Big data privacy aggregation encryption method based on polymorphic fuzzy logic

Abstract

The invention discloses a big data privacy aggregation encryption method based on polymorphic fuzzy logic. The method comprises the following steps: S1, generating a to-be-encrypted data set containing privacy sensitive information; s2, constructing a fuzzy logic rule base based on polymorphic fuzzy logic; s3, generating a dynamically optimized fuzzy logic rule; s4, forming a fuzzy data set; s5, forming an optimized aggregated data block; s6, generating an encrypted data block, and generating index information corresponding to the encrypted data block; and S7, storing the encrypted data block and the index information to a distributed storage system, and dynamically controlling the access of the distributed storage system based on a data access permission rule, so that access users with different permissions can only obtain data matched with the permissions in the encrypted data block. According to the method, the privacy protection strength and efficiency are effectively improved, and a safer and more reliable solution is provided for a big data privacy protection scene.

Description

Technical Field

[0001] The present invention relates to the field of encryption technology, and in particular to a big data privacy aggregation encryption method based on polymorphic fuzzy logic. Background Art

[0002] With the rapid development of big data technology, data privacy protection has gradually become a focus of public and technical attention. In big data scenarios involving sensitive information in the medical, financial, and social fields, how to protect privacy and security during data sharing and processing has become an important issue that needs to be addressed urgently. Data privacy protection not only concerns personal rights and interests, but is also directly related to the popularization and application of data-driven technology. However, the current technical system faces many challenges in achieving a balance between privacy protection and data utilization.

[0003] At present, traditional data privacy protection methods mainly use static encryption technology or simple desensitization processing. Static encryption technology prevents unauthorized access by encrypting the entire data, but its disadvantage is that the encryption strength is linearly related to the computational complexity. In big data scenarios, due to the large amount of data and strong heterogeneity, static encryption often leads to low processing efficiency and difficulty in meeting real-time requirements. At the same time, although desensitization processing can reduce computational complexity, overly simple rule design can easily weaken the availability of data and even lead to the leakage of sensitive information.

[0004] In recent years, fuzzy logic and intelligent optimization algorithms have been gradually introduced into the field of data privacy protection to address the above-mentioned issues. Fuzzy logic can achieve multi-level protection of complex data by utilizing its uncertainty processing capabilities, while optimization algorithms can dynamically adjust encryption parameters to adapt to different scenarios. However, there are still deficiencies in practical applications: on the one hand, the rule design of existing fuzzy logic methods mostly relies on human experience, which makes it difficult to dynamically adapt to heterogeneous data and multi-scenario requirements; on the other hand, traditional optimization algorithms are prone to fall into local optimality, resulting in the inability to achieve ideal levels of privacy protection strength and computational efficiency. In addition, existing technologies lack a unified privacy protection mechanism in data aggregation and distributed storage, making it difficult to simultaneously meet the requirements of data processing efficiency and privacy security. Summary of the invention

[0005] One purpose of the present invention is to propose a big data privacy aggregation encryption method based on polymorphic fuzzy logic. The present invention effectively improves the strength and efficiency of privacy protection, and also provides a safer and more reliable solution for big data privacy protection scenarios.

[0006] A big data privacy aggregation encryption method based on polymorphic fuzzy logic according to an embodiment of the present invention includes the following steps: S1. Preprocess the target data set to generate a data set to be encrypted containing privacy-sensitive information; S2, building a fuzzy logic rule base based on polymorphic fuzzy logic; S3, using the bitter fish optimization algorithm to optimize the fuzzy membership function parameters and fuzzification rules in the fuzzy logic rule base to generate dynamically optimized fuzzy logic rules; S4, using the optimized fuzzy logic rules to perform fuzzification processing on each data element in the encrypted data set, converting the privacy-sensitive data into a fuzzy set to form a fuzzy data set; S5. Introduce an aggregate encryption mechanism based on the fuzzy data set, aggregate the privacy-sensitive data with similar characteristics in the fuzzy data set according to the similarity, and determine the data block partitioning strategy according to the optimization goal to form an optimized aggregate data block; S6. Encrypt the optimized aggregated data block using a dynamic encryption key generation strategy to generate an encrypted data block, and generate index information corresponding to the encrypted data block; S7. Storing the encrypted data blocks and index information in a distributed storage system, and dynamically controlling access to the distributed storage system based on data access permission rules, so that access users with different permissions can only obtain data in the encrypted data blocks that matches their permissions.

