A railway disaster prevention monitoring data sharing method and system

Abstract

The present invention proposes a method and system for sharing railway disaster prevention monitoring data. The method enhances the reliability of proof and system scalability through innovative distributed verification mechanism, chain proof structure and cross-chain design. It combines differential privacy, de-identification technology with zero-knowledge proof (ZKP), anonymous credentials and blind signature technology, which not only effectively prevents the leakage of sensitive information, but also ensures data security even in multi-node verification and data sharing environments. The system includes data classification, distributed proof chain, cross-chain communication, identity anonymity and privacy protection modules, and fully supports data sharing between heterogeneous chains. Compared with the mainstream homogeneous chain cross-chain solution, the present invention makes up for the shortcomings of the existing cross-chain method, which is open and transparent but easy to expose user identities, and provides a more comprehensive and secure solution for the privacy protection and cross-chain sharing of railway disaster prevention monitoring data.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a railway disaster prevention monitoring data sharing method and system. Background Art

[0002] With the rapid development of railway transportation, the importance of railway disaster prevention monitoring systems has become increasingly prominent. These systems deploy various sensors and monitoring equipment along the railway to collect key data including geological structure, meteorological conditions, and railway facility status in real time, providing important support for railway safety operations.

[0003] At present, the sharing of railway disaster prevention monitoring data mainly relies on the traditional centralized data management platform, which has significant limitations. Centralized systems are prone to single point failure problems. Once attacked or data leaked, it will cause significant losses. At the same time, data may also be illegally obtained or tampered with during transmission and storage. In the process of data sharing, sensitive information such as the real-time status of railway facilities, geological structures, key infrastructure and other monitoring data may be obtained by unauthorized third parties, which seriously threatens data privacy. In addition, traditional data verification methods often rely on complex encryption algorithms and centralized verification agencies. The verification process is cumbersome and inefficient, and it is difficult to meet real-time requirements.

[0004] Furthermore, existing railway disaster prevention monitoring data usually comes from different sensors, equipment and systems. However, these data have great differences in format, structure and storage method. At the same time, these data may be distributed in different organizations or institutions, and each is managed by different blockchain platforms. Among them, a single blockchain platform is difficult to meet the needs of cross-system and cross-platform data sharing, because there are still technical barriers and interoperability issues between different blockchain platforms.

[0005] Therefore, in view of the shortcomings of existing technologies, it is necessary to provide a method for sharing railway disaster prevention monitoring data safely and effectively. Summary of the invention

[0006] In view of the deficiencies in the prior art, the object of the present invention is to provide a railway disaster prevention monitoring data sharing method and system, so as to provide a method for sharing railway disaster prevention monitoring data safely and effectively.

[0007] A first aspect of the present invention is to provide a railway disaster prevention monitoring data sharing method, the method comprising:

[0008] A method for sharing railway disaster prevention monitoring data, comprising the following steps:

[0009] In the initial setting stage of the system: cross-chain verification nodes are selected to be distributed on multiple heterogeneous blockchain platforms to generate public and private key pairs corresponding to the data publisher and the cross-chain verification nodes, respectively, and a corresponding cross-chain protocol is formulated based on the public and private key pairs to generate corresponding public parameters based on the cross-chain protocol;

[0010] In the segmented encryption and identity anonymization stage: the data publisher classifies and grades the railway disaster prevention monitoring data according to sensitivity to formulate a corresponding sharing strategy; the data to be shared is segmented and encrypted using a symmetric encryption algorithm; differential privacy and de-identification schemes are applied to prevent sensitive information leakage, and blind signatures are used to and zero-knowledge proof Prove that they have legitimate anonymous credentials ;

[0011] In the cross-chain communication and segment verification stage: the encrypted data fragments, data hashes H ( D ), encrypted identity information , blind signature and zero-knowledge proof The data is sent to the cross-chain nodes for segment verification. Each cross-chain node verifies the data fragment of its own chain and communicates with other chain nodes through the cross-chain protocol to verify the consistency of the cross-chain data.

