CN115758432A - Omnibearing data encryption method and system based on machine learning algorithm - Google Patents
Omnibearing data encryption method and system based on machine learning algorithm Download PDFInfo
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Abstract
The application provides an omnibearing data encryption method and system based on a machine learning algorithm, and relates to the field of data encryption. An omnibearing data encryption method based on a machine learning algorithm comprises the following steps: constructing corresponding encryption labels based on data sources with different security levels through a data owner node; the tag party generates a public key and a private key by using a homomorphic encryption library, and encrypts data sources with different security levels by using a homomorphic encryption data algorithm to generate corresponding ciphertext data; and dynamically authorizing the key and the generated corresponding ciphertext data, and recording the access record. The core of production data safety can be used, an intelligent database safety management and control mechanism is researched and developed, and the database safety management and control strength is improved. In addition, the application also provides an omnibearing data encryption system based on the machine learning algorithm.
Description
Technical Field
The application relates to the field of data encryption, in particular to an omnibearing data encryption method and system based on a machine learning algorithm.
Background
In the prior art, the data source is complex: data resources can not be unified, various data of various units are difficult to store, centralize and collect;
unordered data storage: the method has the advantages that no data classification standard, no data management standard and no data safe use standard exist;
coarse data access: the method has a precautionary measure, but has no access measure aiming at data, so that the interface is more in call and cannot be controlled;
the safety means are limited: simple access control; no effective control means exists;
data flow to no trace: data access control is insufficient, and no interface is safe;
the audit content utilization is low: the platform audits too much content, and the threat risk cannot be centralized;
the safety measures are lacked: administrative requirements, data sharing and publicizing;
data sharing flows to no trace: lack of a tracking mechanism;
data attributes cannot be identified: lack of anti-repudiation means.
Disclosure of Invention
The application aims to provide an all-round data encryption method based on a machine learning algorithm, which can use the core of production data safety to research and develop an intelligent database safety control mechanism and improve the database safety control strength.
Another object of the present application is to provide an all-round data encryption system based on machine learning algorithm, which can operate an all-round data encryption method based on machine learning algorithm.
The embodiment of the application is realized as follows:
in a first aspect, an embodiment of the present application provides an omni-directional data encryption method based on a machine learning algorithm, which includes constructing, by a data owner node, corresponding encryption tags based on data sources of different security levels; the tag party generates a public key and a private key by using a homomorphic encryption library, and encrypts data sources with different security levels by using a homomorphic encryption data algorithm to generate corresponding ciphertext data; and dynamically authorizing the key and the generated corresponding ciphertext data, and recording the access record.
In some embodiments of the present application, the constructing, by the data owner node, the corresponding encrypted tags based on the data sources with different security levels includes: the data source comprises characteristic information, wherein the characteristic information is head information of the data source, and the row number, the column number and the separator of the data source.
In some embodiments of the present application, the above further includes: and acquiring a full data object in the data source, wherein the full data object comprises a main external unique key, an index and a sequence storage process, and main external key relevance, data service characteristics, key desensitization and reversible desensitization are reserved.
In some embodiments of the present application, the tag party generates a public key and a private key by using a homomorphic encryption library, and encrypts data sources with different security levels by using a homomorphic encryption data algorithm, and generating corresponding ciphertext data includes: the tag party decrypts the encrypted machine learning weight data by using the private key to obtain the machine learning weight data, and encrypts data sources with different security levels based on the machine learning weight data and a homomorphic encryption data algorithm.
In some embodiments of the present application, the above further includes: and training and adjusting parameters of the machine learning model to be used by using data sources with different security levels to obtain the optimal machine learning model.
In some embodiments of the present application, the dynamically granting the right to the key and the generated corresponding ciphertext data, and recording the access record includes: and (4) configuring desensitization engineering aiming at a user so as to make corresponding configuration and modification on a desensitized source library, a desensitized target library, a desensitization scheme, an execution plan and the like.
In some embodiments of the present application, the above further includes: submitting user access information, confirming the authenticity of the access information, and if the access information is wrong, ending the request; if the access information is correct, sending a dynamic check code, inputting the provided dynamic check code by a user, judging whether the dynamic check code is correct, if the dynamic check code is wrong, ending the request, and if the dynamic check code is correct, establishing a database access channel.
In a second aspect, an embodiment of the present application provides an omnibearing data encryption system based on a machine learning algorithm, which includes an encryption tag module, configured to construct, by a data owner node, corresponding encryption tags based on data sources with different security levels;
the system comprises a ciphertext data module, a public key generation module, a private key generation module, a ciphertext data generation module, a data source encryption module and a data source encryption module, wherein the ciphertext data module is used for generating a public key and a private key by using a homomorphic encryption library by a tag party, and encrypting data sources with different security levels by using a homomorphic encryption data algorithm to generate corresponding ciphertext data;
and the dynamic empowerment module is used for dynamically empowering the secret key and the generated corresponding ciphertext data and recording the access record.
