CN114938311A - Data processing method and system based on artificial intelligence - Google Patents
Data processing method and system based on artificial intelligence Download PDFInfo
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- CN114938311A CN114938311A CN202210865051.5A CN202210865051A CN114938311A CN 114938311 A CN114938311 A CN 114938311A CN 202210865051 A CN202210865051 A CN 202210865051A CN 114938311 A CN114938311 A CN 114938311A
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- H—ELECTRICITY
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- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
- H04L63/0478—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload applying multiple layers of encryption, e.g. nested tunnels or encrypting the content with a first key and then with at least a second key
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
- H04L63/045—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload wherein the sending and receiving network entities apply hybrid encryption, i.e. combination of symmetric and asymmetric encryption
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/12—Applying verification of the received information
- H04L63/123—Applying verification of the received information received data contents, e.g. message integrity
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- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
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- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/14—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using a plurality of keys or algorithms
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- 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
- H04L9/3236—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 using cryptographic hash functions
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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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Abstract
The invention provides a data processing method and a system based on artificial intelligence, wherein the method comprises the following steps: collecting sample data, and converting the sample data into TFRecord data; encrypting the TFRecord data by adopting a symmetric encryption algorithm SM 2; carrying out asymmetric encryption on the data of S2 by adopting an asymmetric encryption algorithm SM3 to obtain an encrypted data set; carrying out SM4 digest encryption on the data set to check whether the sample data is tampered; constructing a model according to the data set for training; performing SM4 verification according to the data set to confirm whether the data is tampered; decrypting the data by adopting an asymmetric encryption algorithm SM3 algorithm; finally decrypting the data through a symmetric encryption algorithm SM2 algorithm; and acquiring the decrypted TFRecord through the data set, and performing a subsequent training task of the model. The purpose of this application is to ensure that training data is not revealed.
Description
Technical Field
The invention relates to the technical field of data center networks, in particular to a data processing method and system based on artificial intelligence.
Background
With the rapid development of Artificial Intelligence (AI) technology, the main content studied by AI is about the algorithm for generating "models" from data on a computer, i.e., "learning algorithm". The process of learning a model from data is called "learning" or "training", and is accomplished by executing some learning algorithm. The data used in the training process is called "training data".
The training data used for model training is stored and used on a training machine (equipment for completing training), so that the risk of leakage exists, the safety of the training data is low, and a method for ensuring that the training data is not leaked is needed.
Disclosure of Invention
The invention provides a programmable data plane flow scheduling method based on network behavior prediction, and aims to solve the problem that training data are easy to leak in the prior art and the like.
In order to solve the above technical problem and achieve the above object, a first aspect of the present invention provides a data processing method based on artificial intelligence, the method including:
s1, collecting sample data by the system, and converting the sample data into TFRecord data;
s2, encrypting the TFRecord data by adopting a symmetric encryption algorithm SM 2;
s3, carrying out asymmetric encryption on the data of the S2 by adopting an asymmetric encryption algorithm SM3 to obtain an encrypted data set;
s4, carrying out SM4 digest encryption on the data set of S3, and verifying whether the sample data is tampered once;
s5, constructing a model according to the data set of S4 for training:
obtaining data of S4, wherein the problem data carries a model processing result;
calculating the similarity between the model training sample and the problem data;
updating labels of the model training samples with the similarity meeting the preset conditions;
taking the model training sample and the problem data after the label is updated as model training data;
s6, carrying out SM4 verification according to the data set of the S4 to confirm whether the data are tampered;
s7, decrypting the data of S6 by adopting an asymmetric encryption algorithm SM3 algorithm;
and S8, finally decrypting the data through the symmetric encryption algorithm SM2 algorithm.
Further, the data set encrypted by the S3 using the asymmetric encryption algorithm SM3 is stored in MINIO.
Further, the MINIO is used for storing data of the data set, version data of the TFRecord and storing large-capacity unstructured data.
Further, the version data of the TFRecord includes picture self information, data tagging information, and tagging name information.
Further, the storage large-capacity unstructured data are pictures, videos, log files, backup data and container/virtual machine images.
Further, the key elements of the MINIO include a storage number, a bucket name, and a path of a file within a bucket.
In a second aspect the present invention provides an artificial intelligence based data processing system comprising:
the client is used for collecting sample data;
the application layer server is used for sending a request to the block chain platform to call a service interface provided by the block chain platform and receiving a result returned by the block chain platform;
and the block chain platform is used for creating a unique identification ID, encrypting the data set and forming a ciphertext, chaining the formed ciphertext and associating the unique identification ID, inquiring the ciphertext, homomorphically encrypting the ciphertext obtained by inquiry and decrypting the sum of the obtained ciphertexts.
