Safe and efficient data traceability method and system based on block chain

Abstract

According to the block chain-based safe and efficient data traceability method and system provided by the invention, the example threshold is determined through the potential safety hazard feature of the global network minimum potential hazard analysis applied in the business interaction data processing process, and the abnormal information needing traceability processing in the business interaction data needing traceability is obtained through feature extraction; the traceability can be timely and accurately successful.

Description

Technical Field

[0001] The present application relates to the field of data security analysis technology, and specifically, to a blockchain-based data security and efficient traceability method and system. Background Art

[0002] With the continuous development of science and technology, information security is an important issue. It not only requires how to protect information from being leaked, but also requires finding the terminal location of the information leak. Therefore, a data security and efficient traceability method is urgently needed to solve the above problems. Summary of the invention

[0003] In order to improve the technical problems existing in related technologies, the present application provides a blockchain-based data security and efficient traceability method and system.

[0004] In a first aspect, a blockchain-based data security and efficient traceability method is provided, the method comprising at least: obtaining business interaction data that needs to be traced; determining a paradigm threshold through the security risk characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network; tracing the network data of the business interaction data that needs to be traced for risk analysis to determine at least one data tracing result; extracting features from the at least one data tracing result in combination with the paradigm threshold to obtain risk key features; tracing abnormal information in the risk key features in combination with the at least one data security analysis network to obtain a tracing result; and parsing the tracing result to obtain a data security parsing result.

[0005] In an independently implemented embodiment, determining the example threshold through the safety hazard characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network includes: determining the safety hazard characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network; determining the best value among the safety hazard characteristics of each of the minimum risk analysis as the example threshold.

[0006] In an independently implemented embodiment, the acquisition of business interaction data that needs to be traced includes: real-time determination of the depolarization data of the part that needs to be traced in different time periods of the data security analysis system; in response to the depolarization data being not less than a specified depolarization requirement, data processing is performed in combination with the data security analysis system to obtain the business interaction data that needs to be traced.

[0007] In an independently implemented embodiment, determining the example threshold through the safety hazard characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network includes: determining the safety hazard characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network; determining the best value among the depolarization requirement and the safety hazard characteristics of each of the minimum risk analysis as the example threshold.

[0008] In an independently implemented embodiment, the data security analysis network includes a partially depolarized safety assessment network, and the tracing processing of the abnormal information in the key features of the hidden danger in combination with the at least one data security analysis network to obtain the tracing processing result includes: extracting features of the abnormal information in the key features of the hidden danger in combination with the partially depolarized safety assessment network to obtain target abnormal information; tracing the target abnormal information in combination with the non-partial depolarized safety assessment network in the at least one tracing processing vector to obtain the tracing processing result.

[0009] In an independently implemented embodiment, the method of extracting features of the abnormal information in the key features of the hidden danger in combination with the partial depolarization safety assessment network to obtain target abnormal information includes: performing a partial depolarization safety assessment network for analyzing the hidden danger of the abnormal information in each key feature of the hidden danger to obtain no less than one partial assessment vector, each of the partial assessment vectors having a corresponding assessment judgment value; and determining abnormal information in which all global partial assessment vectors are greater than the corresponding assessment judgment value as target abnormal information.

[0010] In an independently implemented embodiment, the data security analysis network includes at least one of a partial data optimization network, a partial key data debugging network, a partial key data verification network, and a partial key data identification network.

[0011] In an independently implemented embodiment, the example threshold includes a data risk threshold and a data hidden danger threshold, the data tracing result vector includes a data risk analysis result and a data hidden danger analysis result, and the feature extraction of the at least one data tracing result in combination with the example threshold to obtain the hidden danger key feature includes: comparing the at least one data tracing result vector with the example threshold; in response to the data scale of the data tracing result being greater than the data risk threshold, and the data tracing result vector being greater than the data hidden danger threshold, determining that the data tracing result is a hidden danger key feature.

[0012] In a second aspect, a secure and efficient data traceability system based on blockchain is provided, comprising a processor and a memory that communicate with each other, wherein the processor is used to identify and execute a computer program from the memory to implement the above method.

