A data sharing whole process tracing method based on digital watermark technology

Abstract

The present invention belongs to the technical field of data watermark traceability, and in particular, relates to a data sharing whole process traceability method based on digital watermark technology, comprising: firstly, obtaining multidimensional structure and unstructured data, and obtaining the target shared data feature space through a differential structural feature extraction integration model; secondly, fusing data owner information and sharing path, generating a final digital watermark through hierarchical fusion and multi-level encryption algorithm, and embedding the target structure data using a differential watermark embedding algorithm; thirdly, constructing a data transmission traceability chain with the help of blockchain and distributed federation algorithm based on relevant information, and configuring a monitoring model; fourthly, monitoring the data transmission status and zero-knowledge proof results of each node in real time, and transmitting them to the analysis control sub-model after analysis by the analysis sub-model; once an abnormal result is received, constructing a leakage scenario node map, and realizing abnormal tracing and marking of fixed nodes with the help of distributed monitoring model and blockchain smart contract.

Description

Technical Field

[0001] The present invention belongs to the technical field of data watermark tracing, and in particular relates to a data sharing whole process tracing method based on digital watermark technology. Background Art

[0002] As the value of data continues to increase, the security and traceability issues in the data sharing process are becoming increasingly important. On the one hand, data copyright protection is a key challenge facing data sharing. In the data sharing scenario, data owners want to ensure that the copyright of their data is clear during the sharing process to prevent the data from being used, copied and disseminated without authorization. For example, if the copyright of scientific research results data, commercial confidential data, etc. is not effectively protected, the data owner may suffer economic losses and infringement of rights and interests. On the other hand, data traceability is crucial to maintaining the security and responsibility of data sharing. When a data leak occurs, being able to quickly and accurately trace the source of the data leak is of great significance for taking timely measures to prevent the further spread of data and hold the relevant responsible parties accountable. For example, in medical data sharing, if patient data is leaked, accurate traceability can help determine which link has a problem, so as to take corresponding remedial measures to protect patient privacy. However, existing technologies have many shortcomings in dealing with these problems.

[0003] From the perspective of data copyright protection, the patent application with publication number CN117118697A discloses a copyright protection method, system, device and medium based on multiple watermarks and blockchain. Although it uses blockchain network and watermark technology to protect copyright, it has some limitations. Although blockchain technology provides decentralized and tamper-proof characteristics, building and maintaining blockchain networks requires certain technical thresholds and costs, which may be difficult for some small businesses or personal data owners to afford. In addition, this method mainly focuses on copyright protection by encrypting and embedding the characteristic information of files. It may not be compatible with complex data structures and multiple data types, and it is difficult to adapt to diverse data sharing scenarios.

[0004] From the perspective of data traceability, the Chinese patent application with authorization announcement number CN110232263B proposes a method for tracing relational data by embedding watermark information to confirm the source of the leak, but this method may have the risk of watermarks being easily tampered with or destroyed. In the process of data sharing, malicious users may use technical means to remove or modify watermark information, resulting in traceability failure. Moreover, this method is only for relational data, and lacks an effective traceability mechanism for non-relational data such as text and images, and its scope of application is relatively narrow.

[0005] The above existing technologies have the following problems: the existing technologies have not yet effectively solved the technical problem of realizing copyright protection and accurate traceability of the entire data sharing process for data of different structures and types. To this end, the present invention provides a method for tracing the entire data sharing process based on digital watermark technology. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention proposes a data sharing whole process tracing method based on digital watermark technology, including: first, obtaining multi-dimensional structure and unstructured data, and obtaining the target shared data feature space through the differential structure feature extraction integration model; second, integrating the data owner information and sharing path, generating the final digital watermark through layered fusion and multi-level encryption algorithm, and embedding the target structure data using the differential watermark embedding algorithm; third, building a data transmission traceability chain with the help of blockchain and distributed federation algorithm based on relevant information, and configuring a monitoring model; fourth, monitoring the data transmission status and zero-knowledge proof results of each node in real time, and transmitting them to the analysis control sub-model after analysis by the analysis sub-model; once an abnormal result is received, a leakage scenario node map is constructed, and the abnormal tracing and marking of fixed nodes are realized with the help of distributed monitoring model and blockchain smart contract, so as to ensure the security and traceability of the whole process of data sharing.

[0007] To achieve the above object, the present invention provides the following technical solutions: A data sharing whole process tracing method based on digital watermark technology, comprising: S1. Obtain multi-dimensional structured and unstructured data, and extract the integrated model through the configured differential structural features to obtain the target structured or unstructured shared data feature space; S2, obtain the data owner information, sharing path, sharing target node information, target structured or unstructured shared data feature space and transmission data security level, obtain the final digital watermark of the corresponding data type through the homomorphic encryption algorithm, and embed it into the corresponding target structure data through the differential watermark embedding algorithm; S3. Based on the data owner information, shared path and target information, and shared target node information, a data transmission traceability chain is constructed through zero-knowledge proof and distributed federation algorithm, and a data transmission distributed monitoring model is configured in the data transmission traceability chain; The data transmission traceability chain includes familiar sharing node communities, first-time sharing node communities, and unfamiliar sharing node communities; S4, real-time monitoring of the transmission status and zero-knowledge proof evidence of the data after watermarking for each node in the data transmission traceability chain, and analysis of the data transmission status and zero-knowledge proof evidence through the analysis sub-model configured for the corresponding node, and transmission of the analysis results to the analysis control sub-model corresponding to the data owner through parameter interaction in the distributed monitoring model; Zero-knowledge proof evidence is obtained dynamically through the community type to which the node belongs in the data transmission traceability chain, the node's corresponding historical data and interaction information, and the security level of the transmitted data; S5. When the analysis and control sub-model receives the abnormal analysis results of each sub-node, it constructs a leakage scenario node map through the abnormal analysis results of the sub-node, and traces the abnormal transmission process to a fixed node through the distributed monitoring model and the smart contract in the blockchain, and marks the corresponding transmission node as abnormal or malicious according to the analysis and tracing results.

[0008] Specifically, the differential structural feature extraction integrated model includes a data type discrimination sub-model, a relational feature extraction sub-model, a text extraction sub-model, an image extraction sub-model and an audio extraction sub-model; Specifically, the steps of acquiring the target structured or unstructured shared data feature space include: S101, according to the multi-dimensional structure and unstructured data, through the configured data type discrimination sub-model, obtain the data type corresponding to the structure or unstructure of the input transmission data; S102, inputting the relational data of the corresponding type into the relational feature extraction sub-model, performing feature extraction of the index relationship and the hierarchical relationship under the corresponding type data, and obtaining a relational feature vector; S103, inputting each type of non-relational data in the corresponding type into the text extraction sub-model, the image extraction sub-model and the audio extraction sub-model in parallel, and simultaneously obtaining a non-relational text feature vector, a non-relational image feature vector and a non-relational audio feature vector; S104, constructing a target structured or unstructured shared data feature space according to the relational feature vector and the non-relational text feature vector, the non-relational image feature vector, and the non-relational audio feature vector; S105. According to the attribute links between relational and non-relational data in the input transmission data, a correlation coefficient algorithm is used to construct an output data type related attribute vector, and the related attribute vector is added to the target structured or unstructured shared data feature space.

