Artificial intelligence data encryption method for big data

Abstract

The invention discloses a big data artificial intelligence data encryption method, and relates to the technical field of big data artificial intelligence data encryption, and the method comprises the steps: collecting to-be-encrypted data through a database, and carrying out the preprocessing of the to-be-encrypted data; performing security level classification on the to-be-encrypted data through artificial intelligence; performing encryption strength calculation according to the data security level division to generate an encryption key; performing encryption operation by using artificial intelligence; storing the encrypted data; and recording an audit log. According to the method, security level classification is performed on the data through the sensitivity of the data, the encryption key with the corresponding strength is generated according to the security level division of the data, and the data is encrypted by adopting an artificial intelligence encryption technology, so that the aims of higher-level and safer encryption of the high-sensitivity data are fulfilled, and the method has the advantages of improving the efficiency of encryption operation, reducing the cost and the like. And the encryption strategy can be flexibly adjusted according to the characteristics of the data and the environment change, so that the data security is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of big data artificial intelligence data encryption technology, and in particular to a big data artificial intelligence data encryption method. Background Art

[0002] In today's era of big data and artificial intelligence, the importance of data encryption methods continues to increase, especially in situations involving sensitive information processing and storage. With the explosive growth of data volume and the widespread application of artificial intelligence technology, how to effectively protect data security has become an urgent problem to be solved. Existing data encryption technologies are mainly based on static encryption algorithms and key management solutions, which often seem powerless when faced with complex and changing security threats.

[0003] There are several shortcomings in the existing technology when dealing with big data encryption. First, traditional encryption methods are difficult to achieve efficient data encryption and decryption operations in a big data environment, which limits the speed of data processing and analysis. Second, the application of fixed encryption strength cannot meet the personalized protection needs of data with different security levels, which may lead to excessive encryption of low-sensitivity data, wasting computing resources, or insufficient encryption of high-sensitivity data, exposing it to security risks. Summary of the invention

[0004] In view of the problems existing in the above-mentioned existing big data artificial intelligence data encryption methods, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that it is difficult for the existing technology to achieve efficient data encryption operations in a big data environment, and the application of fixed encryption strength cannot meet the personalized protection needs of data with different security levels.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a big data artificial intelligence data encryption method, which includes collecting data to be encrypted through a database and preprocessing the encrypted data; classifying the security level of the encrypted data through artificial intelligence; calculating the encryption strength according to the data security level classification and generating an encryption key; using artificial intelligence to perform encryption operations; storing the encrypted data; and recording audit logs.

[0007] As a preferred solution of the big data artificial intelligence data encryption method described in the present invention, the artificial intelligence collects data to be encrypted through a database, which refers to collecting data to be encrypted from personal users' bank account details, credit card information, investment records, health information, medical records, social security numbers, email addresses, log files generated by networks and applications, and open data platforms provided by enterprises and individuals, network shared folders, spreadsheets, governments, educational institutions and non-profit organizations.

[0008] As a preferred solution of the big data encryption method described in the present invention, the preprocessing of the encrypted data refers to applying natural language processing technology to parse text data, using image recognition technology to process image data, identifying the encrypted data, removing incomplete and duplicate data, and performing unified formatting processing.

[0009] As a preferred solution of the big data artificial intelligence data encryption method of the present invention, wherein: the security level classification of the encrypted data by artificial intelligence refers to constructing a security level score S formula for the encrypted data:

[0010]

[0011] Where S represents the security level score of data encryption, with a value range of [0,1], N represents the diversity index of data, α and β are coefficients adjusted according to data sensitivity and encryption algorithm complexity, λ represents the attenuation coefficient of data access frequency, θ is the real-time coefficient of data update, M represents the number of encryption algorithm types, i represents the index of encryption algorithm, d i represents the strength score of the i-th encryption algorithm, Ω, Ψ, and Φ are adjustment coefficients, K represents the number of sensitive data types, and h j represents the sensitivity level score of the jth category based on the sensitivity of the data, r j is the access control coefficient of the jth category of sensitive data, t is the independent variable in the integral operation, and T represents the time period of data access;

[0012] Calculate the security level assessment threshold Θ as:

[0013]

[0014] Where Θ represents the security level assessment threshold of data encryption, with a range of [0,1], and γ i is the weight coefficient of the i-th encryption algorithm, δ j is the weight coefficient of the jth category of sensitive data, and η is the adjustment coefficient;

[0015] The data security level is determined based on the security level assessment threshold Θ and the data encryption security level score S.

[0016] When S>Θ, it is judged as a low security level;

[0017] When S = Θ, it is determined to be a medium safety level;

[0018] When S<Θ, it is determined to be a high security level.