[0012] In the cross-chain chain proof generation stage: from the cross-chain node Start by verifying and generating chain proofs one by one , each node links the proof of the previous node with the current verification result to form , and pass the signature to the next node until the last node Generate the final cross-chain chain proof;

[0013] In the cross-chain chain proof verification stage: the verifier uses the public parameters and the public keys of each node to gradually verify the cross-chain chain to prove each link in π. If all links are verified, the authenticity and consistency of the data D are confirmed, otherwise it is rejected;

[0014] In the identity anonymization verification stage: the verifier uses zero-knowledge proof Verifying a blind signature The legitimacy of the data D is verified to ensure the anonymity of the user's identity. If the verification is successful, the authenticity and consistency of the data D are accepted and the anonymity of the user's identity is confirmed;

[0015] In the result feedback and audit log recording stage: the verifier will feedback the verification results to the cross-chain nodes and data publishers; display the verification results to the data publisher and other authorized users through the user interface to confirm that the data has been successfully shared across the chain; record the key operations of the entire cross-chain sharing process, generate audit logs, ensure the auditability of the data sharing process, and complete the data sharing accordingly.

[0016] Furthermore, the data publisher classifies and grades the railway disaster prevention monitoring data according to sensitivity to formulate a corresponding sharing strategy, including the following steps:

[0017] Classifying and processing the railway disaster prevention monitoring data to generate corresponding high-sensitivity data and low-sensitivity data respectively;

[0018] Corresponding sharing strategies are set according to the sensitivity of the data, and the sharing strategies include primary, secondary and tertiary protection measures.

[0019] Furthermore, the step of encrypting using a symmetric encryption algorithm includes:

[0020] Noise generation: For each data segment Generate random noise that conforms to the Laplace distribution, where the noise generation parameters are adjusted according to the sensitivity of the data and the privacy protection requirements;

[0021] Data perturbation: adding random noise to the original data segment Generate perturbed data segments ;

[0022] Encryption and obfuscation: perturbed data fragments Perform encryption and obfuscation;

[0023] Verification Adjustment: During the verification process, considering the noise introduced by differential privacy, the verification algorithm is designed to tolerate errors within a certain range to ensure the accuracy and robustness of the verification process.

[0024] Furthermore, the step of applying differential privacy and de-identification schemes to prevent sensitive information leakage includes:

[0025] Identification information stripping: Before data segmentation, all identification information in the original data is stripped to form de-identified data fragments ;

[0026] Identity mapping encryption: Encrypt the original identity information to generate a mapped encrypted identity ID′; the encrypted identity ID′ will be stored in the data publisher or a trusted third party;

[0027] Distributed identification verification: During the data verification process, each verification node only processes the de-identified data fragments without involving identification information; after the verification is passed, the final verification result is associated and confirmed through the encrypted identification ID′.

[0028] Furthermore, the anonymous credential Obtained by the user from the certification authority CA through the registration process, it contains encrypted user identity information and other related attributes.

[0029] Furthermore, the blind signature σ P The generation process includes:

[0030] The user randomly generates a blinding factor , and use the blinding factor to blind the certificate. The blinded certificate is ,

[0031] The user will The certificate is sent to the certification authority CA. Without knowing the original certificate, the CA Sign and generate blind signature ,

[0032] The user uses r to Unblind and obtain a valid signature;

[0033]

[0034] in, represents the signature function, represents the blinding function, represents the unblinding function, mod represents modular multiplication, d represents the private key of the certification authority CA, and n represents the number of signatures.

[0035] Furthermore, the step of each node linking the proof of the previous node with the current verification result includes:

[0036] The first cross-chain node Receive data fragment from user and its accompanying blind signature and zero-knowledge proof ,verify and effectiveness;

[0037] node The proof is generated using the following formula:

[0038]

[0039]

[0040] Is part of a chain proof and is then passed to the next cross-chain node ;

[0041] Each cross-chain node Both verify the received chain proof and its associated data ;

[0042] If the verification passes, the node The next chain proof will be generated :

[0043]

[0044] in, represents the proof generating function, represents a partial chain proof, Represents the chain proof of the previous node, represents an identifier encrypted using the private key of user P, represents the public key, Represents a hash value.