[0013] The embodiments of the present application provide a blockchain-based data security and efficient traceability method and system, which determines the example threshold through the security risk characteristics of the global network minimum risk analysis applied in the business interaction data processing process, and obtains the abnormal information that needs to be traced in the business interaction data that needs to be traced by feature extraction, so as to achieve timely and accurate traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A flowchart of a blockchain-based data security and efficient traceability method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0017] See also Figure 1 , showing a method for secure and efficient data tracing based on blockchain, which may include the technical solutions described in the following steps 100-600.

[0018] Step 100: Obtain business interaction data that needs to be traced.

[0019] Step 200: determining a sample threshold value through the security risk characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network.

[0020] Step 300, tracing the network data that needs to be traced for potential risks analysis of business interaction data to determine at least one data tracing result.

[0021] Step 400: extract features from the at least one data tracing result in combination with the example threshold to obtain key features of hidden dangers.

[0022] Step 500: perform source tracing processing on abnormal information in the key features of the hidden dangers in combination with the at least one data security analysis network to obtain a source tracing processing result.

[0023] Step 600, parse the traceability processing result to obtain a data security analysis result.

[0024] It can be understood that when executing the contents described in the above steps 100-600, the example threshold is determined by the security risk characteristics of the global network minimum risk analysis applied in the business interaction data processing process, and the abnormal information that needs to be traced is obtained in the business interaction data that needs to be traced by feature extraction, so that timely and accurate tracing can be successful.

[0025] In one possible implementation, determining the example threshold through the safety hazard characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network includes: determining the safety hazard characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network; and determining the best value among the safety hazard characteristics of each of the minimum risk analysis as the example threshold.

[0026] In one possible implementation, the acquisition of business interaction data that needs to be traced includes: determining in real time the depolarization data of the part that needs to be traced in different time periods of the data security analysis system; in response to the depolarization data being not less than a specified depolarization requirement, performing data processing in combination with the data security analysis system to obtain the business interaction data that needs to be traced.

[0027] In one possible implementation, determining the example threshold through the safety hazard characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network includes: determining the safety hazard characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network; and determining the best value among the depolarization requirement and the safety hazard characteristics of each of the minimum risk analysis as the example threshold.

[0028] In a possible implementation embodiment, the data security analysis network includes a partially depolarized safety assessment network, and the tracing processing of the abnormal information in the key features of the hidden danger in combination with the at least one data security analysis network to obtain the tracing processing result includes: extracting features of the abnormal information in the key features of the hidden danger in combination with the partially depolarized safety assessment network to obtain target abnormal information; tracing the target abnormal information in combination with the non-partial depolarized safety assessment network in the at least one tracing processing vector to obtain the tracing processing result.

[0029] In a possible implementation embodiment, the method of extracting features of abnormal information in the key features of the hidden danger in combination with the partial depolarization safety assessment network to obtain target abnormal information includes: performing a partial depolarization safety assessment network for analyzing the hidden danger of abnormal information in each key feature of the hidden danger to obtain at least one partial assessment vector, each of the partial assessment vectors having a corresponding assessment judgment value; and determining abnormal information in which all global partial assessment vectors are greater than the corresponding assessment judgment value as target abnormal information.

[0030] In a possible implementation embodiment, the data security analysis network includes at least one of a partial data optimization network, a partial key data debugging network, a partial key data verification network, and a partial key data identification network.

[0031] In one possible implementation embodiment, the example threshold includes a data risk threshold and a data hidden danger threshold, the data tracing result vector includes a data risk analysis result and a data hidden danger analysis result, and the feature extraction of the at least one data tracing result in combination with the example threshold to obtain the hidden danger key feature includes: comparing the at least one data tracing result vector with the example threshold; in response to the data scale of the data tracing result being greater than the data risk threshold, and the data tracing result vector being greater than the data hidden danger threshold, determining that the data tracing result is a hidden danger key feature.

[0032] On the basis of the above, a data security and efficient traceability system based on blockchain is shown, including a processor and a memory that communicate with each other, and the processor is used to identify and execute a computer program from the memory to implement the above method.

[0033] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0034] In summary, based on the above scheme, the example threshold is determined by the security risk characteristics of the global network minimum risk analysis applied in the process of business interaction data processing, and the abnormal information that needs to be traced in the business interaction data that needs to be traced is obtained by feature extraction, so that timely and accurate traceability can be achieved.

[0035] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above methods and systems can be implemented using computer executable instructions and / or included in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software such as executed by various types of processors, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0036] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or may be any other beneficial effects that may be obtained.