[0019] As a preferred solution of the big data artificial intelligence data encryption method of the present invention, wherein: the encryption strength calculation is performed according to the data security level classification, and the encryption key is generated by combining the security level assessment threshold Θ and the security level score S to form an encryption strength formula as follows:

[0020]

[0021] Where F is the final encryption strength, C is the data complexity, V is the amount of data to be encrypted, n is the index variable for summation, and x is the integral variable;

[0022] Generate encryption key:

[0023]

[0024] Among them: K is the encryption key finally generated.

[0025] As a preferred solution of the big data artificial intelligence data encryption method of the present invention, the encryption operation using artificial intelligence refers to generating an encryption key for the encrypted data:

[0026] R=A·e K +sin(π·K)

[0027] A is the data to be encrypted, and R is the final encryption result.

[0028] As a preferred solution of the big data artificial intelligence data encryption method described in the present invention, the storage of the encrypted data refers to allocating the encrypted data to low, medium, and high storage levels according to the security level of the data, performing multiple redundant backups of the encrypted data, and storing the backups in physically separated locations.

[0029] As a preferred solution to the big data artificial intelligence data encryption method described in the present invention, the audit log records all access and operation behavior timestamps, user identifiers, accessed data, and performed operation information for data. For modification or deletion operations, the data snapshots before and after the operation are recorded.

[0030] A computer device comprises: a memory and a processor; the memory stores a computer program, and is characterized in that: when the processor executes the computer program, the steps of the above-mentioned big data artificial intelligence data encryption method are implemented.

[0031] A computer-readable storage medium having a computer program stored thereon, characterized in that: when the computer program is executed by a processor, the steps of the above-mentioned big data artificial intelligence data encryption method are implemented.

[0032] The beneficial effects of the present invention are as follows: the present invention classifies the security level of the data according to its sensitivity, generates encryption keys of corresponding strengths according to the security level of the data, and uses artificial intelligence encryption technology to encrypt the data, thereby achieving more advanced and more secure encryption of highly sensitive data, improving the efficiency of encryption operations, and flexibly adjusting encryption strategies according to the characteristics of the data and environmental changes, greatly improving data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0034] Figure 1 Flowchart of the big data artificial intelligence data encryption method.

[0035] Figure 2 This is a structural diagram of the big data artificial intelligence data encryption method. DETAILED DESCRIPTION

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0037] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0038] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0039] Example 1

[0040] Reference Figure 1 , which is the first embodiment of the present invention, and this embodiment provides a big data artificial intelligence data encryption method, and the big data artificial intelligence data encryption method includes:

[0041] S1: Collect the data to be encrypted through the database and pre-process the data to be encrypted;

[0042] It should be noted that the collection of encrypted data through databases refers to the collection of encrypted data from personal users' bank account details, credit card information, investment records, health information, medical records, social security numbers, email addresses, log files generated by networks and applications, corporate and personal hard drives, network shared folders, spreadsheets, and open data platforms provided by governments, educational institutions and non-profit organizations.

[0043] By encrypting sensitive data such as personal user's bank account details, credit card information, etc., the user's financial security and personal privacy are effectively protected and the risks of data leakage are avoided. Encryption can not only prevent unauthorized access, but also ensure that data is not tampered with during transmission, thereby maintaining the integrity of the data. On the premise of ensuring data security, enterprises, governments and educational institutions can share data on open data platforms with greater confidence, which promotes cross-border cooperation and knowledge sharing. The use of encryption technology in the processing of data such as log files and email addresses generated by networks and applications can enhance consumers' trust in enterprises and enhance the company's brand image and market competitiveness. By uniformly collecting data from different sources and in different formats and integrating multiple data sources, the comprehensiveness and systematicness of data protection are ensured. Artificial intelligence can more accurately identify data that needs to be encrypted, providing an accurate and clear data source for encryption.

[0044] It should also be noted that preprocessing of encrypted data refers to applying natural language processing technology to parse text data, using image recognition technology to process image data, identifying encrypted data, removing incomplete and duplicate data, and performing unified formatting processing.

[0045] Natural language processing technology is a technology that allows computers to understand, interpret and generate human language through algorithms. The present invention is used to parse text data, identify sensitive information therein, use image recognition technology to process image data, and use convolutional neural networks to automatically extract image features through convolutional layers to identify sensitive information that may be contained therein. Through the application of natural language processing and image recognition technology, the leakage of sensitive information can be effectively prevented, duplicate and incomplete data can be removed, the demand for storage space can be significantly reduced, and the efficiency of data processing can be improved. By preprocessing the encrypted data, we can more accurately identify sensitive information that needs to be encrypted.