[0009] Specifically, the steps for building the data transmission traceability chain include: S301. According to the corresponding node information of the data owner, the shared target node information and the owner's historical shared target node, a data transmission traceability chain is constructed through a community discovery algorithm, and the data transmission traceability chain is divided; S302: if the corresponding node is both a node in the data transmission traceability chain and a historical sharing target node, the corresponding node is divided into a familiar sharing node community; if the corresponding node is only a node in the data transmission traceability chain, the corresponding node is divided into a first-time sharing node community; S303, according to the order of corresponding data transmission in the shared path, the nodes in the data transmission traceability chain are marked by combining the timestamp service of the blockchain with the encrypted hash chain; S304. Configure the path node discrimination model to discriminate the data after adding the watermark during the transmission process on the data transmission traceability chain, and discriminate whether the next transmission node is a node in the familiar shared node community. If so, verify the current node through the configured zero-knowledge proof algorithm and the key established by the corresponding node of the data owner. When the verification is passed, the data is transmitted to the corresponding node.

[0010] Specifically, the steps for building the data transmission traceability chain also include: S305. If the verification fails, the corresponding verification information is fed back to the analysis and control sub-model, and the analysis sub-model configured for the corresponding node is controlled by the analysis and control sub-model to perform verification exception analysis, obtain the exception analysis results, and construct a cause-and-effect diagram of the corresponding node based on the exception analysis results, and feed back the verification exception map of the corresponding node to the analysis and control sub-model.

[0011] S306. According to the abnormality analysis result, the node with the verification abnormality is evaluated through the evaluation algorithm to obtain whether the corresponding node abnormality is a general abnormality or a malicious leakage abnormality, and the corresponding node reputation evaluation score is output. According to the evaluation result and the feedback causal graph, the corresponding node is marked for transmission through the smart contract in the blockchain; S307, when the transmission mark is generally abnormal and the corresponding node reputation evaluation score is greater than the configured trust score threshold, the data watermark encryption information transmitted to the corresponding node is secondary enhanced through the encryption adjustment algorithm configured by the previous node, and the valid verification time interval configured in the historically established key is adjusted; S308. When the transmission is marked as a general exception and the corresponding node reputation assessment score is less than or equal to the configured trust score threshold or is a malicious leakage exception, the corresponding node is deleted from the data transmission traceability chain through the analysis control sub-model, and the shared path corresponding to the current data transmission traceability chain is updated.

[0012] Specifically, the steps for building the data transmission traceability chain also include: S309: When the next transmission node is a node in the first-time shared node community, the current node is verified by the configured zero-knowledge proof algorithm and the enhanced key established between the nodes of the data transmission traceability chain. When the verification is passed, the data is transmitted to the corresponding node; S310: When the verification fails, the corresponding node is deleted from the data transmission traceability chain by analyzing the control sub-model, and the shared path corresponding to the current data transmission traceability chain is updated; S311, if the current corresponding node is neither a node in the first-time shared node community nor a node in the familiar shared node community, classify the corresponding transmission node into the unfamiliar shared node community; S312: If the current node is a node in an unfamiliar shared node community, the current node information is analyzed and evaluated through the analysis sub-model configured by the previous node, and the current node information is fed back to the node corresponding to the data owner, and the analysis control sub-model is used to determine whether the current node is a new transmission node added by the data owner; S313, if yes, then configure the data parameter relationship corresponding to the current node through the data owner, establish a connection relationship between the previous node and the current node, configure the key corresponding to the transmitted data into the connection relationship between the previous node and the current node, and re-divide the current node into the first shared node community; S314, repeating the process of S309-S310, performing data transmission verification and determination of the current node, and performing data sharing or node disconnection according to the determination result.

[0013] Specifically, the steps for building the data transmission traceability chain also include: S315. If the current node is not a new transmission node added by the data owner, the information obtained by scanning the current node is analyzed through the corresponding analysis sub-model of the previous node to obtain whether there is an abnormal state in the history of the current node and obtain the corresponding node reputation evaluation score; S316. If there is no abnormal state in the history of the current node and the node reputation evaluation score is greater than the trust score threshold, the corresponding analysis result information is shared to the analysis control sub-model through the corresponding analysis sub-model of the previous node, and the instruction of whether the data owner to add the node is obtained; S317: When the data owner issues a node adding instruction, the analysis sub-model corresponding to the previous node is controlled by analyzing the control sub-model, and the current node is divided into the first sharing node community according to the same steps as S313, and the step S314 is repeated to make data sharing judgment; S318: When the data owner does not issue a node add instruction or there is an abnormal state in the history or the node reputation evaluation score is less than or equal to the trust score threshold, no connection relationship is established with the current node, the current node is marked as abnormal, and the marking result is saved to the unfamiliar shared node community.

[0014] Specifically, the steps for obtaining zero-knowledge proof evidence in S4 include: S401. Configure a zero-knowledge proof protocol library, divide the basic information and historical data transmission interaction information of the corresponding nodes in the familiar shared node community, the first-time shared node community, and the unfamiliar shared node community according to the data transmission traceability chain, and obtain the trusted data transmission score of the corresponding data transmission node through the evaluation algorithm; S402, constructing a dynamic call factor of a zero-knowledge proof protocol according to the trusted data transmission score of the corresponding data transmission node and the community type to which the corresponding node belongs; S403, using the node historical data and interaction information recorded on the data transmission traceability chain, combined with the data to be transmitted and the knowledge proof protocol dynamic call factor of the current corresponding data transmission node, call the corresponding zero-knowledge proof protocol to generate zero-knowledge proof evidence of the corresponding node; S404, according to the type of community to which the node belongs, and according to the sharing rules, historical interaction patterns, shared security levels, and security assessment factors among the nodes in the community, the generation method of the challenge value is adjusted differentially, so as to obtain the challenge value of the node in the transmission verification process; S405. Build a dynamic verification model based on machine learning, and input the dynamically generated zero-knowledge proof evidence, the dynamic call factor of the zero-knowledge proof protocol, and the challenge value into the dynamic verification model for training to obtain a trained dynamic verification model. Configure the trained dynamic verification model to the connection relationship between different nodes in the data transmission traceability chain to perform dynamic zero-knowledge proof verification.