In some embodiments of the present application, the above includes: at least one memory for storing computer instructions; at least one processor in communication with the memory, wherein the at least one processor, when executing the computer instructions, causes the system to: the system comprises an encryption tag module, a ciphertext data module and a dynamic empowerment module.
In a third aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method as any one of machine learning algorithm-based omnibearing data encryption methods.
Compared with the prior art, the embodiment of the application has at least the following advantages or beneficial effects:
the normal use of the existing database can be guaranteed, sensitive information of the database is prevented from being leaked, the access authority of the database is controlled in order, an operator is dynamically intercepted to damage the database unintentionally or intentionally, safety audit and filing are carried out on access behaviors of a system, and a corresponding operation specification is formulated by matching with a database safety control platform.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic diagram illustrating steps of an omni-directional data encryption method based on a machine learning algorithm according to an embodiment of the present application;
fig. 2 is a detailed step diagram of an all-round data encryption method based on a machine learning algorithm according to an embodiment of the present application;
fig. 3 is a schematic diagram of an omni-directional data encryption system module based on a machine learning algorithm according to an embodiment of the present application;
fig. 4 is an electronic device according to an embodiment of the present disclosure.
Icon: 10-an encrypted tag module; 20-ciphertext data module; 30-a dynamic empowerment module; 101-a memory; 102-a processor; 103-communication interface.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
It is to be noted that the term "comprises," "comprising," or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the individual features of the embodiments can be combined with one another without conflict.
Example 1
Referring to fig. 1, fig. 1 is a schematic diagram of steps of an omni-directional data encryption method based on a machine learning algorithm according to an embodiment of the present application, which is shown as follows:
step S100, constructing corresponding encryption labels based on data sources with different security levels through a data owner node;
in some embodiments, since the process of cryptographic machine learning training in the LatticeX-Rosetta framework involves multiple participants, and the data used for training may be provided by multiple participants separately, the participants cannot share their data publicly due to competitive advantages, privacy concerns, regulations, and issues regarding data ownership and jurisdiction, and therefore, the data has an owner relationship property, and the multiple participants may be classified as data owners and non-data owners. In this embodiment, the encryption tag is an abstract concept, and is an abstraction of data sources with different security levels, the encryption tag can be regarded as an input channel, and external data transmission to a program needs to be performed by means of the encryption tag. Since the data owner node can directly construct the encryption tag using the API of tf.data, the data owner node can construct the encryption tag based on the private data source. The tf.data is an API (application programming interface) for building a data reading pipeline, and the data pipeline is built by using the API of the tf.data, and mainly depends on two APIs: the data reader comprises tf.data.Dataset and tf.data.Iterator, wherein the tf.data.Dataset is used for reading data and preprocessing, and the read data depends on a tf.data.Iterator interface.
But in the state that the data is an original value (private data), for a computing node owning the data, the computing node can still use the API (interface) of tf.
Step S110, the tag party generates a public key and a private key by using a homomorphic encryption library, and encrypts data sources with different security levels by using a homomorphic encryption data algorithm to generate corresponding ciphertext data;
in some embodiments, an input matrix and a public key are obtained; the number of rows of the input matrix is the size of the characteristic data set, and the number of columns is the characteristic number of the characteristic data; dividing the input matrix to obtain a plurality of sub-matrices; calculating the encrypted ciphertext data of each submatrix according to the public key; each data source with different security levels supports single instruction multiple data stream SIMD operation; packaging and outputting the data sources with different security levels after the sub-matrixes are encrypted to obtain encrypted feature ciphertext data; where packing is the assembly of a number of common scalars into polynomials.
And step S120, dynamically authorizing the key and the generated corresponding ciphertext data, and recording the access record.
In some embodiments, the original data to be predicted or classified is encrypted by using a corresponding key, and an optimal machine learning model is input to obtain a prediction or classification result, wherein the specific process is as follows: preprocessing original data to be predicted or classified to enable the original data to be matched with a plaintext space of a selected encryption algorithm; encrypting the preprocessed data by using a key stored in a key management system to generate corresponding ciphertext data to be predicted or classified; and inputting the ciphertext data to be predicted or classified into the trained optimal machine learning model to obtain a prediction or classification result.
When ciphertext data to be predicted or classified is predicted or classified through the optimal machine learning model, if original data to be predicted or classified is normally encrypted, the optimal machine learning model outputs a normal prediction result according to a learning result; otherwise, the results output by the optimal machine learning model are unpredictable and unreliable.