[0037] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0038] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0039] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, each aspect of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0040] The computer storage medium may contain a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination of forms. The computer storage medium may be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or equipment to realize the communication, dissemination or hidden danger analysis for use. The program code located on the computer storage medium can be transmitted through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0041] The computer program codes required for the operation of each part of the present application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages, etc. The program code can be run entirely on the user's computer, or run on the user's computer as an independent software package, or run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0042] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0043] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0044] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used for the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers allow adaptive changes. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which can be changed according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0045] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, instructions, publications, documents, etc., cited in this application are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the content of this application, documents that limit the broadest scope of the claims of this application (currently or later attached to this application) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the attached materials of this application are inconsistent or conflicting with the content described in this application, the descriptions, definitions, and / or use of terms in this application shall prevail.

[0046] Finally, it should be understood that the embodiments described in this application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, as an example and not a limitation, the alternative configurations of the embodiments of the present application may be considered to be consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

[0047] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A secure and efficient data tracing method based on blockchain, characterized in that: The method at least comprises: Obtain business interaction data that needs to be traced; Determine a sample threshold value by using the security risk characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network; Tracing the network data source of the business interaction data that needs to be traced for potential risks analysis to determine at least one data tracing result; In combination with the example threshold, feature extraction is performed on the at least one data tracing result to obtain key features of hidden dangers; Performing source tracing processing on abnormal information in the key features of the hidden dangers in combination with the at least one data security analysis network to obtain a source tracing processing result; The traceability processing results are analyzed to obtain data security analysis results.

2. The method according to claim 1, characterized in that The method of determining the example threshold value through the safety hazard characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network includes: determining the safety hazard characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network; and determining the best value among the safety hazard characteristics of each of the minimum risk analysis as the example threshold value.

3. The method according to claim 1, characterized in that The acquisition of the business interaction data that needs to be traced includes: Determine in real time the depolarized data that needs to be traced in different periods of the data security analysis system; In response to the depolarization data being not less than the specified depolarization requirement, data processing is performed in combination with the data security analysis system to obtain business interaction data that needs to be traced.

4. The method according to claim 3, characterized in that The method of determining the example threshold value through the safety hazard characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network includes: determining the safety hazard characteristics of the minimum risk analysis corresponding to each of the data security analysis networks in at least one data security analysis network; and determining the best value among the depolarization requirement and the safety hazard characteristics of each of the minimum risk analyses as the example threshold value.

5. The method according to claim 4, characterized in that The data security analysis network includes a partially depolarized security assessment network, and the at least one data security analysis network is combined to perform source tracing processing on abnormal information in the key features of the hidden dangers, and the tracing processing results obtained include: Extracting features of abnormal information in the key features of the hidden dangers in combination with the partial depolarization safety assessment network to obtain target abnormal information; The target abnormal information is traced by the non-partial depolarization safety assessment network in combination with the at least one traceability processing vector to obtain a traceability processing result.

6. The method according to claim 5, characterized in that The feature extraction of abnormal information in the key features of the hidden danger combined with the partial depolarization safety assessment network to obtain target abnormal information includes: Depolarizing the partial safety assessment network for analyzing the abnormal information hidden danger in each of the hidden danger key features to obtain at least one partial assessment vector, each of the partial assessment vectors having a corresponding assessment judgment value; The abnormal information whose global partial evaluation vectors are all greater than the corresponding evaluation judgment value is determined as the target abnormal information.

7. The method according to claim 6, characterized in that The data security analysis network includes at least one of a partial data optimization network, a partial key data debugging network, a partial key data verification network and a partial key data identification network.

8. The method according to claim 7, characterized in that The example threshold includes a data risk threshold and a data hidden danger threshold, the data tracing result vector includes a data risk analysis result and a data hidden danger analysis result, and the step of extracting features from the at least one data tracing result in combination with the example threshold to obtain hidden danger key features includes: Comparing the at least one data tracing result vector with the example threshold; In response to the data scale of the data tracing result being greater than the data risk threshold, and the data tracing result vector being greater than the data hidden danger threshold, the data tracing result is determined to be a hidden danger key feature.

9. A data security and efficient traceability system based on blockchain, characterized in that: The invention comprises a processor and a memory communicating with each other, wherein the processor is used to identify a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 8.

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