[0015] Specifically, the configuration of the zero-knowledge proof protocol library includes: a first-level zero-knowledge proof protocol, a second-level zero-knowledge proof protocol and a third-level zero-knowledge proof protocol, and the strength relationship corresponding to the zero-knowledge proof protocol is: a first-level zero-knowledge proof protocol < a second-level zero-knowledge proof protocol < a third-level zero-knowledge proof protocol; the zero-knowledge proof protocol dynamic call factor includes a first-level call factor, a second-level call factor and a third-level call factor; the first-level call factor corresponds to the first-level zero-knowledge proof protocol, the second-level call factor corresponds to the second-level zero-knowledge proof protocol, and the third-level call factor corresponds to the third-level zero-knowledge proof protocol.

[0016] Specifically, the steps for generating the zero-knowledge proof of the corresponding node include: S4031. According to the community type to which the corresponding node belongs, when the corresponding data transmission node is a node in a familiar shared node community, the first-level zero-knowledge proof protocol is called through the first-level calling factor, and the historical successful data transmission interaction information between the corresponding node and the owner node is combined to generate the zero-knowledge proof evidence of the corresponding node; S4032. When the corresponding data transmission node is a node in the first shared node community, the secondary zero-knowledge proof protocol is called through the secondary call factor, and the node registration information, initial verification record and node addition protocol information between the data owner node and the current node recorded in the data transmission traceability chain are combined to generate the corresponding node's zero-knowledge proof evidence; S4033. When the corresponding data transmission node is a node in an unfamiliar shared node community, the three-level zero-knowledge proof protocol is called through the three-level calling factor, and the previous node in the data transmission traceability chain scans the record information, historical abnormal information and data owner node of the current node, and the consent to add instruction information issued by the control sub-model is analyzed to generate zero-knowledge proof evidence of the corresponding node.

[0017] A computer-readable storage medium stores computer instructions, which, when executed, execute a data sharing whole-process tracing method based on digital watermark technology.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. In view of the shortcomings of the prior art, the present invention processes data through a differential structural feature extraction integrated model, so that the method can adapt to various complex data forms, comprehensively cover the feature extraction requirements of different types of data, and break through the limitations of traditional methods on specific data types. Secondly, it integrates multi-dimensional information such as data owner information, shared path, target node information, and combines a hierarchical fusion algorithm with a multi-level data encryption algorithm to generate a digital watermark, so that the watermark contains rich and unique information. At the same time, multi-level encryption improves the security of the watermark, making it difficult to forge or crack, and effectively protecting data copyright. A differential watermark embedding algorithm is used to embed watermarks according to the characteristics of different target structure data, which can not only ensure the concealment of the watermark, but also ensure the stability of the watermark during data transmission and use.

[0019] 2. Aiming at the deficiencies of the prior art, the present invention uses zero-knowledge proof and distributed federated algorithm to construct a data transmission traceability chain. The privacy protection of zero-knowledge proof and the synergistic advantages of distributed federated algorithm ensure that the data transmission is traceable throughout the whole process and protect data privacy, while improving the reliability of the traceability chain. Secondly, a data transmission distributed monitoring model is configured in the traceability chain, which can monitor the data transmission status and zero-knowledge proof results of each node in real time, realize all-round and real-time monitoring of the data transmission process, discover potential problems in time, and ensure the security and compliance of data transmission; thirdly, by setting different node attribute communities in the data transmission traceability chain and calling different zero-knowledge proof protocols according to different node attribute communities, the verification rate between nodes is improved while enhancing the verification security level; 3. The present invention also analyzes the transmission status and proof results through the analysis sub-model configured on each node, and transmits the results to the analysis and control sub-model, so as to realize intelligent analysis of data transmission anomalies, and can quickly and accurately discover abnormal situations. When anomalies occur, a leakage scenario node map is constructed, and the node anomaly is traced with the help of distributed monitoring models and blockchain smart contracts. It can accurately locate abnormal or malicious transmission nodes, clarify the responsible parties, and facilitate timely measures to prevent further data leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of a data sharing whole process tracing method based on digital watermarking technology according to Embodiment 1 of the present invention; Figure 2 This is a structural diagram of the integrated model for extracting differential structural features according to Example 1 of the present invention. DETAILED DESCRIPTION

[0021] Example 1 See also Figure 1 The present invention provides an embodiment: a data sharing whole process tracing method based on digital watermark technology, the steps comprising: S1. Obtain multi-dimensional structured and unstructured data, and extract the integrated model through the configured differential structural features to obtain the target structured or unstructured shared data feature space; For further information, see Figure 2 In this embodiment, the differential structural feature extraction integrated model includes a data type discrimination sub-model, a relational feature extraction sub-model, a text extraction sub-model, an image extraction sub-model and an audio extraction sub-model; further, the data type discrimination sub-model, the relational feature extraction sub-model, the text extraction sub-model, the image extraction sub-model and the audio extraction sub-model in this embodiment are all obtained by constructing the existing pre-trained model, and are integrated into the data processing interface using an integrated algorithm, and the corresponding sub-model is called to process the corresponding data according to the corresponding data type obtained by the secondary classification model, and to extract the corresponding type of data features; Furthermore, the step of acquiring the target structured or unstructured shared data feature space in this embodiment includes: S101, according to the multi-dimensional structure and unstructured data, through the configured data type discrimination sub-model, obtain the data type corresponding to the structure or unstructure of the input transmission data; Furthermore, the data type discrimination sub-model in this embodiment is constructed through a logical classification algorithm, which first discriminates whether the corresponding data is relational data or non-relational data, and on this basis, discriminates whether the corresponding data is text data, image data, audio data, or a table data type with a relational index.

[0022] S102, inputting the relational data of the corresponding type into the relational feature extraction sub-model, performing feature extraction of the index relationship and the hierarchical relationship under the corresponding type data, and obtaining a relational feature vector; S103, inputting each type of non-relational data in the corresponding type into the text extraction sub-model, the image extraction sub-model and the audio extraction sub-model in parallel, and simultaneously obtaining a non-relational text feature vector, a non-relational image feature vector and a non-relational audio feature vector; S104, constructing a target structured or unstructured shared data feature space according to the relational feature vector and the non-relational text feature vector, the non-relational image feature vector, and the non-relational audio feature vector. If the current transmission data has no corresponding type of data, the corresponding feature vector in the shared data feature space is replaced by 0; S105. According to the attribute links between relational and non-relational data in the input transmission data, a correlation coefficient algorithm is used to construct an output data type related attribute vector, and the related attribute vector is added to the target structured or unstructured shared data feature space.

