration stages; it introduces polynomial mutation and local perturbation design for mutation operations to avoid local optima, thus making the data protection strategy more refined and accurate, and improving data protection security.

Description

Technical Field

[0001] This invention relates to the field of data protection technology, specifically to a data protection method based on genetic algorithms. Background Technology

[0002] Data protection methods refer to a series of technical means employed to ensure the confidentiality, integrity, and availability of data, prevent unauthorized access, tampering, or loss of data, and ensure reliable recovery when needed. However, general data protection methods suffer from problems such as unbalanced initial data protection strategies, severe undersampling of some strategy spaces, easy omission of potential high-quality solutions, and limited neighborhood search and information exchange, leading to poor data protection effectiveness. Furthermore, general data protection methods lack effective guidance, resulting in the loss of excellent data protection strategies, and insufficient search accuracy for data protection strategies, leading to low data security. Summary of the Invention

[0003] To address the aforementioned issues and overcome the shortcomings of existing technologies, this invention provides a data protection method based on genetic algorithms. Addressing the problems of unbalanced initial data protection strategies, severe undersampling of some strategy spaces, easy omission of potential high-quality solutions, and limited neighborhood search and information exchange in general data protection methods, leading to poor data protection effectiveness, this scheme introduces a uniform sampling set initialization strategy to provide unbiased coverage of the entire feasible space, offering diverse inspirations for subsequent data protection strategy searches. It also introduces an adaptive pairing mechanism based on small-world networks for mate selection, maintaining high randomness in the early stages and strong local stability in the later stages, avoiding inefficient search due to excessive randomness in the later stages, while maintaining necessary neighborhood reshaping capabilities, thereby improving data protection effectiveness. Furthermore, addressing the problems of lack of effective guidance leading to the loss of excellent data protection strategies and insufficient search accuracy in general data protection methods, resulting in low data protection security, this scheme introduces a dynamic retention probability and perturbation factor design cross-operation to adaptively retain excellent data protection strategies, providing a smooth transition of exploration-utilization trade-offs at different iteration stages. Finally, it introduces polynomial mutation and local perturbation design mutation operations to avoid local optima, thereby making the data protection strategy more refined and accurate, and improving data protection security.

[0004] The technical solution adopted by this invention is as follows: The data protection method based on genetic algorithm provided by this invention includes the following steps:

[0005] Step S1: Predefined;

[0006] Step S2: Initialize the uniform sampling set;

[0007] Step S3: Global non-dominated sort;

[0008] Step S4: Cross-operation design;

[0009] Step S5: Design of mutation operations;

[0010] Step S6: Iteration strategy;

[0011] Step S7: Data Protection.

[0012] Further, in step S1, the predefinition involves acquiring the data to be protected. Let each data protection decision-making individual x represent a data protection strategy, including encryption level, redundancy backup ratio, storage node distribution, access control granularity, and protection parameter settings. A parameter optimization space is established based on the encryption level, redundancy backup ratio, storage node distribution, access control granularity, and protection parameter settings. An optimization objective is defined. Represented as: ;in, It is the data loss rate; It is a consumption of resources; This is the access delay time.

[0013] Further, in step S2, the uniform sampling set initialization is performed by generating a uniform sampling set to initialize the data protection strategy; the uniform sampling set generation is expressed as: The data protection policy initialization is represented as follows: ;in, It is the initial good point value of the i-th individual in the j-th dimension; i is the individual index, and j is the dimension index of the parameter optimization space; It is the initial value of the j-th dimension for the i-th data protection decision-making individual; and These are the minimum and maximum values ​​of the j-th dimension of the parameter optimization space, respectively.

[0014] Further, in step S3, the global non-dominated sorting is used to sort all data protection strategies; a small-world network is constructed, defining the number of nodes N, where each data protection decision-maker corresponds to a node in the network, and the total number of nodes is equal to the size of the data protection decision-making population; the degree K is defined, where each node in the initial ring graph is connected to its left and right neighbors K / 2, for a total of K edges; and the adaptive reconnection probability p is defined as: Where gen is the current iteration number; maxgen is the maximum iteration number; γ is the basic reconnection probability; γ is the exponential control factor; for each edge on the ring, after being disconnected with probability p, it is randomly connected to any other node in the network, resulting in a small-world network; for the i-th data protection decision-maker... Extract its neighbor set in the small-world network, perform a non-dominated sort on the neighbor set, and select the individual with the highest sorted value as the spouse of the data protection decision individual. ; Define the dominance relationship. If , and there is at least one u such that ; then is better than .