[0007] Optionally, S1 includes the following specific processes: S11, performing data cleaning on the target data set, detecting and removing redundant data, incomplete data and abnormal data in the target data set; S12, performing format standardization processing on the target data set after data cleaning, and standardizing the numerical data, categorical data and time data respectively; S13: Label the privacy-sensitive attributes of the standardized target dataset, predefine a sensitive attribute set S based on domain knowledge, and annotate each column of data in the target dataset. Classify the attributes and determine whether they belong to the sensitive attribute set S. If the data column If it belongs to the sensitive attribute set S, then the column is marked as a privacy-sensitive attribute; S14. Generate a dataset to be encrypted containing privacy-sensitive information based on data cleaning, format standardization, and target dataset labeled with privacy-sensitive attributes .

[0008] Optionally, S2 includes the following specific processes: S21. Construct a method for treating encrypted data sets according to different data types, privacy sensitivity levels and scenario requirements in the target data set. Polymorphic fuzzy membership function for fuzzy description of each attribute data , the polymorphic fuzzy membership function is realized by integrating the forms of Gaussian fuzzy membership function, triangular fuzzy membership function and discrete type fuzzy membership function, and uniformly expresses the fuzzy descriptions of the same attribute in different scenarios: ; Among them, represents the attribute with the serial number in the dataset to be encrypted, represents the privacy requirement for the th application scenario, is the value of the attribute in the data record, 丶 and are weight parameters, used to balance the contribution degrees of the Gaussian fuzzy membership function, triangular fuzzy membership function and discrete type fuzzy membership function in the scenario . The weight parameters determine the influence intensity of different types of data in the fuzzification process in big data privacy aggregation encryption. and are the center and standard deviation parameters of the Gaussian membership function. , , are the lower bound, peak and upper bound parameters of the triangular membership function, used to fuzzify the attribute data in the numerical domain to meet the requirements of different privacy sensitivity levels. is the v-th possible value in the discrete value set of the categorical data. is an indicator function, used to perform fuzzy description of specific value matching for categorical data, and then granularize the privacy characteristics of categorical attributes in the big data privacy aggregation encryption process. is the number of possible values of the categorical data in the scenario . g represents the Gaussian membership function, t represents the triangular membership function, cat represents the category characteristics of discrete data, and v represents the index of the discrete value currently being processed; S22. Construct a set of fuzzification rules R based on the polymorphic fuzzy membership function, and perform fuzzy decision mapping on the privacy-sensitive attributes and application requirements in the scenario in the dataset to be encrypted, and define the matching degree of the fuzzy rule as: ; ; In the formula, is the matching degree of the k-th rule to the dataset to be encrypted in the scenario , is the set of privacy-sensitive attributes, corresponding to the marked sensitive attributes. is the rule for the attribute The weight parameter, Adjust the parameters for rule matching, is the number of records in the dataset to be encrypted, To accumulate the fuzzy membership of the attribute after traversing all the records in the data set to be encrypted, Indicates that for any data record d in the data set; S23, polymorphically initialize the fuzzy parameter set P to meet the dynamic adaptability of big data privacy aggregation encryption to various data types and privacy requirements; S24, constructing a polymorphic fuzzy logic rule base F based on the polymorphic fuzzy membership function, the fuzzification rule set R and the fuzzification parameter set P: .

[0009] Optionally, S3 includes the following specific processes: S31, fuzzy logic rule base F defines comprehensive optimization objective function for the three goals of privacy protection strength, encryption efficiency and decryption accuracy : ; in, It is a measure of the privacy protection degree after fuzzification and is defined as the inverse of the deviation between the fuzzy membership and the target threshold. It reflects the encryption efficiency and is defined as the inverse of the encryption time and the data size. Characterizes the similarity between the decrypted data and the original data, , , is the weight parameter; S32. Construct and initialize the bitter fish population , each individual Represents a set of specific parameter configurations in the fuzzy logic rule base F, setting the population size , the proportion of escaped individuals , Bitter fish activation threshold and the maximum number of iterations ; S33. For each individual in the population Calculate the fitness value based on the fuzzy logic rule base and the optimization objective function: ; in, Reflecting the population The comprehensive degree of satisfaction with the optimization goal is used to select the optimal solution; S34. The update rule for bitter fish optimization includes the population individuals whose fitness has not triggered the activation threshold. , update the population individual parameters: ; in, is the updated population individual parameter, is the individual parameter of the current generation population, is the learning step size, is the optimal population individual of the current generation, is a random disturbance; For fitness below the activation threshold The population individuals trigger the bitter fish activation mechanism and generate new population individuals to replace: ; in, It is a new population individual generated by the bitter fish activation mechanism. is the step size, used to control the generation range, is a uniform random number; After multiple rounds of iterations, the parameter set converges to the optimal solution and generates an optimized parameter set. ; S35. After all iterations are completed or the optimization target converges, the dynamic optimization fuzzy logic rule set corresponding to the optimal solution is output. : ; in, , is the optimized fuzzy set, corresponding to the updated membership function parameters, Output fuzzy sets for optimized rules, is the fuzzy rules generated by optimization; S36, based on the optimized fuzzy rule set and optimized fuzzy membership function And the optimized parameter set , build a dynamically optimized fuzzy logic rule base: .

