Data sharing optimization method based on swan Mongolia distributed data management

Abstract

The invention belongs to the technical field of data sharing, and particularly relates to a data sharing optimization method based on Swan Mongolian distributed data management. A data providing end firstly performs duplicate removal processing on original data, then encrypts the data by using an algorithm based on a quantum encryption principle and a hash function, stores the encrypted data in a Qingmong distributed database, allocates identifiers, and records meta information. After a data request end initiates a sharing request, a data providing end calculates an authority score according to multiple factors such as equipment fingerprint credibility, user behavior matching degree and environmental risk to perform authority verification. And after the verification is passed, determining the optimal transmission mode and rate to transmit the encrypted data according to the equipment performance and the network condition of the request end, and decrypting to obtain the original data after the request end receives the encrypted data. Compared with the prior art, the data security is enhanced, the authority verification accuracy is improved, the data transmission efficiency is optimized, the problems existing in a traditional data sharing mechanism are effectively solved, and powerful support is provided for efficient and safe data sharing.

Description

Technical Field

[0001] The present invention belongs to the field of data sharing technology, and in particular, relates to a data sharing optimization method based on Hongmeng distributed data management. Background Art

[0002] As the Internet of Things and smart devices are rapidly becoming popular, the demand for cross-device and cross-platform data sharing is growing. Taking smart medical scenarios as an example, doctors, patients, inspection equipment, and cloud servers need to share key information such as medical records, imaging data, and monitoring indicators in real time to support remote diagnosis and treatment, health management, and scientific research analysis. However, traditional data sharing mechanisms have significant drawbacks in security, authority management, and transmission efficiency, and are difficult to meet the needs of data circulation in complex scenarios. In terms of data security, traditional encryption algorithms are difficult to resist the escalating quantum attacks, and data is at risk of being stolen and tampered during transmission and storage, which makes the protection of sensitive data extremely difficult. In the field of authority management, existing verification methods are often too simple, relying only on a single factor for identity authentication, and are unable to fully consider multiple factors such as equipment, user behavior, and environment, which can easily lead to misjudgment of authority and allow illegal users to take advantage of opportunities. In addition, there is also a lack of adaptive mechanisms during data transmission, and the transmission mode and rate cannot be flexibly adjusted according to device performance and network conditions. Problems such as long transmission time, data loss, or excessive energy consumption often occur, reducing the efficiency and experience of data sharing. Summary of the invention

[0003] In response to the technical problems existing in the above-mentioned background technology, the present invention proposes a data sharing optimization method based on Hongmeng distributed data management.

[0004] In order to achieve the above object, the technical solution adopted by the present invention comprises the following steps:

[0005] S1. The data provider uses an encryption algorithm to encrypt the original data to generate encrypted data. The encryption algorithm is based on the principle of quantum encryption combined with a hash function, and extracts data features through the one-time use of the quantum key and the hash function;

[0006] S2. Store the encrypted data in the Hongmeng distributed database and assign it a unique distributed data identifier, while recording the metadata of the data, including data type, data size, and creation time;

[0007] S3. The data requesting end initiates a data sharing request to the data providing end. The request includes its own device identification, user identity information, and a description of the requested data.

[0008] S4. After receiving the request, the data provider verifies the identity and authority of the data requester according to the preset authority verification rules;

[0009] The permission verification rule is: by verifying the device fingerprint credibility , User behavior matching and environmental risks Input the weighted model and get the authority score F: ,in is the weight of user behavior matching and device fingerprint credibility, is the nonlinear power parameter, They are the environmental risk suppression coefficient, Adjust the parameters for the environmental risk suppression factor. The higher the device fingerprint credibility, the stronger the tolerance for environmental risks. When the obtained permission score is greater than the threshold of 0.7, it indicates that the verification is passed, otherwise the access request is immediately blocked.

[0010] S5. If the verification is successful, the data provider uses an adaptive data transmission algorithm to determine the optimal data transmission method and transmission rate based on the device performance and network conditions of the data requester, extracts the encrypted data from the Hongmeng distributed database, and transmits it to the data requester;

[0011] S6. After receiving the encrypted data, the data requesting end uses the locally stored decryption key to decrypt the data and obtain the original data.