[0045] A second aspect of the present invention provides a railway disaster prevention monitoring data sharing system, wherein the system comprises:

[0046] The data classification and grading module is used to select cross-chain verification nodes distributed on multiple heterogeneous blockchain platforms during the initial setting stage of the system, so as to generate public and private key pairs corresponding to the data publisher and the cross-chain verification nodes, respectively, and formulate a corresponding cross-chain protocol based on the public and private key pairs, so as to generate corresponding public parameters based on the cross-chain protocol;

[0047] The distributed proof chain module is used in the segmented encryption and identity anonymization stage: the data publisher classifies and grades the railway disaster prevention monitoring data according to sensitivity to formulate a corresponding sharing strategy; the data to be shared is segmented and encrypted using a symmetric encryption algorithm; differential privacy and de-identification schemes are applied to prevent sensitive information leakage, and blind signatures are used and zero-knowledge proofs Prove that they have legitimate anonymous credentials ;

[0048] The cross-chain communication module is used to convert encrypted data fragments and data hashes into H ( D ), encrypted identity information , blind signature and zero-knowledge proofs The data is sent to the cross-chain nodes for segment verification. Each cross-chain node verifies the data fragment of its own chain and communicates with other chain nodes through the cross-chain protocol to verify the consistency of the cross-chain data.

[0049] Identity anonymization module, used in the cross-chain chain proof generation stage: from the cross-chain node Start by verifying and generating chain proofs one by one , each node links the proof of the previous node with the current verification result to form , and pass the signature to the next node until the last node Generate the final cross-chain chain proof;

[0050] The differential privacy module is used in the cross-chain chain proof verification stage: the verifier uses the public parameters and the public key of each node to gradually verify the cross-chain chain to prove each link in π. If all links are verified, the authenticity and consistency of the data D are confirmed, otherwise it is rejected;

[0051] De-identification module, used in the identity anonymization verification stage: the verifier uses zero-knowledge proof Verifying a blind signature The legitimacy of the data D is verified to ensure the anonymity of the user's identity. If the verification is successful, the authenticity and consistency of the data D are accepted and the anonymity of the user's identity is confirmed;

[0052] The sharing module is used in the result feedback and audit log recording stage: the verifier feeds back the verification results to the cross-chain nodes and data publishers; displays the verification results to the data publisher and other authorized users through the user interface to confirm that the data has been successfully shared across the chain; records the key operations of the entire cross-chain sharing process, generates audit logs, and ensures the auditability of the data sharing process to complete the sharing of the corresponding data.

[0053] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the railway disaster prevention monitoring data sharing method as described above.

[0054] The fourth aspect of the present invention proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the railway disaster prevention monitoring data sharing method as described above when executing the computer program.

[0055] Compared with the prior art, the railway disaster prevention monitoring data sharing method shown in the present invention has the following beneficial effects:

[0056] Through innovative distributed verification mechanisms, chain proof structures and cross-chain designs, the reliability of proofs and system scalability are enhanced; combined with differential privacy, de-identification technology and zero-knowledge proof (ZKP), anonymous credentials and blind signature technology, it not only effectively prevents the leakage of sensitive information, but also ensures data security even in multi-node verification and data sharing environments. Compared with the mainstream homogeneous chain cross-chain solution, DPC has achieved a breakthrough in heterogeneous chain cross-chain, and on this basis has increased the anonymity of cross-chain sharing, making up for the shortcomings of existing cross-chain methods that are open and transparent but easy to expose user identities, and providing a more comprehensive and secure solution for the privacy protection and cross-chain sharing of railway disaster prevention monitoring data.

[0057] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0059] Figure 1 A schematic diagram of a flow chart of a railway disaster prevention monitoring data sharing method provided by an embodiment of the present invention;

[0060] Figure 2 This is a structural block diagram of a railway disaster prevention monitoring data sharing system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings. Several embodiments of the present invention are shown in the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0062] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0064] Embodiment 1

[0065] See also Figure 1 , Figure 1 The flowchart of the railway disaster prevention monitoring data sharing method shown in the first embodiment of the present invention includes steps S10 to S70:

[0066] Step S10, selecting cross-chain verification nodes distributed on multiple heterogeneous blockchain platforms to generate public and private key pairs corresponding to the data publisher and the cross-chain verification nodes, respectively, and formulating a corresponding cross-chain protocol according to the public and private key pairs to generate corresponding public parameters based on the cross-chain protocol;

[0067] Specifically, step S10 is an initial setting stage.

[0068] During the initial setup phase, a set of cross-chain validation nodes are selected , these nodes are distributed on multiple heterogeneous blockchain platforms.