[0046] S2: Classify the security level of encrypted data through artificial intelligence;

[0047] Furthermore, using artificial intelligence to classify the security level of encrypted data means constructing a security level score S for encrypted data as follows:

[0048]

[0049] Where S represents the security level score of data encryption, with a value range of [0,1], N represents the diversity index of data, α and β are coefficients adjusted according to data sensitivity and the complexity length of encryption algorithm, λ represents the attenuation coefficient of data access frequency, θ is the real-time coefficient of data update, M represents the number of encryption algorithm types, i represents the index of encryption algorithm, d i represents the strength score of the i-th encryption algorithm, Ω, Ψ, and Φ are adjustment coefficients, K represents the number of sensitive data types, and h j represents the sensitivity level score of the jth category based on the sensitivity of the data, r j is the access control coefficient of the jth category of sensitive data, t is the independent variable in the integral operation, the lower limit of the integral is 0, the upper limit is T, and T represents the time period of data access;

[0050] Calculate the security level assessment threshold Θ as:

[0051]

[0052] Where Θ represents the security level assessment threshold of data encryption, with a range of [0,1], and γ i is the weight coefficient of the i-th encryption algorithm, δ j is the weight coefficient of the jth category of sensitive data, and η is the adjustment coefficient;

[0053] The data security level is determined based on the security level assessment threshold Θ and the data encryption security level score S.

[0054] When S>Θ, the closer the S value is to 1, the higher the security level is, and it is judged as a low security level.

[0055] When S=Θ, the S value is equal to the Θ value, indicating that the security level is average and is judged as the medium security level.

[0056] When S<Θ, the S value is close to 0, indicating that the security level of the data is low and is judged to be a high security level.

[0057] The security level score S is a quantitative indicator used to measure the security level of data encryption. The value range is [0,1], where values ​​close to 1 indicate a high security level and values ​​close to 0 indicate a low security level. η is an adjustment coefficient used to balance the impact of various parts in the formula. The closer the Θ value is to 1, the higher the security level. This is a preset threshold used to compare with the security level score S to determine the security level classification of the data. N represents the diversity index of the data, that is, the number of data types. Ω, Ψ, and Φ are adjustment coefficients used to balance the impact of various parts in the formula, ensuring that the security level classification of data encryption is more scientific and reasonable, implementing stricter encryption measures for high-risk data, and implementing more efficient encryption strategies for low-risk data, which not only ensures data security, but also optimizes resource allocation while increasing the flexibility and scalability of data protection.

[0058] S3: Calculate the encryption strength according to the data security level and generate the encryption key;

[0059] Specifically, the encryption strength is calculated according to the data security level classification, and the encryption key is generated by combining the security level assessment threshold Θ and the security level score S to form the encryption strength formula:

[0060]

[0061] Where F is the final encryption strength, C is the data complexity, V is the amount of data to be encrypted, n is the index variable for summation, and x is the integral variable;

[0062] Generate encryption key:

[0063]

[0064] Where K is the final generated encryption key.

[0065] C is the data complexity, which is quantified by the size of the encrypted data and is a positive integer value. n is the index variable for summation, representing a sequence from 1 to encryption strength F. x is the integral variable, representing a change within the data complexity C. By precisely controlling the encryption strength, it not only ensures that sensitive data is adequately protected, but also avoids over-encryption of low-sensitivity data, thereby optimizing the performance and efficiency of the encryption process. The generated encryption key has a high degree of security and complexity, making it extremely difficult to crack. At the same time, the uniqueness and pertinence of the key further enhance the security protection effect of data encryption.

[0066] S4: Use artificial intelligence for cryptographic operations;

[0067] It should be noted that the use of artificial intelligence for encryption operations refers to the generation of encryption keys for the encrypted data:

[0068] R=A·e K +sin(π·K)

[0069] A is the data to be encrypted, and R is the final encryption result.

[0070] Through this unique encryption method, high security of data encryption is achieved. The unpredictability of the encryption results greatly increases the difficulty for potential attackers to crack the encryption, thereby effectively protecting the security of the data. The implementation of this encryption method relies on the automated processing of intelligent algorithms, which improves the efficiency and reliability of encryption operations.

[0071] S5: storing the encrypted data;

[0072] It should also be noted that storing encrypted data means allocating the encrypted data to low, medium, and high storage levels according to the security level of the data, performing multiple redundant backups of the encrypted data, and storing the backups in physically separated locations.

[0073] By storing encrypted data in layers according to security levels, data with high security levels receives stronger protection measures. This layered approach can effectively reduce potential security threats and protect data from unauthorized access. By performing multiple redundant backups of encrypted data and storing the backups in physically separate locations, data can be quickly restored even when a storage system fails or encounters a disaster, ensuring business continuity and high data availability. Encrypted data is allocated to the corresponding storage tier according to its security level, facilitating the implementation of role-based access control and sophisticated permission management, and reducing storage costs.