Further, the blockchain platform comprises:
the consensus node is used for receiving the data interaction request sent by the application layer server and broadcasting the signed and encrypted data interaction request to other consensus nodes in the block chain network;
the proxy node is used for receiving a data interaction request sent by the application layer server and packaging data interaction;
and the authentication node is used for providing identity authentication and certificate issuing services for the consensus node and the proxy node.
Further, the consensus node comprises:
the consensus encryption module is used for encrypting, decrypting, signing and signature checking of data interaction, homomorphic addition of encrypted ciphertext, application and management of various certificates and keys and calculation of a hash value;
the consensus module is used for carrying out consensus operation on the data interaction request together with the consensus modules of other consensus nodes in the same block chain network according to the selected consensus algorithm;
the intelligent contract module is used for verifying the consensus result;
the consensus communication module is used for carrying out data interaction with other consensus nodes;
and the consensus storage module is used for storing the data generated by the verification node and the cross-correlation data of the data into a database.
Further, the proxy node includes:
the agent encryption module is used for encrypting, decrypting, signing and verifying data interaction, applying and managing homomorphic encryption keys and calculating a hash value;
the agent communication module is used for carrying out data interaction with other agent nodes;
the proxy storage module is used for storing the data generated by the verification node and cross-correlation of the data into a database;
the trusted key storage module is used for keeping secret of a homomorphic public key PK and a homomorphic private key SK, binding an endorsement key EK with storage equipment hardware and verifying the reliability of the storage equipment, encrypting and decrypting the homomorphic key SK by using a root key SRK, and generating a key tree to be stored in external equipment;
and the trusted execution environment module is used for safely acquiring homomorphic public keys and private keys of financial institutions to which the ciphertext data belong in a chain in a trusted environment before executing the homomorphic operation of the ciphertext, performing aggregation operation on the private keys and outputting results, and calculating the aggregation keys without revealing keys of all institutions for homomorphic operation.
The beneficial technical effects of the invention are at least as follows:
(1) according to the invention, the data trained by the AI model is used, so that the safety of the data can be effectively improved, and the data is ensured not to be easily leaked;
(2) the invention adopts MinIO to store the data set, is a very light service, and can be combined with other applications.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a schematic diagram of an artificial intelligence based data processing method according to the present invention;
FIG. 2 is a schematic flow chart of the present invention using algorithmic encryption;
fig. 3 is a schematic flow chart of decryption using an algorithm according to the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
As shown in fig. 1, a data processing method based on artificial intelligence is characterized by comprising the following steps:
as shown in fig. 2, S1, the system collects sample data, and converts the sample data into TFRecord data;
s2, encrypting the TFRecord data by adopting a symmetric encryption algorithm SM 2;
s3, carrying out asymmetric encryption on the data of the S2 by adopting an asymmetric encryption algorithm SM3 to obtain an encrypted data set;
s4, carrying out SM4 digest encryption on the data set of S3, and verifying whether the sample data is tampered once;
s5, constructing a model according to the data set of S4 and training;
obtaining data of S4, wherein the problem data carries a model processing result;
calculating the similarity between the model training sample and the problem data;
updating labels of model training samples with similarity meeting preset conditions;
taking the model training sample and the problem data after the label is updated as model training data;
as shown in fig. 3, S6, performing SM4 check according to the data set of S4 to confirm whether the data is tampered;
s7, decrypting the data of S6 by adopting an asymmetric encryption algorithm SM3 algorithm;
s8, carrying out final decryption on the data through a symmetric encryption algorithm SM2 algorithm;
and S9, acquiring the decrypted TFRecord through the data set of S8, and performing a subsequent training task of the model.
Specifically, the data set encrypted by the S3 using the asymmetric encryption algorithm SM3 is stored in MINIO.
Specifically, the MINIO is used for storing data of a data set, version data of TFRecord and storing large-capacity unstructured data.
Specifically, the version data of the TFRecord includes picture information, data label information, and label name information.
Specifically, the storage large-capacity unstructured data are pictures, videos, log files, backup data and container/virtual machine images.
Specifically, the key elements of the MINIO include a storage number, a bucket name, and a path of a file within a bucket.