Example 2
Referring to fig. 2, fig. 2 is a detailed step diagram of an omni-directional data encryption method based on a machine learning algorithm according to an embodiment of the present application, which is shown as follows:
in step S200, the data source includes characteristic information, where the characteristic information is header information of the data source, and the number of rows, columns, and delimiters of the data source.
Step S210, acquiring a full data object in the data source, wherein the full data object comprises a main external unique key, an index and a sequence storage process, and main external key relevance, data service characteristics, key desensitization and reversible desensitization are reserved.
And S220, the labeler decrypts the encrypted machine learning weight data by using the private key to obtain the machine learning weight data, and encrypts data sources with different security levels based on the machine learning weight data and a homomorphic encryption data algorithm.
And step S230, training and parameter adjustment are carried out on the machine learning model needing to be used by using data sources with different security levels, so as to obtain an optimal machine learning model.
Step S240, configuring the desensitization project for the user, so as to make corresponding configuration and modification on the desensitized source library, target library, desensitization scheme, execution plan, and the like.
Step S250, submitting user access information, confirming the authenticity of the access information, and if the access information is wrong, ending the request; if the access information is correct, sending a dynamic check code, inputting the provided dynamic check code by a user, judging whether the dynamic check code is correct, if the dynamic check code is wrong, ending the request, and if the dynamic check code is correct, establishing a database access channel.
In some embodiments, the desensitization thread number, desensitization cache path, and user security related configuration of the system may be self-configured to accommodate different data volumes and security levels of users. And flexible and configurable desensitization rule plug-in extension functions are supported, and all installed plug-ins can perform management configuration, uninstallation and other visualization operations through an interface. And an authority system with discrete users, roles and authorities is adopted, so that the personalized authority management of the users is supported to the maximum extent. And the relevant management of addition, modification, deletion, connectivity test and the like of an administrator and a user on a desensitized data source (source library/target library) is supported. And providing a user configuration desensitization project to make corresponding configuration and modification on a desensitized source library, a desensitization target library, a desensitization scheme, an execution plan and the like.
Modeling and training learning of the new intelligent task model; iterative upgrade evolution learning of the existing algorithm model; joint learning based on knowledge embedding; safety management of the algorithm model; intelligent distribution and transmission of the model and life cycle management; federal learning based on privacy protection.
The attribute information of the data source may include field information. In practical applications, it is pre-configured which data can be subjected to data desensitization. Although the user selects some to-be-processed data which needs to be subjected to data desensitization operation, some to-be-processed data programs cannot be subjected to data desensitization operation, and target data which can be subjected to data desensitization operation by the programs are determined from the to-be-processed data.
The field description in the field information may describe some basic information of the field, such as the card number data of the field, the date of the field, the account number including check digits of the field, and the like. The field type information and the field description in the field information can be obtained from the database, and semantic analysis is performed on the field description based on the field content information, so that the desensitization category of the data to be processed can be obtained. And screening out the data to be processed of which the desensitization category is a preset desensitization category, and taking the data as target data needing data desensitization operation. In practical applications, the desensitization category capable of performing data desensitization operations is a preset desensitization type.
In practical application, a plurality of desensitization modes are configured in advance, such as a comparison table, data replacement, offset rounding, mask masking, a random value, a fixed value, operation, encryption and the like. Desensitization can be performed using one of a number of desensitization modalities on the same data. Such as card number, data replacement, fixed value, etc.
When the target data can be desensitized by a plurality of desensitization modes, one desensitization mode needs to be selected from the plurality of desensitization modes for desensitization.
Example 3
Referring to fig. 3, fig. 3 is a schematic diagram of an omnidirectional data encryption system module based on a machine learning algorithm according to an embodiment of the present disclosure, which is as follows:
the encrypted label module 10 is used for constructing corresponding encrypted labels based on data sources with different security levels through the data owner node;
the ciphertext data module 20 is used for generating a public key and a private key by using the homomorphic encryption library by the tag party, and encrypting data sources with different security levels by using a homomorphic encryption data algorithm to generate corresponding ciphertext data;
and the dynamic empowerment module 30 is configured to dynamically empowerment the key and the generated corresponding ciphertext data, and record the access record.
As shown in fig. 4, an embodiment of the present application provides an electronic device, which includes a memory 101 for storing one or more programs; a processor 102. The one or more programs, when executed by the processor 102, implement the method of any of the first aspects as described above.
Also included is a communication interface 103, and the memory 101, processor 102 and communication interface 103 are electrically connected to each other, directly or indirectly, to enable transfer or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules, and the processor 102 executes the software programs and modules stored in the memory 101 to thereby execute various functional applications and data processing. The communication interface 103 may be used for communicating signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a Random Access Memory 101 (RAM), a Read Only Memory 101 (ROM), a Programmable Read Only Memory 101 (PROM), an Erasable Read Only Memory 101 (EPROM), an electrically Erasable Read Only Memory 101 (EEPROM), and the like.