[0015] Further, in step S4, the crossover operation design preferentially selects the parents with a higher ranking in the global non-dominated sorting, and obtains a new generation of data protection strategy individuals through the crossover operation; in the early stage of gen < maxgen / 3, linear crossover is used. The linear crossover is expressed as: ; ; where and are the values of the j-th dimension of two new offspring data protection strategy individuals generated by the i-th data protection decision individual through the crossover operation in the previous stage; is the value of the j-th dimension; is the value of the j-th dimension; in the later stage, optimal crossover is used. The optimal crossover is expressed as: ; ; where and are the values of the j-th dimension of two new offspring data protection strategy individuals generated by the i-th data protection decision individual through the crossover operation in the later stage; is the value of the j-th dimension of a random data protection strategy individual; is the value of the j-th dimension of the data protection strategy individual with the highest ranking in the non-dominated sorting; is the perturbation factor; ; Introduce the dynamic retention probability , which is expressed as: ; where is the crossover distribution index; c is a value randomly selected from a uniform distribution; each pair of parents generates two offspring, calculates the non-dominated ranks of the offspring respectively. If , only retain the better offspring; otherwise retain both; rand is a random number between 0 and 1.

[0016] Further, in step S5, the mutation operation design uses the Lévy mutation step size in the first half of gen < maxgen / 2. The step size is defined as: ; The update is expressed as: ; where RL is a random number subject to the Lévy distribution; is the value of the j-th dimension of the data protection strategy individual obtained by the mutation operation in the first half; is the random perturbation factor of the uniform distribution; in the second half, polynomial mutation is used for fine adjustment. The update is expressed as: ; ;in, It is the value of the j-th dimension of the data protection strategy individual obtained from the second half of the mutation operation; It is a polynomial variation value; It is the multinomial distribution index; It is a random number between 0 and 1, independent of rand; for each mutated individual, a dimension is randomly selected for local perturbation, using the following formula: ; ;in, and It is the result of local perturbation; It is a local perturbation factor. .

[0017] Furthermore, in step S6, the iterative strategy is to select the next generation of data protection strategy individuals of the parent and child generations according to the non-dominated sorting in each generation, keep the population size unchanged, and repeat the crossover and mutation operations until the maximum number of iterations or the optimization target converges.

[0018] Furthermore, in step S7, the data protection is the optimal data protection strategy individual after the output iteration ends, and the data protection configuration is obtained based on the individual location to finally realize data protection.

[0019] The beneficial effects achieved by the present invention using the above solution are as follows:

[0020] (1) To address the problems of uneven initial data protection strategies, severe undersampling in some strategy spaces, easy omission of potential high-quality solutions, and limited neighborhood search and information exchange in general data protection methods, which lead to poor data protection performance, this scheme introduces a uniform sampling set initialization strategy to perform unbiased coverage of the entire feasible space, providing rich and diverse inspirations for subsequent data protection strategy search; it also introduces an adaptive pairing mechanism based on small-world networks for mate selection, maintaining high randomness in the early stage and strong local stability in the later stage, avoiding low search efficiency caused by excessive randomness in the later stage, while maintaining the necessary neighborhood reshaping capability; thereby improving the data protection effect.

[0021] (2) In view of the problem that general data protection methods lack effective guidance, resulting in the loss of excellent data protection strategies and insufficient search accuracy for data protection strategies, which leads to low data protection security, this scheme introduces dynamic retention probability and perturbation factor design cross operation to adaptively retain excellent data protection strategies and provide a smooth transition of exploration-utilization trade-off at different iteration stages; introduces polynomial mutation and local perturbation design mutation operation to avoid local optima, thereby making the data protection strategy more refined and accurate and improving data protection security. Attached Figure Description

[0022] Figure 1This is a flowchart illustrating the data protection method based on genetic algorithms provided by the present invention.

[0023] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0025] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the system 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 this invention.