[0010] Optionally, S4 includes the following specific processes: S41. Treat each data element in the encrypted data set based on the optimized fuzzy logic rule base Perform fuzzy processing to obtain all data elements after fuzzy processing , and organized into fuzzy sets ; S42. Synthesize the fuzzy sets of all privacy-sensitive attributes to form a complete fuzzy data set : ; in, A collection of application scenarios; S43. The complete fuzzy data set According to the distributed storage requirements, the data is stored in shards and scene index information is added to each data slice to obtain a fuzzy data set stored in shards. .

[0011] Optionally, S5 includes the following specific processes: S51. For fuzzy data sets Define the similarity function between data elements , the similarity function is used to measure the fuzzy data elements Fuzzy data elements The similarity of characteristics; S52, preliminarily aggregate the data elements with high similarity in the fuzzified data set according to the following conditions: ; in, is the similarity threshold, Represents fuzzy data elements Fuzzy data elements Belong to the same aggregate data block; Initial aggregation generates a set of data blocks ,in is the mth aggregate data block.

[0012] Optionally, S6 includes the following specific processes: S61, for each initially generated data block , dynamically define data blocks based on their characteristics Aggregate encryption key : ; in, is a hash function, is the cumulative value of the fuzzy membership of all elements in the aggregated data block, reflecting the fuzzy characteristics of the data block. is a unique identifier for the data block; Utilizing dynamically generated keys For data blocks Encrypt to obtain the encrypted aggregate data block ; S62. Using the optimization objective function Encrypted aggregate data blocks Perform partition optimization to generate an optimized set of aggregated data blocks : ; in, To measure the privacy protection strength of the aggregated data block The blurring effect, For encryption efficiency, measure the aggregation of data blocks during encryption The relationship between size and computation time, To measure the accuracy of decryption, aggregate data blocks The similarity of the decrypted data to the original data.

[0013] The beneficial effects of the present invention are: (1) The present invention adopts polymorphic fuzzy logic to dynamically construct fuzzy membership functions and fuzzification rules according to the data type, privacy sensitivity level and application scenario, which solves the limitation of traditional static fuzzy logic methods that are difficult to adapt to complex scenarios. By organically combining three different fuzzy membership functions, the present invention achieves unified processing capabilities for heterogeneous data, enabling it to flexibly cope with diverse big data environments.

[0014] (2) The present invention uses the bitter fish optimization algorithm to globally optimize the parameters and rules of the fuzzy logic rule base to ensure a balance between privacy protection strength and encryption efficiency. Traditional optimization algorithms are prone to fall into the problem of local optimality, while the bitter fish algorithm improves the global search capability of the algorithm by introducing an escape mechanism and a random perturbation factor, making the optimization of the fuzzy logic rule base more efficient.

[0015] (3) The aggregate encryption mechanism proposed in the present invention defines a dynamic encryption key for the aggregated data block, enabling the encryption process to dynamically adjust the key generation rules according to the characteristics of the data block, thereby solving the problem of poor adaptability of the traditional static key mechanism in heterogeneous data scenarios. The key is dynamically generated based on the cumulative value of the fuzzy membership degree of the data block and the unique identifier, ensuring that the encryption process of each data block is independent and more secure. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of a big data privacy aggregation encryption method based on polymorphic fuzzy logic proposed by the present invention; Figure 2 This is a flowchart of optimizing the fuzzy logic rule base based on the bitter fish optimization algorithm in a big data privacy aggregation encryption method based on polymorphic fuzzy logic proposed by the present invention. DETAILED DESCRIPTION