[0023] This process uses pre-trained data type discrimination sub-models and secondary classification models to accurately identify and classify different data types (relational, text, image, audio); then, the corresponding sub-models are called for different types of data to extract features to ensure that the features of each data type can be fully parsed. In particular, for relational data, indexes and hierarchical relationships are extracted to generate relational feature vectors; for non-relational data, text, images, and audio are processed in parallel to obtain corresponding feature vectors. Finally, attribute links are constructed through the correlation coefficient algorithm to integrate all feature vectors into the shared data feature space. This method not only improves the efficiency and accuracy of data processing, but also ensures the integrity and consistency of the feature space.

[0024] S2, obtain the data owner information, sharing path, sharing target node information, target structured or unstructured shared data feature space and transmission data security level, obtain the final digital watermark of the corresponding data type through the homomorphic encryption algorithm, and embed it into the corresponding target structure data through the differential watermark embedding algorithm; Furthermore, in this embodiment, the specific process of embedding the generated watermark into the corresponding target structure data through the differential watermark embedding algorithm is as follows: Non-relational data includes text data, image data, and audio data; For text data: In addition to embedding watermarks in the tag attributes, metadata areas, and data structure hierarchical relationships corresponding to the text data, watermarks are also embedded in the annotations and descriptions of the data. At the same time, the schema information of XML or JSON data is used to hide the watermark in the correspondence between the schema definition and the data instance. For image data: In addition to embedding watermarks in the DCT domain and visual perception domain, watermarks are also embedded in the phase spectrum of the image. Phase information plays a key role in image reconstruction. By embedding watermarks in the phase spectrum, the robustness and imperceptibility of the watermark are improved. At the same time, auxiliary watermark information is embedded in the metadata area of ​​the image (such as shooting equipment, time, etc.) to enhance the concealment of the watermark.

[0025] For audio data: In addition to embedding watermarks in the time-frequency domain and auditory perception sensitive frequency bands, watermarks are also embedded in the echo hidden domain of the audio. The watermark is hidden by adjusting the echo characteristics of the audio signal, and the insensitivity of the human auditory system to echo is used to improve the imperceptibility of the watermark.

[0026] S3. Based on the data owner information, shared path and target information, and shared target node information, a data transmission traceability chain is constructed through zero-knowledge proof and distributed federation algorithm, and a data transmission distributed monitoring model is configured in the data transmission traceability chain; Furthermore, the distributed monitoring model in this embodiment includes an analysis control sub-model and M analysis sub-models; Furthermore, the analysis and control sub-model in this embodiment is constructed by a reinforcement learning algorithm, and the M analysis sub-models are constructed by a comprehensive evaluation model; Furthermore, in this embodiment, the analysis control sub-model and the M analysis sub-models are integrated through a federation algorithm, and the analysis control sub-model is deployed in the node where the data owner is located, and the M analysis sub-models are deployed in the corresponding M target transmission nodes in the data transmission traceability chain; Furthermore, the steps of constructing the data transmission traceability chain in this embodiment include: S301. According to the corresponding node information of the data owner, the shared target node information and the owner's historical shared target node, a data transmission traceability chain is constructed through a community discovery algorithm, and the data transmission traceability chain is divided; S302: if the corresponding node is both a node in the data transmission traceability chain and a historical sharing target node, the corresponding node is divided into a familiar sharing node community; if the corresponding node is only a node in the data transmission traceability chain, the corresponding node is divided into a first-time sharing node community; S303, according to the order of corresponding data transmission in the shared path, the nodes in the data transmission traceability chain are marked by combining the timestamp service of the blockchain with the encrypted hash chain; S304, configure the path node discrimination model to discriminate the data after adding the watermark during the transmission process on the data transmission traceability chain, and discriminate whether the next transmission node is a node in the familiar shared node community. If so, verify the current node through the configured zero-knowledge proof algorithm and the key established by the corresponding node of the data owner. When the verification is passed, the data is transmitted to the corresponding node.

[0027] S305. If the verification fails, the corresponding verification information is fed back to the analysis and control sub-model, and the analysis sub-model configured for the corresponding node is controlled by the analysis and control sub-model to perform verification exception analysis, obtain the exception analysis results, and build a cause-and-effect diagram of the corresponding node based on the exception analysis results, and feed back the verification exception map of the corresponding node to the analysis and control sub-model.

[0028] S306. According to the abnormality analysis result, the node with the verification abnormality is evaluated through the evaluation algorithm to obtain whether the corresponding node abnormality is a general abnormality or a malicious leakage abnormality, and the corresponding node reputation evaluation score is output. According to the evaluation result and the feedback causal graph, the corresponding node is marked for transmission through the smart contract in the blockchain; S307, when the transmission mark is generally abnormal and the corresponding node reputation evaluation score is greater than the configured trust score threshold, the data watermark encryption information transmitted to the corresponding node is secondary enhanced through the encryption adjustment algorithm configured by the previous node, and the valid verification time interval configured in the historically established key is adjusted; Furthermore, in this embodiment, the data watermark encryption information of the node is enhanced twice by increasing the number of encryption rounds. For example, originally a 128-bit key length and 10 rounds of encryption are used, and the number of rounds is increased to 14 during the secondary enhancement. Furthermore, in this embodiment, a new key for secondary encryption is generated through a key derivation function (KDF) based on the original encryption key; S308. When the transmission is marked as a general exception and the corresponding node reputation assessment score is less than or equal to the configured trust score threshold or is a malicious leakage exception, the corresponding node is deleted from the data transmission traceability chain through the analysis control sub-model, and the shared path corresponding to the current data transmission traceability chain is updated.

[0029] S309: When the next transmission node is a node in the first-time shared node community, the current node is verified by the configured zero-knowledge proof algorithm and the enhanced key established between the nodes of the data transmission traceability chain. When the verification is passed, the data is transmitted to the corresponding node; S310: When the verification fails, the corresponding node is deleted from the data transmission traceability chain by analyzing the control sub-model, and the shared path corresponding to the current data transmission traceability chain is updated; S311, if the current corresponding node is neither a node in the first-time shared node community nor a node in the familiar shared node community, classify the corresponding node into the unfamiliar shared node community; S312: If the current node is a node in an unfamiliar shared node community, the current node information is analyzed and evaluated through the analysis sub-model configured by the previous node, and the current node information is fed back to the node corresponding to the data owner, and the analysis control sub-model is used to determine whether the current node is a new transmission node added by the data owner; S313, if yes, then configure the data parameter relationship corresponding to the current node through the data owner, establish a connection relationship between the previous node and the current node, configure the key corresponding to the transmitted data into the connection relationship between the previous node and the current node, and re-divide the current node into the first shared node community; S314, repeating the process of S309-S310, performing data transmission verification and determination of the current node, and performing data sharing or node disconnection according to the determination result.