[0069] Data publisher P generates a public-private key pair , cross-chain verification node Generate a public-private key pair , ensuring the security of data encryption and signature verification during local blockchain operations and cross-chain communications;

[0070] A corresponding cross-chain protocol is formulated according to the public-private key pair to generate corresponding public parameters based on the cross-chain protocol ,These parameters will serve as an integral part of the subsequent verification process, ensuring the consistency and transparency of the verification process;

[0071] Step S20: The data publisher classifies and grades the railway disaster prevention monitoring data according to sensitivity to develop a corresponding sharing strategy; the data to be shared is segmented and encrypted using a symmetric encryption algorithm; differential privacy and de-identification schemes are applied to prevent sensitive information leakage, and blind signatures are used to and zero-knowledge proof Prove that they have legitimate anonymous credentials ;

[0072] Specifically, step S20 is a segment encryption and identity anonymization stage.

[0073] In the data encryption and identity anonymization stage, the data publisher P classifies and grades the railway disaster prevention monitoring data according to sensitivity, and segments the data D to be shared. , using a symmetric encryption algorithm (AES) to encrypt each data fragment Encryption is performed, and differential privacy and de-identification schemes are used in the data segmentation process to prevent the leakage of sensitive information; blind signatures are used and zero-knowledge proof Prove that they have legitimate anonymous credentials ;

[0074] In this embodiment, the differential privacy scheme includes:

[0075] Noise generation: For each data segment Generate random noise that conforms to the Laplace distribution. The noise generation parameters are adjusted according to the sensitivity of the data and the privacy protection requirements;

[0076] Data perturbation: adding noise to the original data segment Generate perturbed data segments .

[0077] Encryption and obfuscation: perturbed data fragments Due to the noise introduced by differential privacy, even if the attacker obtains the disturbed data fragments, it is difficult to accurately infer the original data content.

[0078] Verification Adjustment: During the verification process, considering the noise introduced by differential privacy, the verification algorithm is designed to tolerate errors within a certain range to ensure the accuracy and robustness of the verification process.

[0079] In this embodiment, the differential privacy scheme includes:

[0080] Identification information stripping: Before data segmentation, all identification information (such as user ID, device ID, location data, etc.) in the original data is stripped to form de-identified data fragments .

[0081] Identity mapping encryption: Encrypt the original identity information to generate a mapped encrypted identity ID'. The encrypted identity will be stored in the data publisher or a trusted third party.

[0082] Distributed identity verification: During the data verification process, each verification node only processes the de-identified data fragments without involving the identification information. After the verification is passed, the final verification result is associated and confirmed through the encrypted identification ID′.

[0083] In this embodiment, the anonymous credential Obtained by the user from the certification authority CA through the registration process, it contains the user's identity information, but the information has been encrypted or obfuscated and only P can decrypt it. Include user public key ,sign and other related attributes (such as permissions, validity period, etc.).

[0084] Among them, blind signature The generation process includes:

[0085] User P randomly generates a blinding factor , and use the blinding factor to blind the certificate. The blinded certificate is .

[0086] User P will The certificate is sent to the certification authority CA. Without knowing the original certificate, the CA Sign and generate blind signature .

[0087] User P uses r to Unblind and get a valid signature:

[0088]

[0089] in, represents the signature function, represents the blinding function, represents the unblinding function, mod represents modular multiplication, d represents the private key of the certification authority CA, and n represents the number of signatures.

[0090] Step S30: Encrypted data fragments, data hash H ( D ), encrypted identity information , blind signature and zero-knowledge proofs The data is sent to the cross-chain nodes for segment verification. Each cross-chain node verifies the data fragment of its own chain and communicates with other chain nodes through the cross-chain protocol to verify the consistency of the cross-chain data.

[0091] Specifically, step S30 is the cross-chain communication and segment verification stage.

[0092] The encrypted data fragments, data hash H ( D ), encrypted identity information And blind signature and zero-knowledge proofs Sent to cross-chain nodes for segment verification;

[0093] During the segmented verification process, each cross-chain node not only verifies the data fragments of its own chain, but also communicates with nodes on other chains through the cross-chain protocol to verify the consistency of data on different chains.

[0094] Step S40, from the cross-chain node Start by verifying and generating chain proofs one by one , each node links the proof of the previous node with the current verification result to form , and pass the signature to the next node;

[0095] Specifically, step S40 is the cross-chain chain proof generation stage.

[0096] The first cross-chain node Receive cross-chain protocol parameters , user data fragment And its accompanying blind signature and zero-knowledge proofs ,verify and The validity of , and pass it to the cross-chain node on the next chain after signing it ;

[0097] Each cross-chain node Verify the received chain proof and its associated data If the verification passes, the node Generate the next chain proof and compare it with Link together to form , then sign and pass it to the node on the next chain through the cross-chain protocol. Until the last node Generate the final chain proof.

