Customer behavior pattern analysis method, system and device based on homomorphic encryption

Abstract

The invention relates to the technical field of financial services, and discloses a customer behavior pattern analysis method, system and device based on homomorphic encryption, and the technical scheme comprises the following steps: collecting behavior data of a target customer; encrypting the behavior data through a homomorphic encryption algorithm, and sending a private key to an application end; storing the encrypted behavior data in a security database; in response to the analysis call instruction, sending the encrypted behavior data from the secure database to an analysis server through a secure transmission channel; after receiving the encrypted behavior data, the analysis server performs data aggregation and behavior pattern analysis on the encrypted behavior data in an encrypted state to obtain an encrypted analysis result; the encrypted analysis result is sent to the application end and decrypted by the application end through the private key, the analysis result of the customer behavior is obtained, the privacy protection advantage is achieved, the safety performance is enhanced, and the expandability is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of financial services, more particularly, it relates to a customer behavior pattern analysis method, system and device based on homomorphic encryption. BACKGROUND

[0002] The customer behavior pattern analysis method of skilled banks and other financial institutions has some deficiencies and defects in the technical level. Including: Insufficient privacy protection: in traditional customer behavior pattern analysis, banks often need to collect and store a large amount of customer data, including transaction records, account information, etc. These data are vulnerable to data leakage risk during collection, transmission and storage, resulting in invasion of customer privacy.

[0003] Data security problem: traditional methods usually use encryption technology to protect data, but decryption is often needed during data processing, which increases the risk of malicious access to data. Even if the data is encrypted during transmission, it needs to be decrypted once it reaches the processing server, which may cause data to be exposed during processing.

[0004] Data leakage risk: in traditional customer behavior analysis methods, data usually needs to be stored centrally in one or several centralized locations. Centralized data storage makes data more vulnerable to attack, and once the server is breached, a large amount of customer data may be leaked. SUMMARY

[0005] The purpose of the present application is to provide a customer behavior pattern analysis method, system and device based on homomorphic encryption, which has the advantages of privacy protection, enhanced security performance and improved scalability.

[0006] The above technical purpose of the present application is achieved by the following technical scheme: a customer behavior pattern analysis method based on homomorphic encryption, comprising the following steps: Collecting behavior data of target customers; Encrypting the behavior data through a homomorphic encryption algorithm, and sending the private key to the application end; Storing the encrypted behavior data into a secure database; In response to an analysis call instruction, sending the encrypted behavior data from the secure database to the analysis server through a secure transmission channel; After receiving the encrypted behavior data, the analysis server performs data aggregation and behavior pattern analysis on the encrypted behavior data in an encrypted state to obtain encrypted analysis results; Sending the encrypted analysis results to the application end and decrypting them by the application end through the private key to obtain the analysis results of customer behavior.

[0007] As a preferred technical solution of the present application, the behavior data of the target customer is collected through a customer-authorized networked device.

[0008] As a preferred technical solution of the present application, after the behavior data is collected, the behavior data is preprocessed, and the preprocessing includes cleaning processing, missing value filling, outlier detection, and normalization processing of the behavior data.

[0009] As a preferred technical solution of the present application, the homomorphic encryption algorithm includes a full homomorphic encryption algorithm and a partial homomorphic encryption algorithm.

[0010] As a preferred technical solution of the present application, when the data is aggregated, statistical data is calculated through operations supported by the homomorphic encryption algorithm.

[0011] As a preferred technical solution of the present application, the behavior pattern analysis is implemented by using a machine learning model trained in advance through historical encrypted data.

[0012] As a preferred technical solution of the present application, a permission control strategy is configured in advance at the application end, when decrypted data is viewed from the application end, the access person's permission is verified first, and after the verification is passed, the decrypted data is displayed.

[0013] As a preferred technical solution of the present application, all operation logs are recorded at the application end.

[0014] A customer behavior pattern analysis system based on homomorphic encryption, comprising: A client module for collecting behavior data of a target customer; A data encryption module for encrypting the behavior data through a homomorphic encryption algorithm, and sending a private key to an application end; A data storage module for storing the encrypted behavior data in a secure database; A security management module for sending the encrypted behavior data from the secure database to an analysis server through a secure transmission channel in response to an analysis calling instruction; A data processing module for aggregating the encrypted behavior data in an encrypted state after receiving the encrypted behavior data; A data analysis module for analyzing the behavior pattern of the data processed by the data processing module to obtain an encrypted analysis result; A decryption module for decrypting the encrypted analysis result sent to the application end through the private key to obtain an analysis result of the customer behavior.