In a second aspect the present invention provides an artificial intelligence based data processing system comprising:
the client is used for collecting sample data;
the application layer server is used for sending a request to the blockchain platform to call a service interface provided by the blockchain platform and receiving a result returned by the blockchain platform;
and the block chain platform is used for creating a unique identification DID, encrypting the data set and forming a ciphertext, chaining the formed ciphertext and associating the unique identification DID, inquiring the ciphertext, homomorphically encrypting the ciphertext obtained by inquiry and decrypting the sum of the obtained ciphertexts.
Specifically, the blockchain platform includes:
the consensus node is used for receiving the data interaction request sent by the application layer server and broadcasting the signed and encrypted data interaction request to other consensus nodes in the block chain network;
the proxy node is used for receiving a data interaction request sent by the application layer server and packaging data interaction;
and the authentication node is used for providing identity authentication and certificate issuing services for the consensus node and the proxy node.
Specifically, the consensus node includes:
the consensus encryption module is used for encrypting, decrypting, signing and verifying data interaction, homomorphic addition of encrypted ciphertext, application and management of various certificates and keys and calculation of a hash value;
the consensus module is used for carrying out consensus operation on the data interaction request together with the consensus modules of other consensus nodes in the same block chain network according to the selected consensus algorithm;
the intelligent contract module is used for verifying the consensus result;
the consensus communication module is used for carrying out data interaction with other consensus nodes;
and the consensus storage module is used for storing the data generated by the verification node and the cross-correlation data of the data into a database.
Specifically, the proxy node includes:
the agent encryption module is used for encrypting, decrypting, signing and verifying data interaction, applying and managing homomorphic encryption keys and calculating a hash value;
the agent communication module is used for carrying out data interaction with other agent nodes;
the proxy storage module is used for storing the blocks generated by the verification nodes and the data cross-correlation data into a database;
the trusted key storage module is used for keeping secret of a homomorphic public key PK and a homomorphic private key SK, binding an endorsement key EK with storage equipment hardware and verifying the reliability of the storage equipment, encrypting and decrypting the homomorphic key SK by using a root key SRK, and generating a key tree to be stored in external equipment;
and the trusted execution environment module is used for safely acquiring homomorphic public keys and private keys of financial institutions to which the ciphertext data belong in a chain in a trusted environment before executing the homomorphic operation of the ciphertext, performing aggregation operation on the private keys and outputting results, and calculating the aggregation keys without revealing keys of all institutions for homomorphic operation.
To sum up, the patent provides a data processing method and system based on artificial intelligence, which is specially designed for training data of an application model, and comprises a series of operations such as encryption and operation audit on the data. Data encryption is one of the key points of data security design. Compared with the existing homomorphic encryption method, the invention uses the homomorphic encryption algorithm with multiple keys, can avoid mechanisms decrypting ciphertext information of other mechanisms on a chain through a single homomorphic key, and protects privacy data of the mechanisms. The homomorphic public key and the private key are stored in the trusted key storage device for encryption, the device only stores the endorsement key and the root key, and the endorsement key is bound with the trusted key storage device and can be verified with a remote server, so that the device is ensured not to be maliciously tampered or controlled. The root key is used for encrypting the homomorphic public key, the private key and the signature, the key and the signature are encrypted to generate the key tree, the key tree can be stored in external equipment and is not limited by the storage space of the trusted key storage equipment, meanwhile, the security of the key is ensured, and the security coefficient of an encryption system is higher. The aggregation private key used by the ciphertext homomorphic operation is calculated and extracted in a trusted execution environment, homomorphic operation is completed on the premise that private keys of all mechanisms are not leaked, privacy data of the mechanisms are not leaked, and a safety system of an operation system is higher.
While embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (10)
1. A data processing method based on artificial intelligence, characterized in that the method comprises:
s1, collecting sample data, and converting the sample data into TFRecord data;
s2, encrypting the TFRecord data by adopting a symmetric encryption algorithm SM 2;
s3, carrying out asymmetric encryption on the data obtained in the S2 by adopting an asymmetric encryption algorithm SM3 to obtain an encrypted data set;
s4, carrying out SM 4-based digest encryption on the data set of S3, and checking whether the sample data is tampered or not;
s5, constructing a model according to the data set of S4 and training;
s6, performing SM4 verification according to the data set of S4 to confirm whether the data set is tampered;
s7, decrypting the data of S4 by adopting an asymmetric encryption algorithm SM3 algorithm;
s8, finally decrypting the data of S7 through the symmetric encryption algorithm SM2 algorithm;
and S9, acquiring the decrypted TFRecord through the data set of S8, and performing subsequent training tasks of the model.
2. The artificial intelligence based data processing method of claim 1, wherein the data set encrypted by the asymmetric encryption algorithm SM3 at S3 is stored in MINIO.