[0017] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0018] refer to Figure 1-Figure 2 , a big data privacy aggregation encryption method based on polymorphic fuzzy logic, comprising the following steps: S1. Preprocess the target data set to generate a data set to be encrypted containing privacy-sensitive information; S2, building a fuzzy logic rule base based on polymorphic fuzzy logic; S3, using the bitter fish optimization algorithm to optimize the fuzzy membership function parameters and fuzzification rules in the fuzzy logic rule base to generate dynamically optimized fuzzy logic rules; S4, using the optimized fuzzy logic rules to perform fuzzification processing on each data element in the encrypted data set, converting the privacy-sensitive data into a fuzzy set to form a fuzzy data set; S5. Introduce an aggregate encryption mechanism based on the fuzzy data set, aggregate the privacy-sensitive data with similar characteristics in the fuzzy data set according to the similarity, and determine the data block partitioning strategy according to the optimization goal to form an optimized aggregate data block; S6. Encrypt the optimized aggregated data block using a dynamic encryption key generation strategy to generate an encrypted data block, and generate index information corresponding to the encrypted data block; S7. Storing the encrypted data blocks and index information in a distributed storage system, and dynamically controlling access to the distributed storage system based on data access permission rules, so that access users with different permissions can only obtain data in the encrypted data blocks that matches their permissions.

[0019] In this implementation, S1 includes the following specific processes: S11, performing data cleaning on the target data set, detecting and removing redundant data, incomplete data and abnormal data in the target data set; S12, performing format standardization processing on the target data set after data cleaning, and standardizing the numerical data, categorical data and time data respectively; S13: Label the privacy-sensitive attributes of the standardized target dataset, predefine a sensitive attribute set S based on domain knowledge, and annotate each column of data in the target dataset. Classify the attributes and determine whether they belong to the sensitive attribute set S. If the data column If it belongs to the sensitive attribute set S, then the column is marked as a privacy-sensitive attribute; S14. Generate a dataset to be encrypted containing privacy-sensitive information based on data cleaning, format standardization, and target dataset labeled with privacy-sensitive attributes .

[0020] In this implementation, S2 includes the following specific processes: S21. Construct a polymorphic fuzzy membership function for fuzzily describing each attribute data in the dataset to be encrypted according to different data types, privacy sensitivity levels, and scenario requirements in the target dataset The polymorphic fuzzy membership function realizes the integration of Gaussian fuzzy membership function, triangular fuzzy membership function, and discrete type fuzzy membership function, and uniformly expresses the fuzzy descriptions of the same attribute in different scenarios: ; Among them, represents the attribute with the attribute serial number in the dataset to be encrypted, represents the privacy requirement for the th application scenario, is the value of the attribute in the data record, 丶 and are weight parameters used to balance the contribution degrees of the Gaussian fuzzy membership function, triangular fuzzy membership function, and discrete type fuzzy membership function in the scenario . The weight parameters determine the influence intensity of different types of data in the fuzzification process in big data privacy aggregation encryption, and are the center and standard deviation parameters of the Gaussian membership function, , , are the lower bound, peak, and upper bound parameters of the triangular membership function, which are used to fuzzify the attribute data in the numerical domain to meet the requirements of different privacy sensitivity levels, is the vth possible value in the discrete value set of the categorical data, is an indicator function used to fuzzily describe the specific value matching of the categorical data, and further granularize the privacy characteristics of the categorical attributes in the big data privacy aggregation encryption process, is the number of possible values of the categorical data in the scenario . g represents the Gaussian membership function, t represents the triangular membership function, cat represents the category characteristics of the discrete data, and v represents the index of the discrete value currently being processed; S22. Based on the polymorphic fuzzy membership function, construct a set of fuzzy rules R, and perform fuzzy decision mapping on the privacy-sensitive attributes and application requirements in the scenario in the dataset to be encrypted, and define the matching degree of the fuzzy rule as: ; In the formula, is the kth rule in the scenario​​ The matching degree of the encrypted data set is as follows: is a set of privacy-sensitive attributes, corresponding to the labeled sensitive attributes, For rules For attributes The weight parameter, Adjust the parameters for rule matching, is the number of records in the dataset to be encrypted, To accumulate the fuzzy membership of the attribute after traversing all the records in the data set to be encrypted, Indicates that for any data record d in the data set; S23, polymorphically initialize the fuzzy parameter set P to meet the dynamic adaptability of big data privacy aggregation encryption to various data types and privacy requirements; S24, constructing a polymorphic fuzzy logic rule base F based on the polymorphic fuzzy membership function, the fuzzification rule set R and the fuzzification parameter set P: .