[0074] S6: Record audit logs;

[0075] Furthermore, recording audit logs means recording the timestamp, user ID, data accessed, and information about the operations performed on all data accesses and operations. For modification or deletion operations, data snapshots before and after the operation are recorded.

[0076] By recording all access and operation behaviors to data, audit logs provide a mechanism to track and analyze security incidents when they occur, thereby enhancing overall data security. By analyzing audit logs, abnormal or abnormal behaviors in system operations can be identified. This detection is an important means to identify potential threats early and prevent data leaks. In the event that data is modified or deleted by mistake, data snapshots before and after the operation can be used to restore the lost information. Audit logs provide indispensable evidence for post-analysis and investigation of data security incidents. Recording audit logs makes all data operation behaviors transparent, which helps to build a work environment with a strong sense of responsibility and trust.

[0077] Example 2

[0078] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0079] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0080] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0081] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A big data artificial intelligence data encryption method, characterized by: include, Collect the data to be encrypted through the database and pre-process the data to be encrypted; Use artificial intelligence to classify the security level of encrypted data; Calculate encryption strength based on data security level and generate encryption keys; Use artificial intelligence for cryptographic operations; Storing the encrypted data; Record audit logs.

2. The big data artificial intelligence data encryption method according to claim 1, characterized in that: The collection of data to be encrypted through a database refers to the collection of data to be encrypted from personal users' bank account details, credit card information, investment records, health information, medical records, social security numbers, email addresses, log files generated by networks and applications, and open data platforms provided by corporate and personal hard drives, network shared folders, spreadsheets, governments, educational institutions, and non-profit organizations.

3. The big data artificial intelligence data encryption method according to claim 2, characterized in that: The preprocessing of the encrypted data refers to applying natural language processing technology to parse text data, using image recognition technology to process image data, identifying the encrypted data, removing incomplete and duplicate data, and performing unified formatting processing.

4. The big data artificial intelligence data encryption method according to claim 3, characterized in that: The security level classification of encrypted data by artificial intelligence refers to constructing a security level score S formula for the encrypted data: Where S represents the security level score of data encryption, with a value range of [0,1], N represents the diversity index of data, α and β are coefficients adjusted according to data sensitivity and encryption algorithm complexity, λ represents the attenuation coefficient of data access frequency, θ is the real-time coefficient of data update, M represents the number of encryption algorithm types, i represents the index of encryption algorithm, d i represents the strength score of the i-th encryption algorithm, Ω, Ψ, and Φ are adjustment coefficients, K represents the number of sensitive data types, and h j represents the sensitivity level score of the jth category based on the sensitivity of the data, r j is the access control coefficient of the jth category of sensitive data, t is the independent variable in the integral operation, and T represents the time period of data access; Calculate the security level assessment threshold Θ as: Where Θ represents the security level assessment threshold of data encryption, with a range of [0,1], and γ i is the weight coefficient of the i-th encryption algorithm, δ j is the weight coefficient of the jth category of sensitive data, and η is the adjustment coefficient; The data security level is determined based on the security level assessment threshold Θ and the data encryption security level score S. When S>Θ, it is judged as a low security level; When S = Θ, it is determined to be a medium safety level; When S<Θ, it is determined to be a high security level.

5. The big data artificial intelligence data encryption method according to claim 4, characterized in that: The encryption strength calculation is performed according to the data security level classification, and the encryption key is generated by combining the security level assessment threshold Θ and the security level score S to form an encryption strength formula: Where F is the final encryption strength, C is the data complexity, V is the amount of data to be encrypted, n is the index variable for summation, and x is the integral variable; Generate encryption key: Where K is the final generated encryption key.

6. The big data artificial intelligence data encryption method according to claim 5, characterized in that: The encryption operation using artificial intelligence refers to the encryption of the encrypted data by generating an encryption key: R=A·e K +sin(π K) A is the data to be encrypted, and R is the final encryption result.

7. The big data artificial intelligence data encryption method according to claim 6, characterized in that: The storing of the encrypted data refers to allocating the encrypted data to low-level, medium-level, and high-level storage levels according to the security level of the data, performing multiple redundant backups of the encrypted data, and storing the backups in physically separated locations.

8. The big data artificial intelligence data encryption method according to claim 7, characterized in that: The audit log records all access and operation behavior timestamps, user IDs, accessed data, and information about the operations performed. For modification or deletion operations, the audit log records data snapshots before and after the operation.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the big data artificial intelligence data encryption method described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the big data artificial intelligence data encryption method described in any one of claims 1 to 8 are implemented.

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