[0012] Preferably, the encryption algorithm in step S1 is specifically implemented as follows: using quantum randomness and chaotic systems to generate a dynamic key sequence; binding data features to keys to resist quantum collision attacks; and performing dynamic quantum gate encryption.

[0013] Preferably, the method of generating a dynamic key sequence by using quantum randomness and a chaotic system is to first generate an initial seed key by a quantum random number generator. ,satisfy , and then input the quantum seed S into the mapped chaotic system to generate a chaotic sequence K, which is ,in is the intermediate state value of the chaotic system, K is the dynamic key generated by the chaotic system, is the chaotic system parameter, which is calculated as follows: .

[0014] As a preferred method, after generating the dynamic key sequence, the data feature is bound to the key, and the implementation of resisting quantum collision attack is to divide the original data D into blocks, calculate the quantum-resistant hash for each block and project it into a vector, and perform nonlinear confusion on the chaotic sequence K and the hash feature H. The calculation method is: ,in For bitwise XOR operation, The obfuscated key.

[0015] As a preferred method, a quantum gate operation matrix is ​​constructed based on the obfuscated key to achieve encryption: Split into control parameters to generate a dynamic quantum gate set : ,in is a Y-axis rotary gate, CNOT is a controlled NOT gate, and the data block Encoded as quantum states , apply the quantum gate sequence to obtain the encrypted quantum state : ,in Represents the tensor product, and finally performs basis measurement on the encrypted quantum state to generate the classical ciphertext : .

[0016] Preferably, before data encryption, step S1 further requires deduplication of the data, extraction of semantic features, structural features and time features of the data, fusion of the extracted multi-dimensional features, conversion of high-dimensional feature vectors into low-dimensional vectors using principal component analysis, and construction of feature indexes using distributed hash tables. Each node is responsible for storing part of the feature index information, and feature vectors are mapped to different nodes through hash functions. When new data needs to be stored, its feature vector is calculated and a search is performed in the distributed hash table to determine whether there are similar features.

[0017] The similarity feature is to set a threshold. When the similarity between the feature vector of new data and the feature vector of stored data exceeds the threshold, it is determined to be duplicate data. The similarity calculation is performed using the cosine similarity method.

[0018] Preferably, the adaptive data transmission algorithm in step S5 is implemented as follows: constructing an intelligent decision-making model based on reinforcement learning, taking device performance and network conditions as environmental state vectors, transmission mode and transmission rate as action space, and using a multi-objective optimization algorithm to find the optimal solution among multiple objectives of shortest transmission time, highest data integrity, and lowest energy consumption, and determine the optimal data transmission mode and transmission rate.

[0019] Compared with the prior art, the advantages and positive effects of the present invention are that, at the encryption level, it combines the principles of quantum encryption with hash functions, uses quantum randomness and chaotic systems to generate keys, binds data features and performs quantum gate encryption, effectively resists quantum attacks and ensures data security. In the authority verification stage, a weighted model is constructed by integrating factors such as device fingerprint credibility, user behavior matching and environmental risks to accurately evaluate authority and reduce the risk of misjudgment. In the data transmission stage, an intelligent decision-making model is constructed based on reinforcement learning. According to device performance and network conditions, the optimal transmission strategy is determined through multi-objective optimization to achieve a balance between transmission time, integrity and energy consumption. These innovative technologies work together to improve the security, accuracy and efficiency of data sharing and solve traditional technical problems. DETAILED DESCRIPTION

[0020] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described below in conjunction with embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways than those described herein. Therefore, the present invention is not limited to the specific embodiments of the following disclosure.

[0022] Embodiment: In response to the special needs of smart medical scenarios, the present invention proposes a data sharing optimization method based on Hongmeng distributed data management to ensure the reliability of medical data in the entire link of "secure storage-accurate authorization-efficient transmission".