[0015] A customer behavior pattern analysis device based on homomorphic encryption includes: a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor implements the above-described method when executing the computer program.

[0016] In summary, this invention offers the following advantages: First, it provides privacy protection. Homomorphic encryption allows direct computation on encrypted data without prior decryption, preventing even data processors from accessing plaintext data and thus protecting user privacy. Second, it enhances security. Homomorphic encryption algorithms typically employ modern cryptographic theories, theoretically resisting attacks from quantum computers and providing stronger security guarantees. Third, it improves scalability. With continuous optimization of homomorphic encryption algorithms, their computational efficiency and memory usage are gradually improving, making applications on large-scale datasets possible and enhancing system scalability. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to the accompanying drawings.

[0019] like Figure 1 As shown, this invention provides a customer behavior pattern analysis method based on homomorphic encryption, comprising the following steps: S1. Collect behavioral data of target customers; Target customer behavior data is collected through customer-authorized networked devices, such as ATMs, online banking, and mobile banking.

[0020] Behavioral data includes transaction behavior data, customer base data, and financial-related operational data. For example, transaction behavior data includes a series of data such as transaction amount, transaction time, and transaction location.

[0021] After collecting behavioral data, the data is preprocessed, including cleaning, missing value imputation, outlier detection, and normalization, to ensure data quality for subsequent encryption and analysis.

[0022] S2. Encrypt the behavioral data using a homomorphic encryption algorithm and send the private key to the application. Homomorphic encryption algorithms include fully homomorphic encryption algorithms and partially homomorphic encryption algorithms.

[0023] In practical applications, homomorphic encryption algorithms that support the corresponding operations are selected according to the analysis requirements. These include: semi-homomorphic encryption algorithms that support addition operations, semi-homomorphic encryption algorithms that only support multiplication operations, and fully homomorphic encryption algorithms that support both addition and multiplication operations. For example, if it is necessary to support addition and multiplication operations, a fully homomorphic encryption algorithm can be selected.

[0024] S3. Encrypted behavioral data is stored in a secure database to ensure that sensitive information is not leaked even if the data is accessed without authorization.

[0025] S4. In response to the analysis call command, the encrypted behavioral data is sent from the secure database to the analysis server through a secure transmission channel for encrypted transmission. For example, HTTPS or other encryption protocols can be used to protect the security of data transmission. S5. After receiving the encrypted behavioral data, the analysis server performs data aggregation and behavioral pattern analysis on the encrypted behavioral data in an encrypted state to obtain encrypted analysis results. During data aggregation, statistical data such as total transaction amount and average transaction amount are calculated using operations supported by homomorphic encryption algorithms.

[0026] Behavioral pattern analysis employs a machine learning model pre-trained on historical encrypted data, enabling the identification of customer behavioral patterns. The machine learning model utilizes linear regression, logistic regression, and other methods to ensure all calculations are performed under encrypted conditions.

[0027] S6. Send the encrypted analysis results to the application, and the application will decrypt them using the private key to obtain the analysis results of customer behavior.

[0028] Configure access control policies in advance on the application side. When viewing decrypted data from the application side, first verify the access user's permissions. After the verification is successful, display the decrypted data to ensure that only authorized personnel can access the decrypted data and protect the privacy of customer behavior patterns.

[0029] When viewing the analysis results on the application, all operation logs are recorded, recording all data access and processing activities for tracking and auditing.

[0030] After applying the method of this invention, the decrypted results can be used to gain a deeper understanding of customer behavior habits, which can be used for customer segmentation, risk assessment, product recommendations, etc., providing customers with more personalized services. Risk assessment based on customer behavior patterns can also help identify potential fraudulent activities.

[0031] Through the above solutions, banks and other financial institutions can analyze customer behavior patterns while protecting customer financial privacy. This approach not only improves data security but also helps financial institutions better understand customer needs and provide more personalized services.

[0032] Corresponding to the above method, the present invention also provides a customer behavior pattern analysis system based on homomorphic encryption, comprising: The client module is used to collect behavioral data from target customers; The data encryption module is used to encrypt behavioral data using a homomorphic encryption algorithm and send the private key to the application. The data storage module is used to provide a secure database for storing encrypted behavioral data; The security management module is used to respond to analysis call commands by sending encrypted behavioral data from the secure database to the analysis server through a secure transmission channel; it can also provide key management for homomorphic encryption services, authenticate clients, and ensure the security of data transmission.