[0030] S315. If the current node is not a new transmission node added by the data owner, the information obtained by scanning the current node is analyzed through the corresponding analysis sub-model of the previous node to obtain whether there is an abnormal state in the history of the current node and obtain the corresponding node reputation evaluation score; S316. If there is no abnormal state in the history of the current node and the node reputation evaluation score is greater than the trust score threshold, the corresponding analysis result information is shared to the analysis control sub-model through the corresponding analysis sub-model of the previous node, and the instruction of whether the data owner to add the node is obtained; S317: When the data owner issues a node adding instruction, the analysis sub-model corresponding to the previous node is controlled by analyzing the control sub-model, and the current node is divided into the first sharing node community according to the same steps as S313, and the step S314 is repeated to make data sharing judgment; S318: When the data owner does not issue a node add instruction or there is an abnormal state in the history or the node reputation evaluation score is less than or equal to the trust score threshold, no connection relationship is established with the current node, the current node is marked as abnormal, and the marking result is saved to the unfamiliar shared node community.

[0031] Furthermore, in this embodiment, the data transmission traceability chain includes a familiar sharing node community, a first-time sharing node community, and an unfamiliar sharing node community; This process uses a series of rigorous and efficient mechanisms to comprehensively guarantee the security and controllability of data on the transmission traceability chain. First, based on whether the node is a historical shared target node, the nodes in the data transmission traceability chain are accurately divided into familiar shared node communities and first-time shared node communities to achieve refined management. Such classification makes it easy to adopt differentiated verification and processing strategies based on the historical sharing relationship of the nodes to improve the security and efficiency of data transmission; for example, for nodes familiar with the shared node community, verification and data transmission can be quickly completed with the existing historical sharing records, trust relationships, and keys; secondly, for unfamiliar nodes, by analyzing and evaluating their information, it is determined whether they are newly added nodes by the data owner, and whether to establish a connection based on their historical status and reputation scores, which not only ensures the openness of the traceability chain, but also effectively prevents potential risks. Furthermore, the nodes are marked by combining the timestamp service of the blockchain with the encrypted hash chain to ensure that the data transmission sequence is traceable and the node information cannot be tampered with; at the same time, the path node discrimination model is configured, and a multi-level verification system is constructed in combination with the zero-knowledge proof algorithm and the corresponding key. Different keys are used for verification of nodes in different communities to enhance pertinence and security, and effectively prevent illegal node access and data leakage. When the verification fails, we deeply analyze the anomaly and build a cause-and-effect diagram to uncover the root cause. We adopt differentiated processing strategies based on the anomaly type and node reputation to maintain the security and stability of the traceability chain.

[0032] S4, real-time monitoring of the transmission status and zero-knowledge proof evidence of the data after watermarking for each node in the data transmission traceability chain, and analysis of the data transmission status and zero-knowledge proof evidence through the analysis sub-model configured for the corresponding node, and transmission of the analysis results to the analysis control sub-model corresponding to the data owner through parameter interaction in the distributed monitoring model; Furthermore, the step of obtaining zero-knowledge proof evidence in S4 in this embodiment includes: S401. Configure a zero-knowledge proof protocol library, divide the basic information and historical data transmission interaction information of the corresponding nodes in the familiar shared node community, the first-time shared node community, and the unfamiliar shared node community according to the data transmission traceability chain, and obtain the trusted data transmission score of the corresponding data transmission node through the evaluation algorithm; S402, constructing a dynamic call factor of a zero-knowledge proof protocol according to the trusted data transmission score of the corresponding data transmission node and the community type to which the corresponding node belongs; S403, using the node historical data and interaction information recorded on the data transmission traceability chain, combined with the data to be transmitted and the knowledge proof protocol dynamic call factor of the current corresponding data transmission node, call the corresponding zero-knowledge proof protocol to generate zero-knowledge proof evidence of the corresponding node; Furthermore, in this embodiment, the process of performing protocol selection calling includes: When selecting a zero-knowledge proof protocol, in addition to the encryption and security mechanisms of the system, dynamic adjustments are also made based on the type of community the node is in. For example, for nodes that are familiar with a shared node community, since the trust between nodes is relatively high and data interactions are frequent, a more efficient and concise zero-knowledge proof protocol is selected, such as a zero-knowledge proof protocol based on homomorphic encryption. This protocol can quickly complete the proof process while ensuring security, thereby improving data transmission efficiency. For nodes that are sharing a node community for the first time or in an unfamiliar shared node community, since the trust in the node is low, a more secure non-interactive and concise verifiable proof protocol based on zero-knowledge proof is selected. This protocol can provide higher security protection and prevent attacks from malicious nodes.

[0033] Based on the zero-knowledge proof protocol selected by the above community division results, when determining the statement to be proved, the prover will not only cover the legitimacy statement of the data source and sharing rules, but also add specific statements related to the node community, making the data transmission verification process more secure and rigorous; For example, for nodes that are familiar with the shared node community, the statement may include content related to the specific sharing history and trust relationship of the community; for nodes that are sharing the node community for the first time, the statement focuses on proving their legitimacy and compliance as newly joined nodes; for nodes that are unfamiliar with the shared node community, the statement needs to emphasize the security assessment and verification of unknown nodes.

[0034] Furthermore, the process of generating evidence in this embodiment includes: Introducing blockchain technology to assist in generating zero-knowledge proof evidence. The proving party uses the node historical data and interaction information recorded on the blockchain, combined with the current data to be transmitted, to generate evidence. For example, for nodes that are familiar with the shared node community, the historical successful interaction records between nodes in the community recorded on the blockchain can be used as part of the evidence generation to increase the credibility of the proof; for nodes that are sharing the node community for the first time, evidence can be generated with the help of the node registration information and initial verification records on the blockchain; for nodes in unfamiliar shared node communities, the security assessment information and historical abnormality records of the node can be queried through the blockchain and integrated into the evidence generation process to more comprehensively prove their own security.

[0035] S404, according to the type of community to which the node belongs, and according to the sharing rules, historical interaction patterns, shared security levels, and security assessment factors among the nodes in the community, the generation method of the challenge value is adjusted differentially, so as to obtain the challenge value of the node in the transmission verification process; Furthermore, the security assessment factors in this embodiment include: node reputation, compliance factors, emergency response capabilities, and data sensitivity; Among them, the node reputation is measured based on the node's historical performance in the data transmission traceability chain; For example, whether the node has completed data transmission tasks on time and accurately in the past, whether there is a record of data loss, tampering or leakage, and whether it complies with the agreed rules and protocols during the interaction with other nodes; nodes with high credibility are more trustworthy in data transmission, while nodes with low credibility may pose higher security risks.