3. The artificial intelligence based data processing method of claim 2, wherein the MINIO is used for storing data of data sets, version data of TFRecord and storing large-capacity unstructured data.
4. The artificial intelligence based data processing method of claim 2, wherein the version data of TFRecord includes picture self information, data annotation information and annotation name information.
5. The artificial intelligence based data processing method of claim 3, wherein the storage large volume unstructured data are pictures, videos, log files, backup data and container/virtual machine images.
6. The artificial intelligence based data processing method of claim 2, wherein the key elements of MINIO include storage number, bucket name and path of file in bucket.
7. An artificial intelligence based data processing system for implementing the method of any of claims 1 to 6, comprising:
the client is used for collecting sample data;
the application layer server is used for sending a request to the blockchain platform to call a service interface provided by the blockchain platform and receiving a result returned by the blockchain platform;
and the block chain platform is used for creating a unique identification ID, encrypting the data set and forming a ciphertext, chaining the formed ciphertext and associating the unique identification ID, inquiring the ciphertext, homomorphically encrypting the ciphertext obtained by inquiry and decrypting the sum of the obtained ciphertexts.
8. An artificial intelligence based data processing system according to claim 7, wherein the blockchain platform comprises:
the consensus node is used for receiving the data interaction request sent by the application layer server and broadcasting the signed and encrypted data interaction request to other consensus nodes in the block chain network;
the proxy node is used for receiving a data interaction request sent by the application layer server and packaging data interaction;
and the authentication node is used for providing identity authentication and certificate issuing services for the consensus node and the proxy node.
9. The artificial intelligence based data processing system of claim 8, wherein the consensus node comprises:
the consensus encryption module is used for encrypting, decrypting, signing and verifying data interaction, homomorphic addition of encrypted ciphertext, application and management of various certificates and keys and calculation of a hash value;
the consensus module is used for carrying out consensus operation on the data interaction request together with the consensus modules of other consensus nodes in the same block chain network according to the selected consensus algorithm;
the intelligent contract module is used for verifying the consensus result;
the consensus communication module is used for carrying out data interaction with other consensus nodes;
and the consensus storage module is used for storing the data generated by the verification node and the cross-correlation data of the data into a database.
10. The artificial intelligence based data processing system of claim 8, wherein the agent node comprises:
the agent encryption module is used for encrypting, decrypting, signing and verifying data interaction, applying and managing homomorphic encryption keys and calculating a hash value;
the agent communication module is used for carrying out data interaction with other agent nodes;
the proxy storage module is used for storing the blocks generated by the verification nodes and the data cross-correlation data into a database;
the trusted key storage module is used for keeping secret of a homomorphic public key PK and a homomorphic private key SK, binding an endorsement key EK with storage equipment hardware and verifying the reliability of the storage equipment, encrypting and decrypting the homomorphic key SK by using a root key SRK, and generating a key tree to be stored in external equipment;
and the trusted execution environment module is used for safely acquiring homomorphic public keys and private keys of financial institutions to which the ciphertext data belong in a chain in a trusted environment before executing the homomorphic operation of the ciphertext, performing aggregation operation on the private keys and outputting results, and calculating the aggregation keys without revealing keys of all institutions for homomorphic operation.
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| CN202210865051.5A CN114938311A (en) | 2022-07-21 | 2022-07-21 | Data processing method and system based on artificial intelligence |
| CN202211195083.5A CN115766071A (en) | 2022-07-21 | 2022-09-29 | Data processing method and system based on artificial intelligence |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115766071A (en) * | 2022-07-21 | 2023-03-07 | 网思科技股份有限公司 | Data processing method and system based on artificial intelligence |
| CN117424760A (en) * | 2023-12-18 | 2024-01-19 | 西安旌旗电子股份有限公司 | Ammeter management method, control device and management device based on Internet of things |
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| CN114938311A (en) * | 2022-07-21 | 2022-08-23 | 网思科技股份有限公司 | Data processing method and system based on artificial intelligence |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115766071A (en) * | 2022-07-21 | 2023-03-07 | 网思科技股份有限公司 | Data processing method and system based on artificial intelligence |
| CN117424760A (en) * | 2023-12-18 | 2024-01-19 | 西安旌旗电子股份有限公司 | Ammeter management method, control device and management device based on Internet of things |
| CN117424760B (en) * | 2023-12-18 | 2024-03-12 | 西安旌旗电子股份有限公司 | Ammeter management method, control device and management device based on Internet of things |
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