[0033] The data processing module is used to aggregate encrypted behavioral data in an encrypted state using homomorphic encryption technology after receiving the encrypted behavioral data. The data analysis module is used to analyze the data processed by the data processing module through methods such as statistical analysis or machine learning to obtain encrypted analysis results. The decryption module is used to decrypt the encrypted analysis results sent to the application using a private key to obtain understandable results and thus the analysis results of customer behavior.

[0034] Specifically, it also includes a homomorphic encryption service module, which interacts with the data encryption module to provide encryption algorithms; interacts with the data processing module to provide data processing capabilities in encrypted state; and interacts with the decryption module to provide decryption algorithms.

[0035] Corresponding to the above methods and systems, the present invention also provides a customer behavior pattern analysis device based on homomorphic encryption, comprising: a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor implements the above methods when executing the computer program.

[0036] The advantages of this invention are as follows: Firstly, it offers privacy protection. Homomorphic encryption allows direct computation on encrypted data without prior decryption, preventing even data processors from accessing plaintext data and thus protecting user privacy. Secondly, it enhances security. Homomorphic encryption algorithms typically employ modern cryptographic theories, theoretically resisting attacks from quantum computers and providing stronger security guarantees. Thirdly, it improves scalability. With continuous optimization of homomorphic encryption algorithms, their computational efficiency and memory usage are gradually improving, making applications on large-scale datasets possible and enhancing system scalability.

[0037] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A homomorphic encryption based client behavior pattern analysis method, characterized by: The method comprises the following steps: Collecting behavior data of a target customer; Encrypting the behavior data by using a homomorphic encryption algorithm and sending a private key to an application end; Storing the encrypted behavior data in a secure database; In response to an analysis calling instruction, sending the encrypted behavior data from the secure database to an analysis server through a secure transmission channel; After receiving the encrypted behavior data, the analysis server performs data aggregation and behavior pattern analysis on the encrypted behavior data in an encrypted state to obtain encrypted analysis results; Sending the encrypted analysis results to the application end and decrypting the encrypted analysis results by using the private key to obtain analysis results of customer behavior.

2. The method of claim 1, wherein the method is characterized by: The behavior data of the target customer is collected by a networked device authorized by the customer.

3. The method of claim 1, wherein the method further comprises: After the behavior data is collected, the behavior data is preprocessed, and the preprocessing includes cleaning processing, missing value filling, abnormal value detection, and normalization processing of the behavior data.

4. The method of claim 1, wherein the method further comprises: The homomorphic encryption algorithm includes a full homomorphic encryption algorithm and a partial homomorphic encryption algorithm.

5. The method of claim 4, wherein the method further comprises: In the data aggregation, statistical data is calculated by using operations supported by the homomorphic encryption algorithm.

6. The method of claim 6, wherein the method further comprises: The behavior pattern analysis is implemented by using a machine learning model trained in advance by using historical encrypted data.

7. The method of claim 1, wherein the method further comprises: A permission control strategy is configured in advance in the application end, and when decrypted data is viewed from the application end, the access permission of a person is verified first, and after the verification is passed, the decrypted data is displayed.

8. The method of claim 1, wherein the method further comprises: All operation logs are recorded in the application end.

9. A homomorphic encryption based client behavior pattern analysis system, characterized by: The method comprises the following steps: A client module is configured to collect behavior data of a target customer; A data encryption module is configured to encrypt the behavior data by using a homomorphic encryption algorithm and send a private key to an application end; A data storage module is configured to store the encrypted behavior data in a secure database; A security management module is configured to send the encrypted behavior data from the secure database to an analysis server through a secure transmission channel in response to an analysis calling instruction; A data processing module is configured to perform data aggregation on the encrypted behavior data in an encrypted state after receiving the encrypted behavior data; A data analysis module is configured to perform behavior pattern analysis on the data processed by the data processing module to obtain encrypted analysis results; A decryption module is configured to decrypt the encrypted analysis results sent to the application end by using the private key to obtain analysis results of customer behavior.

10. A homomorphic encryption based client behavior pattern analysis apparatus, characterized by: The method comprises the following steps: A processor and a memory are provided, the memory stores a computer program executable by the processor, and the processor implements the method in any one of claims 1-8 when executing the computer program.

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