[0098] In this embodiment, the specific process of cross-chain chain proof generation includes:

[0099] node The proof is generated using the following formula:

[0100]

[0101]

[0102] In the formula, Is part of a chain proof and is then passed to the next cross-chain node .

[0103] According to one aspect of the above technical solution, the node Generate the next chain proof The process includes:

[0104]

[0105] in, represents the proof generating function, represents a partial chain proof, Represents the chain proof of the previous node, represents an identifier encrypted using the private key of user P, represents the public key, Represents a hash value.

[0106] Step S50, the verifier uses the public parameters and the public key of each node to gradually verify the cross-chain chain to prove each link in π. If all links are verified, the authenticity and consistency of the data D are confirmed, otherwise it is rejected;

[0107] Specifically, step S50 is the cross-chain chain proof verification stage.

[0108] During the cross-chain chain proof verification process, the verifier V receives the cross-chain chain proof and the hash value of the original data Finally, the public parameters and the public keys of each node are used to gradually verify the proof of each link. If the verification of all cross-chain links is passed, the authenticity and consistency of data D are accepted, otherwise it is rejected.

[0109] Step S60: The verifier proves the Verifying a blind signature The legitimacy of the data D is verified to ensure the anonymity of the user's identity. If the verification is successful, the authenticity and consistency of the data D are accepted and the anonymity of the user's identity is confirmed;

[0110] Specifically, step S60 is the identity anonymization verification stage.

[0111] Verify the identity of the verifier V through zero-knowledge proof verify The legitimacy of the data D is verified to ensure that the anonymity of the user's identity has not been leaked. If all verifications pass, the verifier accepts the authenticity and consistency of the data D and confirms the anonymity of the user's identity.

[0112] Step S70, in the result feedback and audit log recording stage: the verifier feeds back the verification results to the cross-chain nodes and data publishers; displays the verification results to the data publisher and other authorized users through the user interface to confirm that the data has been successfully shared across the chain; records the key operations of the entire cross-chain sharing process, generates audit logs, ensures the auditability of the data sharing process, and completes the sharing of the data accordingly.

[0113] Specifically, step S70 is the result feedback and audit log recording stage.

[0114] After the verification is passed, the verifier V will feedback the verification results to the cross-chain node and the data publisher P, and display them to the data publisher P and other authorized users through the user interface to confirm that the data has been successfully shared across the chain. The key operations of the entire cross-chain sharing process will be recorded and an audit log will be generated to ensure the transparency and traceability of the data sharing process.

[0115] In summary, compared with the prior art, the railway disaster prevention monitoring data sharing method shown in this embodiment has the following beneficial effects:

[0116] Through innovative distributed verification mechanisms, chain proof structures and cross-chain designs, the reliability of proofs and system scalability are enhanced; combined with differential privacy, de-identification technology and zero-knowledge proof (ZKP), anonymous credentials and blind signature technology, it not only effectively prevents the leakage of sensitive information, but also ensures data security even in multi-node verification and data sharing environments. Compared with the mainstream homogeneous chain cross-chain solution, DPC has achieved a breakthrough in heterogeneous chain cross-chain, and on this basis has increased the anonymity of cross-chain sharing, making up for the shortcomings of existing cross-chain methods that are open and transparent but easy to expose user identities, and providing a more comprehensive and secure solution for the privacy protection and cross-chain sharing of railway disaster prevention monitoring data.

[0117] Embodiment 2

[0118] This embodiment also provides a system architecture for the above-mentioned railway disaster prevention monitoring data sharing method, and the system architecture includes:

[0119] Data collection layer: responsible for collecting railway disaster prevention monitoring data, including real-time status, geological structure, weather information, etc. Various sensors and monitoring equipment upload data to local blockchain nodes.

[0120] Data processing layer: classify and grade the collected data, apply differential privacy and de-identification technologies, and encrypt and process data fragments.

[0121] Cross-chain communication layer: Using distributed proof chains (DPC) and cross-chain protocols, data sharing and verification between different blockchain platforms are realized. Nodes in the cross-chain communication layer participate in the distributed verification process and generate chain proofs.

[0122] User interaction layer: Provides a user interface (UI) where users can obtain anonymous credentials through the authentication center and protect identity privacy during the cross-chain process. Users can also view the verification results of shared data through the interaction layer.