[0023] First, when the hospital imaging system uploads CT image data, the multi-dimensional features of the data are extracted. Before the data is encrypted, the data needs to be deduplicated to extract the semantic features, structural features and time features of the data, and the extracted multi-dimensional features are fused. The high-dimensional feature vector is converted into a low-dimensional vector using principal component analysis, and a feature index is constructed using a distributed hash table. Each node is responsible for storing part of the feature index information. The feature vector is mapped to different nodes through a hash function. When new data needs to be stored, its feature vector is calculated and a search is performed in the distributed hash table to determine whether there are similar features. The similarity feature is to set a threshold. When the similarity between the feature vector of the new data and the feature vector of the stored data exceeds the threshold, it is determined to be duplicate data. The similarity calculation is performed using the cosine similarity method.

[0024] Then, for the security of data during storage and transmission, considering that the existing data encryption technology has the defect of being difficult to resist quantum attacks, at the moment of rapid development of quantum computing technology, traditional encryption algorithms face the risk of being cracked, and data is easily stolen and tampered in the transmission and storage links. Therefore, the present invention adopts an encryption algorithm based on the principle of quantum encryption combined with a hash function. The quantum random number generator and chaotic system of the hospital's trusted node are used to generate a dynamic key sequence, and the initial seed key is generated with the help of a quantum random number generator, and then converted into a chaotic sequence through a specific chaotic system. This key generation method greatly improves the randomness and complexity of the key, making it difficult for attackers to crack. After that, the data features are bound to the key, and the quantum hash is calculated by dividing the original data into blocks and projecting it into a vector, and nonlinear confusion is performed with the chaotic sequence to effectively resist quantum collision attacks, further enhancing the security of the data. Finally, a quantum gate operation matrix is ​​constructed based on the confused key to perform dynamic quantum gate encryption, and the data block is encoded into a quantum state, and a classical ciphertext is generated after encryption by the quantum gate sequence.

[0025] The method of using quantum randomness and chaotic system to generate dynamic key sequence is to first generate an initial seed key by using a quantum random number generator. ,satisfy , and then input the quantum seed S into the mapped chaotic system to generate a chaotic sequence K, which is ,in is the intermediate state value of the chaotic system, K is the dynamic key generated by the chaotic system, is the chaotic system parameter, which is calculated as follows: After the dynamic key sequence is generated, the data features are bound to the key. The implementation of resisting quantum collision attacks is to divide the original data D into blocks, calculate the quantum-resistant hash for each block and project it into a vector, and perform nonlinear confusion between the chaotic sequence K and the hash feature H. The calculation method is: ,in For bitwise XOR operation, is the obfuscated key. Finally, the quantum gate operation matrix is ​​constructed based on the obfuscated key to achieve encryption: Split into control parameters to generate a dynamic quantum gate set : ,in is a Y-axis rotary gate, CNOT is a controlled NOT gate, Represents quantum bits, which are blocks of data Encoded as quantum states , apply the quantum gate sequence to obtain the encrypted quantum state : ,in represents the tensor product, and finally performs basis measurement on the encrypted quantum state. j represents the level of quantum gate application to generate classical ciphertext : Compared with traditional encryption algorithms, it can better cope with the threat of quantum attacks, from key generation, data and key binding to encryption operations, and comprehensively improve data security.

[0026] The encrypted CT image data is then stored in the Hongmeng distributed database, and a unique distributed data identifier is assigned to it for easy data management and retrieval. At the same time, the metadata of the data is recorded, including data type, data size, and creation time. Through distributed storage, servers in different hospital districts can synchronize image data in real time, support doctors to access data across hospital districts, and avoid data loss caused by single point failures.

[0027] Patients initiate data sharing requests to hospital servers through mobile devices. The requests include their own device identification, user identity information, and descriptions of the requested data, such as requesting customer transaction record data within a specific time period. After receiving the request, the data provider verifies the identity and authority of the data requester according to the preset authority verification rules.