[0036] The compliance factor is to check whether the node complies with relevant laws and regulations, industry standards and specifications; for example, in terms of data privacy protection, whether the requirements of the data protection law are complied with, and whether the collection, use and storage of user data are legally authorized; in terms of data transmission security, whether it complies with the network security level protection system, etc. Nodes with high compliance can better ensure the legitimacy and security of data during the data transmission process.

[0037] Emergency response capability is to evaluate the emergency response capability of the node when facing security incidents; including whether a comprehensive emergency plan has been formulated, whether emergency drills are conducted regularly, and whether measures can be taken quickly and effectively to deal with security incidents after they occur, so as to reduce the impact of security incidents on data transmission.

[0038] Data sensitivity is to assess the security risk of the data transmitted by the node based on its sensitivity; for nodes that transmit sensitive data (such as personal privacy information, commercial secrets, etc.), higher security protection measures and stricter security assessments are required. The higher the data sensitivity, the higher the security requirements for the node, and the corresponding security assessment factor will have a greater weight.

[0039] Furthermore, in this embodiment, the process of obtaining the challenge value of the node during the transmission verification process includes: For nodes that are familiar with the shared node community, the generation of challenge values ​​can be obtained based on the shared rules and historical interaction patterns within the community. For example, the challenge value is related to the common data operation types and frequencies within the community to verify the prover's compliance with the community rules; For nodes that are sharing the node community for the first time, the generation of challenge values ​​focuses on verifying the node's understanding and execution of basic sharing rules and security requirements, which may involve some common security verification issues; For nodes in unfamiliar shared node communities, the generation of challenge values ​​is more rigorous and comprehensive. Combined with the security assessment information and potential risk points of the node, targeted challenge values ​​are generated to deeply verify the security of the node.

[0040] S405. Build a dynamic verification model based on machine learning, and input the dynamically generated zero-knowledge proof evidence, the dynamic call factor of the zero-knowledge proof protocol, and the challenge value into the dynamic verification model for training to obtain a trained dynamic verification model. Configure the trained dynamic verification model to the connection relationship between different nodes in the data transmission traceability chain to perform dynamic zero-knowledge proof verification.

[0041] Furthermore, in this embodiment, a dynamic verification model based on the node community type is constructed, and the dynamic verification model learns the normal verification modes and abnormal situation characteristics of different node communities. During the verification process, the response of the prover and the node community related information are input into the machine learning model, and the model determines whether the verification is passed based on the learned knowledge. For example, for a familiar shared node community, the model learns the normal verification response mode and common abnormal situations in the community, such as a specific response value range and abnormal response characteristics; for the first shared node community and the unfamiliar shared node community, the model learns their corresponding verification modes and abnormal characteristics respectively. With the assistance of the machine learning model, the verification result can be judged more accurately, especially some potential abnormal situations can be identified.

[0042] Furthermore, in this embodiment, the zero-knowledge proof protocol library is configured to include: a first-level zero-knowledge proof protocol, a second-level zero-knowledge proof protocol and a third-level zero-knowledge proof protocol, and the strength relationship corresponding to the zero-knowledge proof protocol is: a first-level zero-knowledge proof protocol < a second-level zero-knowledge proof protocol < a third-level zero-knowledge proof protocol; the zero-knowledge proof protocol dynamic call factor includes a first-level call factor, a second-level call factor and a third-level call factor; the first-level call factor corresponds to the first-level zero-knowledge proof protocol, the second-level call factor corresponds to the second-level zero-knowledge proof protocol, and the third-level call factor corresponds to the third-level zero-knowledge proof protocol.

[0043] Furthermore, in this embodiment, the step of generating the zero-knowledge proof evidence of the corresponding node includes: S4031. According to the community type to which the corresponding node belongs, when the corresponding data transmission node is a node in a familiar shared node community, the first-level zero-knowledge proof protocol is called through the first-level calling factor, and the historical successful data transmission interaction information between the corresponding node and the owner node is combined to generate the zero-knowledge proof evidence of the corresponding node; S4032. When the corresponding data transmission node is a node in the first shared node community, the secondary zero-knowledge proof protocol is called through the secondary call factor, and the node registration information, initial verification record and node addition protocol information between the data owner node and the current node recorded in the data transmission traceability chain are combined to generate the corresponding node's zero-knowledge proof evidence; S4033. When the corresponding data transmission node is a node in an unfamiliar shared node community, the three-level zero-knowledge proof protocol is called through the three-level calling factor, and the previous node in the data transmission traceability chain scans the record information, historical abnormal information and data owner node of the current node, and the consent to add instruction information issued by the control sub-model is analyzed to generate zero-knowledge proof evidence of the corresponding node.

[0044] Furthermore, the zero-knowledge proof evidence in this embodiment is dynamically obtained through the community type to which the node belongs in the data transmission traceability chain, the historical data and interaction information corresponding to the node, and the security level of the transmitted data; In the process of obtaining zero-knowledge proof evidence, the trusted transmission score is evaluated based on the node community type, basic information and historical interaction information, a dynamic call factor is constructed, and blockchain technology is used to assist in evidence generation. At the same time, the challenge value generation method is adjusted according to the community type. These measures enhance the pertinence and credibility of the evidence. In terms of protocol selection, the system encryption security mechanism and the dynamic adjustment of the node community type are combined to balance efficiency and security to meet the needs of different trust scenarios. A dynamic verification model based on machine learning is constructed to learn the verification modes and abnormal characteristics of different communities, improve the accuracy of verification, and effectively identify potential anomalies. The hierarchical configuration of the zero-knowledge proof protocol library and the design of the corresponding call factor make the protocol call more hierarchical and precise. Overall, from evidence generation, protocol selection to verification, the process fully guarantees the security, accuracy and efficiency of zero-knowledge proof in the data transmission traceability chain, and improves the adaptability and risk prevention and control capabilities of the entire system to different node communities.

[0045] S5. When the analysis and control sub-model receives the abnormal analysis results of each sub-node, it constructs a leakage scenario node map through the abnormal analysis results of the sub-node, and traces the abnormal transmission process to a fixed node through the distributed monitoring model and the smart contract in the blockchain, and marks the corresponding transmission node as abnormal or malicious according to the analysis and tracing results.

[0046] Furthermore, the abnormal or malicious marks in this embodiment are specifically: General abnormality mark: Applicable to abnormal situations that have a certain impact on data transmission but are not caused by malicious intent. For example, due to network fluctuations, temporary system failures, etc., the node may fail verification or the data transmission status may be abnormal during the data transmission process. This type of mark indicates that the abnormal situation of the node is relatively minor and normal data transmission may be restored after appropriate processing.