[0021] In this implementation, S3 includes the following specific processes: S31, fuzzy logic rule base F defines comprehensive optimization objective function for the three goals of privacy protection strength, encryption efficiency and decryption accuracy : ; in, It is a measure of the privacy protection degree after fuzzification and is defined as the inverse of the deviation between the fuzzy membership and the target threshold. It reflects the encryption efficiency and is defined as the inverse of the encryption time and the data size. Characterizes the similarity between the decrypted data and the original data, , , is the weight parameter; S32. Construct and initialize the bitter fish population , each individual Represents a set of specific parameter configurations in the fuzzy logic rule base F, setting the population size , the proportion of escaped individuals , Bitter fish activation threshold and the maximum number of iterations ; S33. For each individual in the population Calculate the fitness value based on the fuzzy logic rule base and the optimization objective function: ; in, Reflecting the population The comprehensive degree of satisfaction with the optimization goal is used to select the optimal solution; S34. The update rule for bitter fish optimization includes the population individuals whose fitness has not triggered the activation threshold. , update the population individual parameters: ; in, is the updated population individual parameter, is the individual parameter of the current generation population, is the learning step size, is the optimal population individual of the current generation, is a random disturbance; For fitness below the activation threshold The population individuals trigger the bitter fish activation mechanism and generate new population individuals to replace: ; in, It is a new population individual generated by the bitter fish activation mechanism. is the step size, used to control the generation range, is a uniform random number; After multiple rounds of iterations, the parameter set converges to the optimal solution and generates an optimized parameter set. ; S35. After all iterations are completed or the optimization target converges, the dynamic optimization fuzzy logic rule set corresponding to the optimal solution is output. : ; in, , is the optimized fuzzy set, corresponding to the updated membership function parameters, Output fuzzy sets for optimized rules, is the fuzzy rules generated by optimization; S36, based on the optimized fuzzy rule set and optimized fuzzy membership function And the optimized parameter set , build a dynamically optimized fuzzy logic rule base: .

[0022] In this implementation, S4 includes the following specific processes: S41. Treat each data element in the encrypted data set based on the optimized fuzzy logic rule base Perform fuzzy processing to obtain all data elements after fuzzy processing , and organized into fuzzy sets ; S42. Synthesize the fuzzy sets of all privacy-sensitive attributes to form a complete fuzzy data set : ; in, A collection of application scenarios; S43. The complete fuzzy data set According to the distributed storage requirements, the data is stored in shards and scene index information is added to each data slice to obtain a fuzzy data set stored in shards. .

[0023] In this implementation, S5 includes the following specific processes: S51. For fuzzy data sets Define the similarity function between data elements , the similarity function is used to measure the fuzzy data elements Fuzzy data elements The similarity of characteristics; S52, preliminarily aggregate the data elements with high similarity in the fuzzified data set according to the following conditions: ; in, is the similarity threshold, Represents fuzzy data elements Fuzzy data elements Belong to the same aggregate data block; Initial aggregation generates a set of data blocks ,in is the mth aggregate data block.

[0024] In this implementation, S6 includes the following specific processes: S61, for each initially generated data block , dynamically define data blocks based on their characteristics Aggregate encryption key : ; in, is a hash function, It is the cumulative value of the fuzzy membership of all elements in the aggregated data block, reflecting the fuzzy characteristics of the data block. is a unique identifier for the data block; Utilizing dynamically generated keys For data blocks Encrypt to obtain the encrypted aggregate data block ; S62. Using the optimization objective function Encrypted aggregate data blocks Perform partition optimization to generate an optimized set of aggregated data blocks : ; in, To measure the privacy protection strength of the aggregated data block The blurring effect, For encryption efficiency, measure the aggregation of data blocks during encryption The relationship between size and computation time, To measure the accuracy of decryption, aggregate data blocks The similarity of the decrypted data to the original data.

[0025] Embodiment 1: In order to verify the feasibility and effectiveness of the present invention, the experimenters selected two public medical datasets, MIMIC-III and HCUP, as the experimental data sources. The two datasets respectively contain a large amount of electronic health record data, which are highly heterogeneous and privacy-sensitive.

[0026] The MIMIC-III dataset contains the hospitalization information of 40,000 patients in Medical Center A, covering clinical diagnosis, laboratory tests, treatment plan structured and unstructured data.

[0027] The HCUP dataset is a collection of inpatient data provided by the B Medical Data Center, containing 50,000 patient records, including personal identification information (name, gender, address) and medical history (disease diagnosis, surgical records).

[0028] The experimenters divided the two data sets into training set and test set in a ratio of 8:2. The goal of the experiment was to verify the performance of the present invention in terms of privacy protection strength, encryption efficiency, decryption accuracy and data utilization.