[0026] Example 1, see Figure 1 The present invention provides a data protection method based on genetic algorithms, which includes the following steps:

[0027] Step S1: Predefinition; Obtain the data to be protected and define the data protection decision-making entity;

[0028] Step S2: Initialize the uniform sampling set; initialize the data protection strategy using the uniform sampling set generation.

[0029] Step S3: Global non-dominated sorting; Introduce adaptive reconnection probability and use network topology to implement non-dominated sorting for data protection strategies;

[0030] Step S4: Crossover operation design; Introduce a two-stage crossover strategy of early linear crossover and late-stage preferred crossover;

[0031] Step S5: Mutation operation design; Introduce a local perturbation design for a dual-mode mutation mechanism;

[0032] Step S6: Iterative strategy; reshaping the population based on non-dominated sorting;

[0033] Step S7: Data protection; Implement data protection individually based on the optimal data protection strategy after the iteration ends.

[0034] Example 2, see Figure 1This embodiment is based on the above embodiment. In step S1, it is predefined to obtain the data to be protected. Let each data protection decision individual x represent a data protection strategy, including encryption level, redundancy backup ratio, storage node distribution, access control granularity, and protection parameter settings. A parameter optimization space is established based on the encryption level, redundancy backup ratio, storage node distribution, access control granularity, and protection parameter settings. An optimization target is defined. Represented as: ;in, It is the data loss rate, which measures the degree to which data is lost from its original state under data protection policies; It refers to resource consumption, measuring the computing and storage resources consumed during the data protection process; It is the access latency time, which measures the latency of data access under data protection policies.

[0035] Example 3, see Figure 1 This embodiment is based on the above embodiment. In step S2, the uniform sampling set initialization is used to ensure that the initial data protection strategy combination uniformly covers all possibilities and prevents potential high-quality solutions from being missed in the initial stage. The uniform sampling set generation is expressed as follows: The data protection policy initialization is represented as follows: ;in, It is the initial good point value of the i-th individual in the j-th dimension; i is the individual index, and j is the dimension index of the parameter optimization space; It is the initial value of the j-th dimension for the i-th data protection decision-making individual; and These are the minimum and maximum values ​​of the j-th dimension of the parameter optimization space, respectively.

[0036] Example 4, see Figure 1 This embodiment is based on the above embodiment. In step S3, the global non-dominated sorting is used to sort all data protection strategies; a small-world network is constructed, defining the number of nodes N, where each data protection decision-maker corresponds to a node in the network, and the total number of nodes is equal to the size of the data protection decision-making population; the degree K is defined, where each node in the initial ring graph is connected to its left and right K / 2 neighbors, for a total of K edges; and the adaptive reconnection probability p is defined as: To avoid significant fluctuations in the network structure later, where gen is the current iteration number and maxgen is the maximum iteration number; γ is the basic reconnection probability; γ is the exponential control factor; for each edge on the ring, after being disconnected with probability p, it is randomly connected to any other node in the network, resulting in a small-world network; for the i-th data protection decision-maker... , take out its neighbor set in the small-world network, perform non-dominated sorting on the neighbor set, and select the individual with the highest ranking as the mate of the data protection decision individual. ; Define the dominance relationship. If , and there is at least one u such that ; then is better than ; and are two data protection decision individuals defining the dominance relationship; is for any , all satisfy , corresponding to the optimization objective of step S1.

[0037] By performing the above operations, aiming at the problems existing in the general data protection method, such as unbalanced initial data protection strategies, serious undersampling in some strategy spaces, easy omission of potential high-quality solutions, limited neighborhood search and information exchange, and thus poor data protection effects, this solution introduces a uniform sampling set initialization strategy to perform unbiased coverage in the entire feasible space, providing rich and diverse inspirations for the subsequent search of data protection strategies; introduces an adaptive pairing mechanism based on the small-world network for mate selection, maintaining high randomness in the early stage and strong local stability in the later stage, avoiding low search efficiency caused by excessive randomness in the later stage, and at the same time maintaining the necessary neighborhood reshaping ability; thereby improving the data protection effect.