[0047] Malicious leakage anomaly marking: For situations where there are clear signs that the node intentionally leaks data or violates sharing rules, posing a serious threat to data security. For example, the node bypasses the security verification mechanism and transmits data to an unauthorized third party, or tampers with data watermark encryption information to cover up illegal behavior. Once marked as such, the node will be considered a serious threat and will be decisively deleted from the data transmission traceability chain to prevent further data leakage.

[0048] Potential risk tag: This tag is given when certain behaviors or states of a node do not constitute obvious abnormalities or malicious intent, but there is a potential risk of data leakage. For example, a node in an unfamiliar shared node community may have historical data that does not show abnormalities, but its reputation score is at a low level, close to the trust threshold. At this time, it can be marked as a potential risk so that it can be monitored and evaluated more closely in the future.

[0049] Repeated anomaly marking: If a node has the same type of anomaly multiple times over a period of time, it can be marked as a repeated anomaly. This indicates that the node may have a systemic problem and requires more in-depth inspection and repair to avoid continuous interference with data transmission.

[0050] Security vulnerability marking: This marking is performed when a node has a security vulnerability that may be maliciously exploited to affect data transmission security. For example, a security assessment may reveal weaknesses in the node's encryption algorithm, or the node's identity authentication mechanism may be bypassed.

[0051] Example 2 An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements a data sharing whole process tracing method based on digital watermark technology when executing the computer program.

[0052] A computer-readable storage medium stores computer instructions, which, when executed, execute a data sharing whole-process tracing method based on digital watermark technology.

[0053] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the scope of protection of the purpose of the present invention and the claims, and all of these are within the protection of the present invention.

[0054] If the disclosed technical solution involves personal information, the product using the disclosed technical solution has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the disclosed technical solution involves sensitive personal information, the product using the disclosed technical solution has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A data sharing whole process tracing method based on digital watermark technology, characterized in that: include: S1. Obtain multi-dimensional structured and unstructured data, and extract the integrated model through the configured differential structural features to obtain the target structured or unstructured shared data feature space; S2, obtain the data owner information, sharing path, sharing target node information, target structured or unstructured shared data feature space and transmission data security level, obtain the final digital watermark of the corresponding data type through the homomorphic encryption algorithm, and embed it into the corresponding target structure data through the differential watermark embedding algorithm; S3. Based on the data owner information, shared path and target information, and shared target node information, a data transmission traceability chain is constructed through zero-knowledge proof and distributed federation algorithm, and a data transmission distributed monitoring model is configured in the data transmission traceability chain; The data transmission traceability chain includes a familiar sharing node community, a first-time sharing node community, and an unfamiliar sharing node community; S4, real-time monitoring of the transmission status and zero-knowledge proof evidence of the data after watermarking for each node in the data transmission traceability chain, and analysis of the data transmission status and zero-knowledge proof evidence through the analysis sub-model configured for the corresponding node, and transmission of the analysis results to the analysis control sub-model corresponding to the data owner through parameter interaction in the distributed monitoring model; The zero-knowledge proof evidence is dynamically obtained through the community type to which the node belongs in the data transmission traceability chain, the historical data and interaction information corresponding to the node, and the security level of the transmitted data; S5. When the analysis and control sub-model receives the abnormal analysis results of each sub-node, it constructs a leakage scenario node map through the abnormal analysis results of the sub-node, and traces the abnormal transmission process to a fixed node through the distributed monitoring model and the smart contract in the blockchain, and marks the corresponding transmission node as abnormal or malicious according to the analysis and tracing results.

2. A data sharing whole process tracing method based on digital watermark technology as claimed in claim 1, characterized in that: The differential structural feature extraction integrated model includes a data type discrimination sub-model, a relational feature extraction sub-model, a text extraction sub-model, an image extraction sub-model and an audio extraction sub-model; The step of acquiring the target structured or unstructured shared data feature space includes: S101, according to the multi-dimensional structure and unstructured data, through the configured data type discrimination sub-model, obtain the data type corresponding to the structure or unstructure of the input transmission data; S102, inputting the relational data of the corresponding type into the relational feature extraction sub-model, performing feature extraction of the index relationship and the hierarchical relationship under the corresponding type data, and obtaining a relational feature vector; S103, inputting each type of non-relational data in the corresponding type into the text extraction sub-model, the image extraction sub-model and the audio extraction sub-model in parallel, and simultaneously obtaining a non-relational text feature vector, a non-relational image feature vector and a non-relational audio feature vector; S104, constructing a target structured or unstructured shared data feature space according to the relational feature vector and the non-relational text feature vector, the non-relational image feature vector, and the non-relational audio feature vector; S105. According to the attribute links between relational and non-relational data in the input transmission data, a correlation coefficient algorithm is used to construct an output data type related attribute vector, and the related attribute vector is added to the target structured or unstructured shared data feature space.

3. A data sharing whole process tracing method based on digital watermark technology as claimed in claim 2, characterized in that: The steps of constructing the data transmission traceability chain include: S301. According to the corresponding node information of the data owner, the shared target node information and the owner's historical shared target node, a data transmission traceability chain is constructed through a community discovery algorithm, and the data transmission traceability chain is divided; S302: if the corresponding node is both a node in the data transmission traceability chain and a historical sharing target node, the corresponding node is divided into a familiar sharing node community; if the corresponding node is only a node in the data transmission traceability chain, the corresponding node is divided into a first-time sharing node community; S303, according to the order of corresponding data transmission in the shared path, the nodes in the data transmission traceability chain are marked by combining the timestamp service of the blockchain with the encrypted hash chain; S304. Configure the path node discrimination model to discriminate the data after adding the watermark during the transmission process on the data transmission traceability chain, and discriminate whether the next transmission node is a node in the familiar shared node community. If so, verify the current node through the configured zero-knowledge proof algorithm and the key established by the corresponding node of the data owner. When the verification is passed, the data is transmitted to the corresponding node.

4. A data sharing whole process tracing method based on digital watermark technology as claimed in claim 3, characterized in that: The steps of constructing the data transmission traceability chain also include: S305. If the verification fails, the corresponding verification information is fed back to the analysis and control sub-model, and the analysis sub-model configured for the corresponding node is controlled by the analysis and control sub-model to perform verification exception analysis, obtain the exception analysis result, and construct a cause-effect graph of the corresponding node according to the exception analysis result, and feed back the verification exception graph of the corresponding node to the analysis and control sub-model; S306. According to the abnormality analysis result, the node with the verification abnormality is evaluated through the evaluation algorithm to obtain whether the corresponding node abnormality is a general abnormality or a malicious leakage abnormality, and the corresponding node reputation evaluation score is output. According to the evaluation result and the feedback causal graph, the corresponding node is marked for transmission through the smart contract in the blockchain; S307, when the transmission mark is generally abnormal and the corresponding node reputation evaluation score is greater than the configured trust score threshold, the data watermark encryption information transmitted to the corresponding node is secondary enhanced through the encryption adjustment algorithm configured by the previous node, and the valid verification time interval configured in the historically established key is adjusted; S308. When the transmission is marked as a general exception and the corresponding node reputation assessment score is less than or equal to the configured trust score threshold or is a malicious leakage exception, the corresponding node is deleted from the data transmission traceability chain through the analysis control sub-model, and the shared path corresponding to the current data transmission traceability chain is updated.

