CN114978531B - Deep learning-oriented data credible traceability marking method and system - Google Patents
Deep learning-oriented data credible traceability marking method and system Download PDFInfo
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- CN114978531B CN114978531B CN202210512826.0A CN202210512826A CN114978531B CN 114978531 B CN114978531 B CN 114978531B CN 202210512826 A CN202210512826 A CN 202210512826A CN 114978531 B CN114978531 B CN 114978531B
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- 238000000034 method Methods 0.000 title claims abstract description 14
- 238000012804 iterative process Methods 0.000 claims abstract description 17
- 238000013135 deep learning Methods 0.000 claims abstract description 14
- 238000012795 verification Methods 0.000 claims description 10
- 238000010276 construction Methods 0.000 claims description 3
- 230000008520 organization Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
- H04L9/3247—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving digital signatures
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
- H04L9/3263—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving certificates, e.g. public key certificate [PKC] or attribute certificate [AC]; Public key infrastructure [PKI] arrangements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L2463/00—Additional details relating to network architectures or network communication protocols for network security covered by H04L63/00
- H04L2463/146—Tracing the source of attacks
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Abstract
The invention relates to a deep learning-oriented data credible traceability marking method and a system, wherein the method comprises the following steps: receiving data to be processed; carrying out iterative processing on the data to be processed and carrying out digital signature on the processing result data by using a digital certificate; a digital signature chain of an iterative process from source data to final processing result data is established based on the obtained plurality of digital signatures. The invention adopts the credible traceability chain formed by the digital signature to finish credible traceability of the iterative process of data processing, can avoid the performance loss caused by adopting the distributed block chain scheme, and is more suitable for the application scene of rapid iterative processing of data required by deep learning.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a data credible tracing marking method and system for deep learning.
Background
With the maturity of the blockchain technology, the blockchain technology is used as a general digital evidence chain solution, the blockchain technology can be used for deeply learning data credible tracing, and the registration of the blockchain can ensure the credibility of data iteration, but has the defect that the retrieval speed is slow and the history blocks need to be traversed during tracing.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a deep learning-oriented data credible traceability marking method and system, which avoid performance loss caused by adopting a distributed block chain scheme and are more suitable for application scenes of rapid iterative processing of data required by deep learning.
The technical scheme adopted for solving the technical problems is as follows: the data credible tracing marking method facing deep learning comprises the following steps:
receiving data to be processed;
Carrying out iterative processing on the data to be processed and carrying out digital signature on the processing result data by using a digital certificate;
A digital signature chain of an iterative process from source data to final processing result data is established based on the obtained plurality of digital signatures.
After receiving the data to be processed, the method further comprises the step of verifying the digital signature attached to the data to be processed to confirm the credibility of data input.
The method further comprises the step of receiving the digital certificate which is issued by a central digital certificate issuing organization and faces deep learning before receiving the data to be processed.
The iterative processing of the data to be processed and the digital signature of the processing result data by using the digital certificate are specifically as follows:
performing iterative processing on the data to be processed to obtain processing result data;
Combining the digital signature of the data to be processed and the processing result data into basic information;
Digitally signing said base information using said digital certificate.
The data credible tracing marking method facing deep learning further comprises the following steps: and sequentially verifying through the digital signature chain to retrospectively confirm the credibility of the whole data processing iterative process.
The technical scheme adopted for solving the technical problems is as follows: the data credible traceability marking system facing deep learning comprises:
The first receiving module is used for receiving data to be processed;
the processing signature module is used for carrying out iterative processing on the data to be processed to obtain processing result data, and carrying out digital signature on the processing result data by using a digital certificate;
And the digital signature chain construction module is used for establishing a digital signature chain of an iterative process from the source data to the final processing result data based on the obtained plurality of digital signatures.
The deep learning-oriented data credible traceability marking system further comprises: and the verification module is used for verifying the digital signature attached to the data to be processed so as to confirm the credibility of the data input.
The deep learning-oriented data trusted traceability marking system further comprises a second receiving module, wherein the second receiving module is used for receiving the deep learning-oriented digital certificate issued by the central digital certificate issuing institution.
The processing signature module comprises:
the processing unit is used for carrying out iterative processing on the data to be processed to obtain processing result data;
the merging unit is used for merging the digital signature of the data to be processed and the processing result data into basic information;
and the signature unit is used for digitally signing the basic information by using the digital certificate.
The deep learning-oriented data credible traceability marking system further comprises: and the verification module is used for sequentially verifying through the digital signature chain to retrospectively confirm the credibility of the whole data processing iterative process.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: the invention adopts the credible traceability chain formed by the digital signature to finish credible traceability of the iterative process of data processing, can avoid the performance loss caused by adopting the distributed block chain scheme, and is more suitable for the application scene of rapid iterative processing of data required by deep learning. According to the invention, the digital signature is carried out by attaching the digital signature of the input data and the content of the output data together, so that a complete digital signature chain from the input data to the output data can be formed, and the quick verification can be completed by traversing the digital signature chain during the trusted traceability verification.
Drawings
Fig. 1 is a flow chart of a first embodiment of the present invention.
Detailed Description
The application will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present application and are not intended to limit the scope of the present application. Furthermore, it should be understood that various changes and modifications can be made by one skilled in the art after reading the teachings of the present application, and such equivalents are intended to fall within the scope of the application as defined in the appended claims.