[0123] This embodiment also provides a railway disaster prevention monitoring data privacy protection and cross-chain sharing system based on a distributed proof chain (DPC), which is applied to the method shown in Example 1. It demonstrates the application scenarios of cross-chain technology in the field of railway disaster prevention monitoring. By building an efficient and secure cross-chain communication system, data sharing and interaction between different railway organizations and between users and railway organizations are realized. The architecture uses the user's external platform as the entrance, connects to the relay chain through a cross-chain gateway, and then connects to each railway organization through their respective cross-chain gateways. This breaks the data island and improves the liquidity and value of the data. The cross-chain chain architecture diagram includes:

[0124] User external platform, users initiate data queries or receive alarm notifications through the user external platform; the platform sends query requests to the relay chain through the cross-chain gateway and obtains query results; the query results are processed and displayed to users, providing an intuitive data visualization interface.

[0125] Cross-chain gateway (user side): The cross-chain gateway connects the user's external platform and the relay chain, responsible for converting user requests into cross-chain transactions and sending them to the relay chain;

[0126] The relay chain, as the core of cross-chain communication, is responsible for receiving cross-chain transaction requests from the cross-chain gateway, and performing verification and routing. According to the transaction content, the cross-chain transaction is forwarded to the corresponding cross-chain gateway. The smart contract on the relay chain can execute the logic of the cross-chain transaction to ensure the consistency and security of the data. Through the distributed verification mechanism, the relay chain can ensure the correctness and legality of the cross-chain transaction.

[0127] The cross-chain gateway (railway agency side) connects the relay chain and the railway agency. It is responsible for monitoring cross-chain transaction requests on local blockchain nodes, packaging cross-chain transactions into a specific format through the cross-chain protocol, forwarding cross-chain transactions on the relay chain to the railway agency, and receiving responses from the railway agency.

[0128] Railway agencies: They represent different data sources and recipients, and each maintains its own blockchain system to store and process railway disaster prevention monitoring data. Railway agencies collect railway disaster prevention monitoring data through their own monitoring equipment and sensors. After preliminary processing, the data is uploaded to the local blockchain node. On the local blockchain node, the data is further classified and graded, and privacy protection technologies such as differential privacy and de-identification technology are applied to protect sensitive data and identity privacy. Data fragments are encrypted and communicate with the relay chain through the cross-chain gateway to achieve data sharing and verification.

[0129] Embodiment 3

[0130] See also Figure 2The present invention provides a railway disaster prevention monitoring data sharing system, wherein the system comprises:

[0131] The data classification and grading module is used to select cross-chain verification nodes distributed on multiple heterogeneous blockchain platforms during the initial setting stage of the system, so as to generate public and private key pairs corresponding to the data publisher and the cross-chain verification nodes, respectively, and formulate a corresponding cross-chain protocol based on the public and private key pairs, so as to generate corresponding public parameters based on the cross-chain protocol;

[0132] The distributed proof chain module is used in the segmented encryption and identity anonymization stage: the data publisher classifies and grades the railway disaster prevention monitoring data according to sensitivity to formulate a corresponding sharing strategy; the data to be shared is segmented and encrypted using a symmetric encryption algorithm; differential privacy and de-identification schemes are applied to prevent sensitive information leakage, and blind signatures are used and zero-knowledge proofs Prove that they have legitimate anonymous credentials ;

[0133] The cross-chain communication module is used to convert encrypted data fragments and data hashes into H ( D ), encrypted identity information , blind signature and zero-knowledge proofs The data is sent to the cross-chain nodes for segment verification. Each cross-chain node verifies the data fragment of its own chain and communicates with other chain nodes through the cross-chain protocol to verify the consistency of the cross-chain data.

[0134] Identity anonymization module, used in the cross-chain chain proof generation stage: from the cross-chain node Start by verifying and generating chain proofs one by one , each node links the proof of the previous node with the current verification result to form , and pass the signature to the next node until the last node Generate the final cross-chain chain proof;

[0135] The differential privacy module is used in the cross-chain chain proof verification stage: the verifier uses the public parameters and the public key of each node to gradually verify the cross-chain chain to prove each link in π. If all links are verified, the authenticity and consistency of the data D are confirmed, otherwise it is rejected;

[0136] De-identification module, used in the identity anonymization verification stage: the verifier uses zero-knowledge proof Verifying a blind signature The legitimacy of the data D is verified to ensure the anonymity of the user's identity. If the verification is successful, the authenticity and consistency of the data D are accepted and the anonymity of the user's identity is confirmed;

[0137] The sharing module is used in the result feedback and audit log recording stage: the verifier feeds back the verification results to the cross-chain nodes and data publishers; displays the verification results to the data publisher and other authorized users through the user interface to confirm that the data has been successfully shared across the chain; records the key operations of the entire cross-chain sharing process, generates audit logs, and ensures the auditability of the data sharing process to complete the sharing of the corresponding data.