5. A data sharing whole process tracing method based on digital watermark technology as claimed in claim 4, characterized in that: The steps of constructing the data transmission traceability chain also include: S309: When the next transmission node is a node in the first-time shared node community, the current node is verified by the configured zero-knowledge proof algorithm and the enhanced key established between the nodes of the data transmission traceability chain. When the verification is passed, the data is transmitted to the corresponding node; S310: When the verification fails, the corresponding node is deleted from the data transmission traceability chain by analyzing the control sub-model, and the shared path corresponding to the current data transmission traceability chain is updated; S311, if the current corresponding node is neither a node in the first-time shared node community nor a node in the familiar shared node community, classify the corresponding node into the unfamiliar shared node community; S312: If the current node is a node in an unfamiliar shared node community, the current node information is analyzed and evaluated through the analysis sub-model configured by the previous node, and the current node information is fed back to the node corresponding to the data owner, and the analysis control sub-model is used to determine whether the current node is a new transmission node added by the data owner; S313, if yes, then configure the data parameter relationship corresponding to the current node through the data owner, establish a connection relationship between the previous node and the current node, configure the key corresponding to the transmitted data into the connection relationship between the previous node and the current node, and re-divide the current node into the first shared node community; S314, repeating the process of S309-S310, performing data transmission verification and determination of the current node, and performing data sharing or node disconnection according to the determination result.

6. A data sharing whole process tracing method based on digital watermark technology as claimed in claim 5, characterized in that: The steps of constructing the data transmission traceability chain also include: S315. If the current node is not a new transmission node added by the data owner, the information obtained by scanning the current node is analyzed through the corresponding analysis sub-model of the previous node to obtain whether there is an abnormal state in the history of the current node and obtain the corresponding node reputation evaluation score; S316. If there is no abnormal state in the history of the current node and the node reputation evaluation score is greater than the trust score threshold, the corresponding analysis result information is shared to the analysis control sub-model through the corresponding analysis sub-model of the previous node, and the instruction of whether the data owner to add the node is obtained; S317: When the data owner issues a node adding instruction, the analysis sub-model corresponding to the previous node is controlled by analyzing the control sub-model, and the current node is divided into the first sharing node community according to the same steps as S313, and the step S314 is repeated to make data sharing judgment; S318: When the data owner does not issue a node add instruction or there is an abnormal state in the history or the node reputation evaluation score is less than or equal to the trust score threshold, no connection relationship is established with the current node, the current node is marked as abnormal, and the marking result is saved to the unfamiliar shared node community.

7. A data sharing whole process tracing method based on digital watermark technology as claimed in claim 6, characterized in that: The steps of obtaining the zero-knowledge proof evidence in S4 include: S401, configuring a zero-knowledge proof protocol library, dividing the basic information and historical data transmission interaction information of corresponding nodes in the familiar sharing node community, the first-time sharing node community and the unfamiliar sharing node community according to the data transmission traceability chain, and obtaining the trusted data transmission score of the corresponding data transmission node through an evaluation algorithm; S402, constructing a dynamic call factor of a zero-knowledge proof protocol according to the trusted data transmission score of the corresponding data transmission node and the community type to which the corresponding node belongs; S403, using the node historical data and interaction information recorded on the data transmission traceability chain, combined with the data to be transmitted and the knowledge proof protocol dynamic call factor of the current corresponding data transmission node, call the corresponding zero-knowledge proof protocol to generate zero-knowledge proof evidence of the corresponding node; S404, according to the type of community to which the node belongs, and according to the sharing rules, historical interaction patterns, shared security levels, and security assessment factors among the nodes in the community, the generation method of the challenge value is adjusted differentially, so as to obtain the challenge value of the node in the transmission verification process; S405. Build a dynamic verification model based on machine learning, and input the dynamically generated zero-knowledge proof evidence, the dynamic call factor of the zero-knowledge proof protocol, and the challenge value into the dynamic verification model for training to obtain a trained dynamic verification model. Configure the trained dynamic verification model to the connection relationship between different nodes in the data transmission traceability chain to perform dynamic zero-knowledge proof verification.

8. A data sharing whole process tracing method based on digital watermark technology as claimed in claim 7, characterized in that: The configured zero-knowledge proof protocol library includes: a first-level zero-knowledge proof protocol, a second-level zero-knowledge proof protocol and a third-level zero-knowledge proof protocol, and the strength relationship corresponding to the zero-knowledge proof protocol is: a first-level zero-knowledge proof protocol < a second-level zero-knowledge proof protocol < a third-level zero-knowledge proof protocol; the zero-knowledge proof protocol dynamic call factor includes a first-level call factor, a second-level call factor and a third-level call factor; the first-level call factor corresponds to the first-level zero-knowledge proof protocol, the second-level call factor corresponds to the second-level zero-knowledge proof protocol, and the third-level call factor corresponds to the third-level zero-knowledge proof protocol.

9. A data sharing whole process tracing method based on digital watermark technology as claimed in claim 8, characterized in that: The step of generating the zero-knowledge proof evidence of the corresponding node includes: S4031. According to the community type to which the corresponding node belongs, when the corresponding data transmission node is a node in a familiar shared node community, the first-level zero-knowledge proof protocol is called through the first-level calling factor, and the historical successful data transmission interaction information between the corresponding node and the owner node is combined to generate the zero-knowledge proof evidence of the corresponding node; S4032. When the corresponding data transmission node is a node in the first shared node community, the secondary zero-knowledge proof protocol is called through the secondary call factor, and the node registration information, initial verification record and node addition protocol information between the data owner node and the current node recorded in the data transmission traceability chain are combined to generate the corresponding node's zero-knowledge proof evidence; S4033. When the corresponding data transmission node is a node in an unfamiliar shared node community, the three-level zero-knowledge proof protocol is called through the three-level calling factor, and the previous node in the data transmission traceability chain scans the record information, historical abnormal information and data owner node of the current node, and the consent to add instruction information issued by the control sub-model is analyzed to generate zero-knowledge proof evidence of the corresponding node.

10. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, a data sharing whole process tracing method based on digital watermark technology as described in any one of claims 1 to 9 is executed.

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