The first embodiment of the invention relates to a deep learning-oriented data credible traceability marking method, which comprises the following steps: receiving data to be processed; carrying out iterative processing on the data to be processed and carrying out digital signature on the processing result data by using a digital certificate; a digital signature chain of an iterative process from source data to final processing result data is established based on the obtained plurality of digital signatures. As shown in fig. 1, specifically:
Step 1, issuing digital certificates required to be used by each data processing module facing deep learning by a central digital certificate issuing authority. That is, after this step, each data processing module can obtain a digital certificate dedicated to each data processing module.
And 2, the data processing module receives the data to be processed, and after receiving the data to be processed, verifies the digital signature of the data to be processed to confirm the input credibility of the data to be processed.
And step 3, the data processing module carries out iterative processing on the input data and carries out digital signature on the processing result data by using the digital certificate of the data processing module. In the step, firstly, the data to be processed is subjected to iterative processing to obtain processing result data, and then when digital signature is carried out, the step comprises the steps of 3a, the digital signature of the data to be processed and the processing result data are added together to obtain basic information needing to be subjected to digital signature; step 3b, the data processing module digitally signs using a digital certificate issued by a central digital certificate authority.
And 4, after each data processing module is based on the processing flow of the step 3, a digital signature chain of an iterative process from the source data to the final processing result data can be established.
And step 5, the credibility of the whole data processing iterative process can be retrospectively confirmed by sequentially verifying the digital signature chain.
It is easy to find that the invention adopts the credible traceability chain formed by the digital signature to finish credible traceability of the data processing iterative process, can avoid the performance loss caused by adopting the distributed block chain scheme, and is more suitable for the application scene of data rapid iterative processing required by deep learning. According to the invention, the digital signature is carried out by attaching the digital signature of the input data and the content of the output data together, so that a complete digital signature chain from the input data to the output data can be formed, and the quick verification can be completed by traversing the digital signature chain during the trusted traceability verification.
The second embodiment of the invention relates to a deep learning-oriented data credible traceability marking system, which comprises: the first receiving module is used for receiving data to be processed; the processing signature module is used for carrying out iterative processing on the data to be processed to obtain processing result data, and carrying out digital signature on the processing result data by using a digital certificate; and the digital signature chain construction module is used for establishing a digital signature chain of an iterative process from the source data to the final processing result data based on the obtained plurality of digital signatures.
The deep learning-oriented data credible traceability marking system further comprises: and the verification module is used for verifying the digital signature attached to the data to be processed so as to confirm the credibility of the data input.
The deep learning-oriented data trusted traceability marking system further comprises a second receiving module, wherein the second receiving module is used for receiving the deep learning-oriented digital certificate issued by the central digital certificate issuing institution.
The processing signature module comprises: the processing unit is used for carrying out iterative processing on the data to be processed to obtain processing result data; the merging unit is used for merging the digital signature of the data to be processed and the processing result data into basic information; and the signature unit is used for digitally signing the basic information by using the digital certificate.
The deep learning-oriented data credible traceability marking system further comprises: and the verification module is used for sequentially verifying through the digital signature chain to retrospectively confirm the credibility of the whole data processing iterative process.
Claims (4)
1. The deep learning-oriented data credible traceability marking method is characterized by comprising the following steps of:
receiving data to be processed;
The data to be processed is subjected to iterative processing, and digital certificates are used for carrying out digital signature on the processing result data, specifically:
performing iterative processing on the data to be processed to obtain processing result data;
Combining the digital signature of the data to be processed and the processing result data into basic information;
Digitally signing the base information using the digital certificate;
Establishing a digital signature chain of an iterative process from source data to final processing result data based on the obtained digital signatures;
sequentially verifying through the digital signature chain to retrospectively confirm the credibility of the whole data processing iterative process;
The method further comprises the step of receiving the digital certificate which is issued by a central digital certificate issuing organization and faces deep learning before receiving the data to be processed.
2. The deep learning oriented data credible traceability marking method according to claim 1, further comprising verifying a digital signature attached to the data to be processed to confirm credibility of data input after receiving the data to be processed.
3. The deep learning-oriented data credible traceability marking system is characterized by comprising:
The first receiving module is used for receiving data to be processed;
The processing signature module is used for carrying out iterative processing on the data to be processed to obtain processing result data, and carrying out digital signature on the processing result data by using a digital certificate; the processing signature module comprises:
the processing unit is used for carrying out iterative processing on the data to be processed to obtain processing result data;
the merging unit is used for merging the digital signature of the data to be processed and the processing result data into basic information;
a signature unit for digitally signing the base information using the digital certificate;
The digital signature chain construction module is used for establishing a digital signature chain of an iterative process from source data to final processing result data based on the obtained digital signatures;
The verification module is used for sequentially verifying through the digital signature chain to retrospectively confirm the credibility of the whole data processing iterative process;
And the second receiving module is used for receiving the digital certificate which is issued by the central digital certificate issuing organization and is oriented to deep learning.
4. The deep learning oriented data trusted traceability marking system of claim 3, further comprising:
and the verification module is used for verifying the digital signature attached to the data to be processed so as to confirm the credibility of the data input.
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