[0038] Example 5, refer to Figure 1 , based on the above example, in step S4, the crossover operation design uses existing high-quality data protection configurations to guide the new generation of data protection strategy combinations and improve the protection quality; preferentially select the parent with the highest ranking in the global non-dominated sorting, and obtain new generation data protection strategy individuals through the crossover operation; use linear crossover in the early exploration when gen < maxgen / 3, and the linear crossover is expressed as: ; ; where, and are the values of the j-th dimension of two new offspring data protection strategy individuals generated by the i-th data protection decision individual through the crossover operation in the previous stage; is the value of the j-th dimension of ; is the value of the j-th dimension of ; in the later stage, use the preferred crossover, and the preferred crossover is expressed as: ; ; where, and are the values of the j-th dimension of two new offspring data protection strategy individuals generated by the i-th data protection decision individual through the crossover operation in the later stage; is the value of the j-th dimension of the random data protection strategy individual; is the value of the j-th dimension of the data protection strategy individual with the highest ranking in non-dominated sorting; is the perturbation factor; ; Introduce a dynamic retention probability , expressed as: ; where, is the crossover distribution index; c is a value randomly selected from a uniform distribution; two offspring are generated for each pair of parents, and the non-dominated ranks of the offspring are calculated respectively. If , only the better offspring is retained; otherwise both are retained; rand is a random number between 0 and 1.

[0039] Example 6, refer to Figure 1 , this example is based on the above example. In step S5, the mutation operation design is to design a dual-mode mutation mechanism, perform a large random jump in the data protection parameter space, explore data protection strategies that have not been covered, and prevent local convergence; use the Lévy mutation step size in the first half of gen < maxgen / 2, and the step size is defined as: ; The update is expressed as: ; where, RL is a random number obeying the Lévy distribution; is the value of the j-th dimension of the data protection strategy individual obtained by the mutation operation in the first half; is a random perturbation factor of the uniform distribution; in the second half, polynomial mutation is used for fine adjustment, and the update is expressed as: ; ; where, is the value of the j-th dimension of the data protection strategy individual obtained by the mutation operation in the second half; is the polynomial mutation value; is the polynomial distribution index; is a random number between 0 and 1, independent of rand; for each mutated individual, a dimension is randomly selected to perform local perturbation to add random details to the individual, and the formula used is: ; ; where, and are the results after local perturbation; is the local perturbation factor, .

[0040] By performing the above operations, this solution addresses the problems of general data protection methods, such as the lack of effective guidance leading to the loss of excellent data protection strategies, insufficient search accuracy for data protection strategies, and consequently low data protection security. It introduces a dynamic retention probability and perturbation factor design cross-operation to adaptively retain excellent data protection strategies, providing a smooth transition between exploration and utilization trade-offs at different iteration stages. Furthermore, it introduces polynomial mutation and local perturbation design mutation operations to avoid local optima, thereby making the data protection strategy more refined and accurate, and improving data protection security.

[0041] Example 7, see Figure 1 This embodiment is based on the above embodiment. In step S6, the iterative strategy is to select the next generation of data protection strategy individuals of the parent and child generations according to the non-dominated sorting in each generation, keep the population size unchanged, and repeat the crossover and mutation operations until the maximum number of iterations or the optimization target converges.

[0042] Example 8, see Figure 1 This embodiment is based on the above embodiment. In step S7, data protection is the optimal data protection strategy individual after the output iteration ends. The data protection configuration is obtained based on the individual position, and finally data protection is realized.