[0138] Embodiment 4

[0139] The fourth embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the railway disaster prevention monitoring data sharing method described in the above embodiment.

[0140] Embodiment 5

[0141] The fifth embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the railway disaster prevention and monitoring data sharing method described in the above embodiment when executing the computer program.

[0142] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0143] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A railway disaster prevention monitoring data sharing method, characterized in that: The following steps are involved: In the initial setting stage of the system: cross-chain verification nodes are selected to be distributed on multiple heterogeneous blockchain platforms to generate public and private key pairs corresponding to the data publisher and the cross-chain verification nodes, respectively, and a corresponding cross-chain protocol is formulated based on the public and private key pairs to generate corresponding public parameters based on the cross-chain protocol; In the segmented encryption and identity anonymization stage: the data publisher classifies and grades the railway disaster prevention monitoring data according to sensitivity to formulate a corresponding sharing strategy; the data to be shared is segmented and encrypted using a symmetric encryption algorithm; differential privacy and de-identification schemes are applied to prevent sensitive information leakage, and blind signatures are used to and zero-knowledge proof Prove that they have legitimate anonymous credentials , wherein the sharing strategy includes primary, secondary and tertiary protection measures; In the cross-chain communication and segment verification stage: the encrypted data fragments, data hashes H ( D ), encrypted identity information , blind signature and zero-knowledge proof The data is sent to the cross-chain nodes for segment verification. Each cross-chain node verifies the data fragment of its own chain and communicates with other chain nodes through the cross-chain protocol to verify the consistency of the cross-chain data. In the cross-chain chain proof generation stage: from the cross-chain node Start by verifying and generating chain proofs one by one , each node links the proof of the previous node with the current verification result to form , and pass the signature to the next node until the last node Generate the final cross-chain chain proof; In the cross-chain chain proof verification stage: the verifier uses the public parameters and the public keys of each node to gradually verify the cross-chain chain to prove each link in π. If all links are verified, the authenticity and consistency of the data D are confirmed, otherwise it is rejected; In the identity anonymization verification stage: the verifier uses zero-knowledge proof Verifying a blind signature The legitimacy of the data D is verified to ensure the anonymity of the user's identity. If the verification is successful, the authenticity and consistency of the data D are accepted and the anonymity of the user's identity is confirmed; In the result feedback and audit log recording stage: the verifier will feedback the verification results to the cross-chain nodes and data publishers; display the verification results to the data publisher and other authorized users through the user interface to confirm that the data has been successfully shared across the chain; record the key operations of the entire cross-chain sharing process, generate audit logs, ensure the auditability of the data sharing process, and complete the data sharing accordingly.

2. The railway disaster prevention monitoring data sharing method according to claim 1 is characterized in that: The data publisher classifies and grades the railway disaster prevention monitoring data according to sensitivity to formulate a corresponding sharing strategy, including the following steps: Classifying and processing the railway disaster prevention monitoring data to generate corresponding high-sensitivity data and low-sensitivity data respectively; Set corresponding sharing strategies according to the sensitivity of the data.

3. The railway disaster prevention monitoring data sharing method according to claim 1, characterized in that: The steps of encrypting using a symmetric encryption algorithm include: Noise generation: For each data segment Generate random noise that conforms to the Laplace distribution, where the noise generation parameters are adjusted according to the sensitivity of the data and the privacy protection requirements; Data perturbation: adding random noise to the original data segment Generate perturbed data segments ; Encryption and obfuscation: perturbed data fragments Perform encryption and obfuscation; Verification Adjustment: During the verification process, considering the noise introduced by differential privacy, the verification algorithm is designed to tolerate errors within a certain range to ensure the accuracy and robustness of the verification process.

4. The railway disaster prevention monitoring data sharing method according to claim 1 is characterized in that: The steps of applying differential privacy and de-identification scheme to prevent sensitive information leakage include: Identification information stripping: Before data segmentation, all identification information in the original data is stripped to form de-identified data segments; Identity mapping encryption: Encrypt the original identity information to generate a mapped encrypted identity ID′; the encrypted identity ID′ will be stored in the data publisher or a trusted third party; Distributed identification verification: During the data verification process, each verification node only processes the de-identified data fragments without involving identification information; after the verification is passed, the final verification result is associated and confirmed through the encrypted identification ID′.