[0029] In the experiment, the experimenters first preprocessed the dataset to clean invalid records and standardized the field format. For the numeric fields of the patient's age, diagnosis records, and laboratory test values, the experimenters performed normalization. For text fields (doctor's clinical instructions), the BERT model was used to extract semantic embedding features and truncate the text length to unify the data format.

[0030] For the privacy-sensitive data in the experiment (name, diagnosis record, laboratory test value), an initial fuzzy logic rule base was constructed according to the data type and privacy sensitivity level, including: Gaussian membership function: used for numerical data (age), fuzzifying "30 years old" into a membership interval of 0.7 to [20-40]; Discrete membership function: used for categorized data (diagnosis codes), mapping similar diagnoses to fuzzy sets, with membership values ​​of 0.8 and 0.9 for "hypertension stage I" and "hypertension stage II" respectively; Triangular membership function: used for laboratory test values, defining the "normal range" as the peak of the fuzzified interval.

[0031] Based on the initial rule base, the experimenters used the bitter fish optimization algorithm to optimize the parameters and weights in the fuzzy logic rule base. The training process included 200 iterations, with a population size of 50 individuals in each generation, a learning rate set to 0.01, an activation threshold of 0.05, and a random perturbation factor of 0.1. The optimization objective function was a comprehensive trade-off between privacy protection strength, encryption efficiency, and decryption accuracy.

[0032] During the experiment, the experimenters performed similarity aggregation on the fuzzified data set and aggregated similar laboratory test records (blood sugar value difference <10%) into the same data block. The encryption key of each data block was dynamically generated based on the characteristics of the aggregated data, and the SHA-256 algorithm was used to generate a unique encryption key to ensure data security and independence.

[0033] In order to prove the effectiveness of the present invention, the experimenters compared the performance of the traditional static encryption method and the method of the present invention on two data sets. The test results are as follows: Table 1 Experimental results on the MIMIC-III dataset

[0034] Table 2 Experimental results on the HCUP dataset

[0035] According to Table 1 and Table 2 above, it can be seen that in terms of privacy protection strength, the fuzzy membership deviation of the method of the present invention on the two data sets is significantly lower than that of the traditional method, and the MIMIC-III and HCUP data sets are improved by 41.7% and 50% respectively, effectively enhancing the privacy protection capability. In terms of encryption and decryption efficiency, compared with the traditional method, the fuzzification time and encryption time of the method of the present invention are reduced by about 40%, and the decryption accuracy is improved by about 10%, which can meet the real-time and data availability requirements of the medical big data scenario. In terms of data utilization, since the present invention adopts dynamically optimized fuzzification rules and aggregate encryption mechanism, the main feature information of the data is retained while protecting privacy, so that the data utilization rate is increased to more than 90%.

[0036] It can be seen from the examples that the method of the present invention is not only superior to traditional methods in terms of privacy protection strength and efficiency, but can also achieve efficient and secure data encryption and sharing in heterogeneous medical data scenarios, and has high practical application value.

[0037] The present invention adopts polymorphic fuzzy logic to dynamically construct fuzzy membership functions and fuzzification rules according to the data type, privacy sensitivity level and application scenario, which solves the limitation that traditional static fuzzy logic methods are difficult to adapt to complex scenarios. By organically combining three different fuzzy membership functions, it realizes the unified processing capability of heterogeneous data, enabling it to flexibly respond to diverse big data environments. Experimental verification shows that in multi-scenario testing, the privacy protection sensitivity of this method is improved by 17%, and the analysis utilization rate of data is maintained above 90% while the sensitive information is blurred, effectively taking into account both privacy protection and data availability.

[0038] The present invention uses the bitter fish optimization algorithm to globally optimize the parameters and rules of the fuzzy logic rule base to ensure the balance between privacy protection strength and encryption efficiency. Traditional optimization algorithms are prone to fall into the problem of local optimality, while the bitter fish algorithm improves the global search capability of the algorithm by introducing escape mechanisms and random perturbation factors, making the optimization of the fuzzy logic rule base more efficient.

[0039] The aggregate encryption mechanism proposed in the present invention defines a dynamic encryption key for the aggregated data block, enabling the encryption process to dynamically adjust the key generation rules according to the characteristics of the data block, thereby solving the problem of poor adaptability of the traditional static key mechanism in heterogeneous data scenarios. The key is dynamically generated based on the cumulative value of the fuzzy membership of the data block and the unique identifier, ensuring that the encryption process of each data block is independent and more secure.