[0043] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

[0045] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A data protection method based on genetic algorithms, characterized in that: The method includes the following steps: Step S1: Predefinition; Obtain the data to be protected and define the data protection decision-making entity; Step S2: Initialize the uniform sampling set; initialize the data protection strategy using the uniform sampling set generation. Step S3: Global non-dominated sorting; Introduce adaptive reconnection probability and use network topology to implement non-dominated sorting for data protection strategies; Step S4: Crossover operation design; Introduce a two-stage crossover strategy of early linear crossover and late-stage preferred crossover; Step S5: Mutation operation design; Introduce a local perturbation design for a dual-mode mutation mechanism; Step S6: Iterative strategy; reshaping the population based on non-dominated sorting; Step S7: Data Protection; Implement data protection individually based on the optimal data protection strategy after the iteration. In step S2, the uniform sampling set initialization is performed by generating a uniform sampling set to initialize the data protection strategy; the uniform sampling set generation is represented as: The data protection policy initialization is represented as follows: ;in, It is the initial good point value of the i-th individual in the j-th dimension; i is the individual index, and j is the dimension index of the parameter optimization space; It is the initial value of the j-th dimension for the i-th data protection decision-making individual; and These are the minimum and maximum values ​​of the j-th dimension of the parameter optimization space, respectively; In step S3, the global non-dominated sorting is used to sort all data protection strategies; a small-world network is constructed, defining the number of nodes N, where each data protection decision-maker corresponds to a node in the network, and the total number of nodes is equal to the size of the data protection decision-making population; the degree K is defined, where each node in the initial ring graph is connected to its left and right neighbors K / 2, for a total of K edges; and the adaptive reconnection probability p is defined as: Where gen is the current iteration number; maxgen is the maximum iteration number; γ is the basic reconnection probability; γ is the exponential control factor; for each edge on the ring, after being disconnected with probability p, it is randomly connected to any other node in the network, resulting in a small-world network; for the i-th data protection decision-maker... Extract its neighbor set in the small-world network, perform a non-dominated sort on the neighbor set, and select the individual with the highest sorted value as the spouse of the data protection decision individual. Define the dominance relationship, if And there exists at least one u such that ;but Compare Better; and These are two data protection decision-makers that define their dominance relationship; It is the data loss rate; It is a consumption of resources; This is the access delay time.

2. The data protection method based on genetic algorithm according to claim 1, characterized in that: In step S4, the crossover operation design preferentially selects the parents with a higher ranking in the global non-dominated sorting, and obtains new generation data protection strategy individuals through the crossover operation. In the early exploration when gen < maxgen / 3, linear crossover is used, and the linear crossover is expressed as: ; ; where and are the values of the j-th dimension of two new offspring data protection strategy individuals generated by the i-th data protection decision individual through the crossover operation in the early stage; is the value of the j-th dimension; is the value of the j-th dimension; In the later stage, optimal crossover is used, and the optimal crossover is expressed as: ; ; where and are the values of the j-th dimension of two new offspring data protection strategy individuals generated by the i-th data protection decision individual through the crossover operation in the later stage; is the value of the j-th dimension of a random data protection strategy individual; is the value of the j-th dimension of the data protection strategy individual with the highest ranking in the non-dominated sorting; is the perturbation factor; ; Introduce the dynamic retention probability , expressed as: ; where is the crossover distribution index; c is a value randomly selected from the uniform distribution; Each pair of parents generates two offspring, calculates the non-dominated ranks of the offspring respectively. If , only retain the better offspring; Otherwise, retain both; rand is a random number between 0 and 1.

3. The data protection method based on genetic algorithm according to claim 2, characterized in that: In step S5, the mutation operation design uses the Lévy mutation step size in the first half of gen < maxgen / 2, and the step size is defined as: ; The update is expressed as: ; where RL is a random number obeying the Lévy distribution; is the value of the j-th dimension of the data protection policy individual obtained by the mutation operation in the first half; is a random perturbation factor of the uniform distribution; In the second half, polynomial mutation is used for fine-tuning, and the update is expressed as: ; ; where is the value of the j-th dimension of the data protection policy individual obtained by the mutation operation in the second half; is the polynomial mutation value; is the polynomial distribution exponent; is a random number between 0 and 1, independent of rand; For each mutated individual, a dimension is randomly selected to perform local perturbation, and the formula used is: ; ; where and are the results after local perturbation; is the local perturbation factor, .

4. The data protection method based on genetic algorithm according to claim 3, characterized in that: In step S1, the predefinition involves acquiring the data to be protected. Each data protection decision-making individual x represents a data protection strategy, including encryption level, redundancy backup ratio, storage node distribution, access control granularity, and protection parameter settings. A parameter optimization space is established based on the encryption level, redundancy backup ratio, storage node distribution, access control granularity, and protection parameter settings. An optimization objective is defined. Represented as: .

5. The data protection method based on genetic algorithm according to claim 4, characterized in that: In step S6, the iterative strategy is to select the next generation of data protection strategy individuals of the parent and child generations according to the non-dominated sorting in each generation, keep the population size unchanged, and repeat the crossover and mutation operations until the maximum number of iterations or the optimization target converges.

6. The data protection method based on genetic algorithm according to claim 5, characterized in that: In step S7, the data protection is the optimal data protection strategy individual after the output iteration ends. The data protection configuration is obtained based on the individual's location, and the data protection is finally realized.

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