[0028] Considering that the existing permission verification does not take into account multiple factors such as device fingerprint credibility, user behavior matching, and environmental risks, it only relies on a single factor for identity authentication, which has the defect of misjudgment of permissions. This allows illegal users to exploit verification loopholes to obtain data access rights, resulting in serious threats to data security and failure to ensure data privacy and integrity. The permission verification rule in this invention is: the hospital server verifies the device fingerprint credibility by , User behavior matching and environmental risks Input the weighted model and get the authority score F: ,in is the weight of user behavior matching and device fingerprint credibility, is the nonlinear power parameter, They are the environmental risk suppression coefficient, The parameters are adjusted for the environmental risk suppression factor. The higher the credibility of the device fingerprint, the greater the tolerance for environmental risks. When the obtained permission score is greater than the threshold of 0.7, it indicates that the verification is successful. Otherwise, the access request is blocked immediately. While ensuring data security, it also takes into account the normal access needs of legitimate users in a complex network environment, providing more reliable security protection for data sharing.

[0029] If the authority verification is passed, the data provider uses an adaptive data transmission algorithm to determine the optimal data transmission method and transmission rate based on the device performance and network conditions of the data requester. The adaptive data transmission algorithm is implemented by constructing an intelligent decision-making model based on reinforcement learning. With device performance and network conditions as environmental state vectors, and transmission methods and transmission rates as action spaces, a multi-objective optimization algorithm is used to find the optimal solution among multiple goals: shortest transmission time, highest data integrity, and lowest energy consumption. When it is detected that the requester device has strong processing power, sufficient network bandwidth, and low latency, the algorithm may choose to transmit data at a higher transmission rate and transmission method to ensure data integrity and transmission efficiency; when the network condition is poor, the algorithm will adjust the transmission rate and method to prioritize data integrity and low energy consumption.

[0030] Finally, after the family doctor workstation at the data request end receives the encrypted data, it uses the locally stored decryption key to decrypt the data and obtain the original data for formulating personalized diagnosis and treatment plans, thereby realizing the need for data sharing.

[0031] The present invention is aimed at the core pain points of security, authority, transmission and other core pain points in data sharing, and is significantly superior to the sharing mechanism of the Hongmeng system in terms of technical architecture and actual performance. At the data security level, the Hongmeng native mechanism adopts a classical encryption algorithm, which is difficult to resist quantum attacks. The present invention integrates the principle of quantum encryption and chaotic hash binding technology, generates dynamic keys through quantum random numbers, nonlinear confusion of data features and keys, and dynamic quantum gate encryption, so as to improve the ability to resist quantum attacks, and at the same time, the amount of duplicate data storage is reduced through multi-dimensional feature deduplication. In the permission verification link, the Hongmeng mechanism relies on single factors such as device ID and user account. The present invention constructs a multi-factor weighted model including device fingerprint credibility, user behavior matching, and environmental risk, and dynamically adjusts the authority threshold with the help of nonlinear functions to reduce the error rate of authority. In particular, in scenarios such as cross-device access and abnormal environment operation, the ability to accurately identify risk requests is improved, and security and access convenience are effectively balanced. In the data transmission stage, Hongmeng adopts a fixed rate strategy, which is difficult to adapt to heterogeneous devices and dynamic network environments. The present invention uses a reinforcement learning intelligent decision-making model to perceive device performance and network status in real time, and improves the dynamic optimization of transmission time, integrity, and energy consumption.

[0032] In general, Hongmeng's built-in sharing mechanism is an efficient interconnection base for consumer scenarios. As an industry enhancement solution, this invention systematically solves the bottlenecks of traditional mechanisms in security strength, authority accuracy, and transmission adaptability through technical innovations such as quantum-resistant encryption, multi-dimensional authority assessment, and intelligent transmission optimization. It provides professional support for the efficient and secure circulation of key data, and promotes the upgrading of distributed data sharing towards quantum-resistant and intelligent directions.