[0040] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A big data privacy aggregation encryption method based on polymorphic fuzzy logic, characterized in that: The steps include: S1. Preprocess the target data set to generate a data set to be encrypted containing privacy-sensitive information; S2, building a fuzzy logic rule base based on polymorphic fuzzy logic; S3, using the bitter fish optimization algorithm to optimize the fuzzy membership function parameters and fuzzification rules in the fuzzy logic rule base to generate dynamically optimized fuzzy logic rules; S4, using the optimized fuzzy logic rules to perform fuzzification processing on each data element in the encrypted data set, converting the privacy-sensitive data into a fuzzy set to form a fuzzy data set; S5. Introduce an aggregate encryption mechanism based on the fuzzy data set, aggregate the privacy-sensitive data with similar characteristics in the fuzzy data set according to the similarity, and determine the data block partitioning strategy according to the optimization goal to form an optimized aggregate data block; S6. Encrypt the optimized aggregated data block using a dynamic encryption key generation strategy to generate an encrypted data block, and generate index information corresponding to the encrypted data block; S7. Storing the encrypted data blocks and index information in a distributed storage system, and dynamically controlling access to the distributed storage system based on data access permission rules, so that access users with different permissions can only obtain data in the encrypted data blocks that matches their permissions.

2. According to the big data privacy aggregation encryption method based on polymorphic fuzzy logic in claim 1, it is characterized in that: The S1 includes the following specific processes: S11, performing data cleaning on the target data set, detecting and removing redundant data, incomplete data and abnormal data in the target data set; S12, performing format standardization processing on the target data set after data cleaning, and standardizing the numerical data, categorical data and time data respectively; S13: Label the privacy-sensitive attributes of the standardized target dataset, predefine a sensitive attribute set S based on domain knowledge, and annotate each column of data in the target dataset. Classify the attributes and determine whether they belong to the sensitive attribute set S. If the data column If it belongs to the sensitive attribute set S, then the column is marked as a privacy-sensitive attribute; S14. Generate a dataset to be encrypted containing privacy-sensitive information based on data cleaning, format standardization, and target dataset labeled with privacy-sensitive attributes .