5. The railway disaster prevention monitoring data sharing method according to claim 1, characterized in that: The anonymous credentials Obtained by the user from the certification authority CA through the registration process, it contains encrypted user identity information and other related attributes.

6. The railway disaster prevention monitoring data sharing method according to claim 5 is characterized in that: The blind signature σ P The generation process includes: The user randomly generates a blinding factor , and use the blinding factor to blind the anonymous credential. The blinded anonymous credential is ; The user will The certificate is sent to the certification authority CA. The certification authority CA, without knowing the original certificate, Sign and generate blind signature ; The user uses r to Unblind and obtain a valid signature; ； in, Represents a signature function, represents the blinding function, represents the unblinding function, mod represents modular multiplication, d represents the private key of the certification authority CA, and n represents the number of signatures.

7. The railway disaster prevention monitoring data sharing method according to claim 1, characterized in that: The step of each node linking the proof of the previous node with the current verification result includes: The first cross-chain node Receive data fragment from user and its accompanying blind signature and zero-knowledge proof ,verify and effectiveness; node The proof is generated using the following formula: ； ； Is part of a chain proof and is then passed to the next cross-chain node ; Each cross-chain node Both verify the received chain proof and its associated data ; If the verification passes, the node The next chain proof will be generated : ； in, represents the proof generating function, represents a partial chain proof, Represents the chain proof of the previous node, represents an identifier encrypted using the private key of user P, represents the public key, Represents a hash value.

8. A railway disaster prevention monitoring data sharing system, characterized in that: The system comprises: The data classification and grading module is used to select cross-chain verification nodes distributed on multiple heterogeneous blockchain platforms during the initial setting stage of the system, so as to generate public and private key pairs corresponding to the data publisher and the cross-chain verification nodes, respectively, and formulate a corresponding cross-chain protocol based on the public and private key pairs, so as to generate corresponding public parameters based on the cross-chain protocol; The distributed proof chain module is used in the segmented encryption and identity anonymization stage: the data publisher classifies and grades the railway disaster prevention monitoring data according to sensitivity to formulate a corresponding sharing strategy; the data to be shared is segmented and encrypted using a symmetric encryption algorithm; differential privacy and de-identification schemes are applied to prevent sensitive information leakage, and blind signatures are used and zero-knowledge proof Prove that they have legitimate anonymous credentials , wherein the sharing strategy includes primary, secondary and tertiary protection measures; The cross-chain communication module is used to convert encrypted data fragments and data hashes into H ( D ), encrypted identity information , blind signature and zero-knowledge proof The data is sent to the cross-chain nodes for segment verification. Each cross-chain node verifies the data fragment of its own chain and communicates with other chain nodes through the cross-chain protocol to verify the consistency of the cross-chain data. Identity anonymization module, used in the cross-chain chain proof generation stage: from the cross-chain node Start by verifying and generating chain proofs one by one , each node links the proof of the previous node with the current verification result to form , and pass the signature to the next node until the last node Generate the final cross-chain chain proof; The differential privacy module is used in the cross-chain chain proof verification stage: the verifier uses the public parameters and the public key of each node to gradually verify the cross-chain chain to prove each link in π. If all links are verified, the authenticity and consistency of the data D are confirmed, otherwise it is rejected; De-identification module, used in the identity anonymization verification stage: the verifier uses zero-knowledge proof Verifying a blind signature The legitimacy of the data D is verified to ensure the anonymity of the user's identity. If the verification is successful, the authenticity and consistency of the data D are accepted and the anonymity of the user's identity is confirmed; The sharing module is used in the result feedback and audit log recording stage: the verifier feeds back the verification results to the cross-chain nodes and data publishers; displays the verification results to the data publisher and other authorized users through the user interface to confirm that the data has been successfully shared across the chain; records the key operations of the entire cross-chain sharing process, generates audit logs, and ensures the auditability of the data sharing process to complete the sharing of the corresponding data.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the railway disaster prevention monitoring data sharing method described in any one of claims 1 to 7 is implemented.

10. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the railway disaster prevention monitoring data sharing method according to any one of claims 1 to 7 when executing the computer program.

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