The processor 102 may be an integrated circuit chip having signal processing capabilities. The Processor 102 may be a general-purpose Processor 102, including a Central Processing Unit (CPU) 102, a Network Processor 102 (NP), and the like; but may also be a Digital Signal processor 102 (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware components.
In the embodiments provided in the present application, it should be understood that the disclosed method and system can be implemented in other ways. The method and system embodiments described above are merely illustrative and, for example, the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In another aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by the processor 102, implements the method according to any one of the first aspect described above. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory 101 (ROM), a Random Access Memory 101 (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
To sum up, the omnibearing data encryption method and system based on the machine learning algorithm can guarantee normal use of the existing database, avoid leakage of sensitive information of the database, control access authority of the database in order, dynamically intercept the unintentional or intentional damage of operators to the database, perform security audit and file on system implementation access behaviors, and formulate corresponding operation specifications in cooperation with a database security control platform.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. An omnibearing data encryption method based on a machine learning algorithm is characterized by comprising the following steps:
constructing corresponding encryption labels based on data sources with different security levels through a data owner node;
the tag party generates a public key and a private key by using a homomorphic encryption library, and encrypts data sources with different security levels by using a homomorphic encryption data algorithm to generate corresponding ciphertext data;
and dynamically authorizing the key and the generated corresponding ciphertext data, and recording the access record.
2. The method of claim 1, wherein the constructing, by the data owner node, the corresponding encryption label based on the data sources with different security levels comprises:
the data source comprises characteristic information, wherein the characteristic information is header information of the data source, and the row number, the column number and the separator of the data source.
3. The omnibearing data encryption method based on machine learning algorithm according to claim 2, further comprising:
and acquiring a full data object in the data source, wherein the full data object comprises a main external unique key, an index and a sequence storage process, and main external key relevance, data service characteristics, key desensitization and reversible desensitization are reserved.
4. The omnibearing data encryption method based on machine learning algorithm as claimed in claim 1, wherein said tag party uses a homomorphic encryption library to generate public key and private key, and uses homomorphic encryption data algorithm to encrypt data sources with different security levels, and generate corresponding ciphertext data comprises:
the tag party decrypts the encrypted machine learning weight data by using the private key to obtain the machine learning weight data, and encrypts data sources with different security levels based on the machine learning weight data and a homomorphic encryption data algorithm.
5. The omnibearing data encryption method based on machine learning algorithm as claimed in claim 4, further comprising:
and training and adjusting parameters of the machine learning model to be used by using data sources with different security levels to obtain the optimal machine learning model.
6. The machine-learning-algorithm-based omnibearing data encryption method according to claim 1, characterized by that said dynamically-empowering key and generated correspondent cipher text data, and recording access record includes:
and (4) configuring desensitization engineering aiming at a user so as to make corresponding configuration and modification on a desensitized source library, a desensitized target library, a desensitization scheme, an execution plan and the like.
7. The omnibearing data encryption method based on machine learning algorithm according to claim 6, further comprising:
submitting user access information, confirming the authenticity of the access information, and if the access information is wrong, ending the request; if the access information is correct, sending a dynamic check code, inputting the provided dynamic check code by a user, judging whether the dynamic check code is correct, if the dynamic check code is wrong, ending the request, and if the dynamic check code is correct, establishing a database access channel.
8. An all-round data encryption system based on machine learning algorithm, comprising:
the encrypted label module is used for constructing corresponding encrypted labels based on data sources with different security levels through the data owner node;
the system comprises a ciphertext data module, a data processing module and a data processing module, wherein the ciphertext data module is used for generating a public key and a private key by a label party by using a homomorphic encryption library, and encrypting data sources with different security levels by using a homomorphic encryption data algorithm to generate corresponding ciphertext data;
and the dynamic empowerment module is used for dynamically empowering the secret key and the generated corresponding ciphertext data and recording the access record.
9. The machine-learning algorithm-based all-round data encryption system of claim 8, comprising:
at least one memory for storing computer instructions;
at least one processor in communication with the memory, wherein the at least one processor, when executing the computer instructions, causes the system to perform: the system comprises an encryption tag module, a ciphertext data module and a dynamic empowerment module.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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| CN117592092A (en) * | 2024-01-19 | 2024-02-23 | 山东铭云信息技术有限公司 | Secret checking method and system for database content |
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| CN117592092A (en) * | 2024-01-19 | 2024-02-23 | 山东铭云信息技术有限公司 | Secret checking method and system for database content |
| CN117592092B (en) * | 2024-01-19 | 2024-04-05 | 山东铭云信息技术有限公司 | Secret checking method and system for database content |
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