3. According to the big data privacy aggregation encryption method based on polymorphic fuzzy logic in claim 1, it is characterized in that: The S2 includes the following specific processes: S21. Construct a method for treating encrypted data sets according to different data types, privacy sensitivity levels and scenario requirements in the target data set. Polymorphic fuzzy membership function for fuzzy description of each attribute data The polymorphic fuzzy membership function is realized by integrating Gaussian fuzzy membership function, triangular fuzzy membership function and discrete type fuzzy membership function, and the fuzzy description of the same attribute in different scenarios is uniformly expressed: ； Among them, represents the attribute with the serial number in the dataset to be encrypted, represents the privacy requirement for the th application scenario, is the value of the attribute in the data record, 丶 and are weight parameters used to balance the contribution degrees of the Gaussian fuzzy membership function, triangular fuzzy membership function, and discrete type fuzzy membership function in scenario . The weight parameters determine the influence intensity of different types of data in the fuzzification process in big data privacy aggregation encryption. and are the center and standard deviation parameters of the Gaussian membership function. , , are the lower bound, peak, and upper bound parameters of the triangular membership function, which are used to fuzzify the attribute data in the numerical domain to meet the requirements of different privacy sensitivity levels. is the v-th possible value in the set of discrete values of the categorical data. is an indicator function used to perform fuzzy description of specific value matching for categorical data, and then granularize the privacy characteristics of categorical attributes in the big data privacy aggregation encryption process. is the number of possible values of the categorical data in scenario . g represents the Gaussian membership function, t represents the triangular membership function, cat represents the category characteristics of discrete data, and v represents the index of the discrete value being processed. S22. Construct a fuzzy rule set R based on the polymorphic fuzzy membership function to treat the encrypted data set Privacy-sensitive attributes and scenarios in The fuzzy decision mapping is performed based on the application requirements below, and the matching degree of the fuzzy rules is defined as: ； In the formula, For the kth rule in the scene The matching degree of the encrypted data set is as follows: is a set of privacy-sensitive attributes, corresponding to the labeled sensitive attributes, For rules For attributes The weight parameter, Adjust the parameters for rule matching. is the number of records in the dataset to be encrypted, To accumulate the fuzzy membership of the attribute after traversing all the records in the data set to be encrypted, Indicates that for any data record d in the data set; S23, polymorphically initialize the fuzzy parameter set P to meet the dynamic adaptability of big data privacy aggregation encryption to various data types and privacy requirements; S24, constructing a polymorphic fuzzy logic rule base F based on the polymorphic fuzzy membership function, the fuzzification rule set R and the fuzzification parameter set P: 。 4. According to the big data privacy aggregation encryption method based on polymorphic fuzzy logic in claim 1, it is characterized in that: The S3 includes the following specific processes: S31, fuzzy logic rule base F defines comprehensive optimization objective function for the three goals of privacy protection strength, encryption efficiency and decryption accuracy : ； in, It is a measure of the privacy protection degree after fuzzification and is defined as the inverse of the deviation between the fuzzy membership and the target threshold. It reflects the encryption efficiency and is defined as the inverse of the encryption time and the data size. Characterizes the similarity between the decrypted data and the original data, , , is the weight parameter; S32. Construct and initialize the bitter fish population , each individual Represents a set of specific parameter configurations in the fuzzy logic rule base F, setting the population size , the proportion of escaped individuals , Bitter fish activation threshold and the maximum number of iterations ; S33. For each individual in the population Calculate the fitness value based on the fuzzy logic rule base and the optimization objective function: ； in, Reflecting the population The comprehensive degree of satisfaction with the optimization goal is used to select the optimal solution; S34. The update rule for bitter fish optimization includes the population individuals whose fitness has not triggered the activation threshold. , update the population individual parameters: ； in, is the updated population individual parameter, is the individual parameter of the current generation population, is the learning step size, is the optimal population individual of the current generation, is a random disturbance; For fitness below the activation threshold The population individuals trigger the bitter fish activation mechanism and generate new population individuals to replace: ； in, It is a new population individual generated by the bitter fish activation mechanism. is the step size, used to control the generation range, is a uniform random number; After multiple rounds of iterations, the parameter set converges to the optimal solution and generates an optimized parameter set. ; S35. After all iterations are completed or the optimization target converges, the dynamic optimization fuzzy logic rule set corresponding to the optimal solution is output. : ； in, , is the optimized fuzzy set, corresponding to the updated membership function parameters, Output fuzzy sets for optimized rules, is the fuzzy rules generated by optimization; S36, based on the optimized fuzzy rule set and optimized fuzzy membership function And the optimized parameter set , build a dynamically optimized fuzzy logic rule base: 。 5. According to the big data privacy aggregation encryption method based on polymorphic fuzzy logic according to claim 1, it is characterized in that: The S4 includes the following specific processes: S41. Treat each data element in the encrypted data set based on the optimized fuzzy logic rule base Perform fuzzy processing to obtain all data elements after fuzzy processing , and organized into fuzzy sets ; S42. Synthesize the fuzzy sets of all privacy-sensitive attributes to form a complete fuzzy data set : ； in, A collection of application scenarios; S43. The complete fuzzy data set According to the distributed storage requirements, the data is stored in shards and scene index information is added to each data slice to obtain a fuzzy data set stored in shards. .

6. According to the big data privacy aggregation encryption method based on polymorphic fuzzy logic in claim 1, it is characterized in that: The S5 includes the following specific processes: S51. For fuzzy data sets Define the similarity function between data elements , the similarity function is used to measure the fuzzy data elements Fuzzy data elements The similarity of characteristics; S52, preliminarily aggregate the data elements with high similarity in the fuzzified data set according to the following conditions: ； in, is the similarity threshold, Represents fuzzy data elements Fuzzy data elements Belong to the same aggregate data block; Initial aggregation generates a set of data blocks ,in is the mth aggregate data block.

7. According to claim 6, a big data privacy aggregation encryption method based on polymorphic fuzzy logic is characterized in that: The S6 includes the following specific processes: S61, for each data block initially generated , dynamically define data blocks based on their characteristics Aggregate encryption key : ； in, is a hash function, is the cumulative value of the fuzzy membership of all elements in the aggregated data block, reflecting the fuzzy characteristics of the data block. is a unique identifier for the data block; Utilizing dynamically generated keys For data blocks Encrypt to obtain the encrypted aggregate data block ; S62. Using the optimization objective function Encrypted aggregate data blocks Perform partition optimization to generate an optimized set of aggregated data blocks : ； in, To measure the privacy protection strength of the aggregated data block The blurring effect, For encryption efficiency, measure the aggregation of data blocks during encryption The relationship between size and computation time, To measure the accuracy of decryption, aggregate data blocks The similarity of the decrypted data to the original data.

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