[0033] The above description is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A data sharing optimization method based on Hongmeng distributed data management, characterized in that: The following steps are involved: S1. The data provider uses an encryption algorithm to encrypt the original data to generate encrypted data. The encryption algorithm is based on the principle of quantum encryption combined with a hash function, and extracts data features through the one-time use of the quantum key and the hash function; S2. Store the encrypted data in the Hongmeng distributed database and assign it a unique distributed data identifier, while recording the metadata of the data, including data type, data size, and creation time; S3. The data requesting end initiates a data sharing request to the data providing end. The request includes its own device identification, user identity information, and a description of the requested data. S4. After receiving the request, the data provider verifies the identity and authority of the data requester according to the preset authority verification rules; The permission verification rule is: by verifying the device fingerprint credibility , User behavior matching and environmental risks Input the weighted model and get the authority score F: ,in is the weight of user behavior matching and device fingerprint credibility, is the nonlinear power parameter, They are the environmental risk suppression coefficient, Adjust the parameters for the environmental risk suppression factor. The higher the device fingerprint credibility, the stronger the tolerance for environmental risks. When the obtained permission score is greater than the threshold of 0.7, it indicates that the verification is passed, otherwise the access request is immediately blocked. S5. If the verification is successful, the data provider uses an adaptive data transmission algorithm to determine the optimal data transmission method and transmission rate based on the device performance and network conditions of the data requester, extracts the encrypted data from the Hongmeng distributed database, and transmits it to the data requester; S6. After receiving the encrypted data, the data requesting end uses the locally stored decryption key to decrypt the data and obtain the original data.

2. According to claim 1, a data sharing optimization method based on Hongmeng distributed data management is characterized in that: The specific implementation of the encryption algorithm in step S1 is: using quantum randomness and chaotic systems to generate a dynamic key sequence; binding data features with keys to resist quantum collision attacks; and performing dynamic quantum gate encryption.

3. According to claim 2, a data sharing optimization method based on Hongmeng distributed data management is characterized in that: The realization of generating a dynamic key sequence by using quantum randomness and chaotic system is to first generate an initial seed key by a quantum random number generator. ,satisfy , and then input the quantum seed S into the mapped chaotic system to generate a chaotic sequence K, which is ,in is the intermediate state value of the chaotic system, K is the dynamic key generated by the chaotic system, is the chaotic system parameter, which is calculated as follows: .

4. According to claim 3, a data sharing optimization method based on Hongmeng distributed data management is characterized in that: After the dynamic key sequence is generated, the data features are bound to the key. The implementation of resisting quantum collision attacks is to divide the original data D into blocks, calculate the quantum-resistant hash for each block and project it into a vector, and perform nonlinear confusion between the chaotic sequence K and the hash feature H. The calculation method is: ,in For bitwise XOR operation, The obfuscated key.

5. According to claim 4, a data sharing optimization method based on Hongmeng distributed data management is characterized in that: Construct a quantum gate operation matrix based on the obfuscated key to achieve encryption: Split into control parameters to generate a dynamic quantum gate set : ,in is a Y-axis rotary gate, CNOT is a controlled NOT gate, and the data block Encoded as quantum states , apply the quantum gate sequence to obtain the encrypted quantum state : ,in Represents the tensor product, and finally performs basis measurement on the encrypted quantum state to generate the classical ciphertext : .

6. According to claim 1, a data sharing optimization method based on Hongmeng distributed data management is characterized in that: In step S1, before data encryption, the data needs to be deduplicated, the semantic features, structural features and time features of the data are extracted, the extracted multi-dimensional features are integrated, the high-dimensional feature vector is converted into a low-dimensional vector using principal component analysis, and a feature index is constructed using a distributed hash table. Each node is responsible for storing part of the feature index information, and the feature vector is mapped to different nodes through a hash function. When new data needs to be stored, its feature vector is calculated and a search is performed in the distributed hash table to find out whether there are similar features. The similarity feature is to set a threshold. When the similarity between the feature vector of new data and the feature vector of stored data exceeds the threshold, it is determined to be duplicate data. The similarity calculation is performed using the cosine similarity method.

7. According to claim 1, a data sharing optimization method based on Hongmeng distributed data management is characterized in that: The implementation of the adaptive data transmission algorithm in step S5 is as follows: constructing an intelligent decision-making model based on reinforcement learning, taking device performance and network conditions as environmental state vectors, transmission mode and transmission rate as action space, and using a multi-objective optimization algorithm to find the optimal solution among multiple objectives of shortest transmission time, highest data integrity, and lowest energy consumption, and determine the optimal data transmission mode and transmission rate.

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