Financial data processing evaluation method, system and equipment based on Internet of Things

Abstract

The invention discloses a financial data processing evaluation method, system and device based on the Internet of Things, and relates to the technical field of financial data processing. The financial data processing evaluation method based on the Internet of Things comprises the following steps of data processing, anonymous evaluation, encryption evaluation and comprehensive judgment. According to the method, the obtained to-be-shared financial data is preprocessed to obtain the financial shared data, then the financial shared data is subjected to data anonymization processing and then is evaluated to obtain the anonymous evaluation index, and then whether data encryption is executed or not is judged based on the anonymous evaluation index and the preset anonymous threshold range; and finally, the data encryption strength is evaluated to obtain an encryption strength effect evaluation value, and whether the financial encrypted data is transmitted or not is judged, so that the effect of improving the security of the financial institution during financial data sharing is achieved, and the problem of relatively low security of the financial institution during financial data sharing in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of financial data processing, and in particular to a financial data processing evaluation method, system and device based on the Internet of Things. Background Art

[0002] With the development of IoT technology, more and more devices are connected to the Internet, forming a huge data generation and transmission network. In the data-driven era, the financial industry has an increasing demand and reliance on data. Financial institutions need to process and analyze data from various channels in real time and efficiently in order to make more accurate decisions, improve risk management capabilities, enhance user experience, and meet compliance requirements.

[0003] The existing financial data processing system collects data in real time through various sensors and smart terminals, transmits the collected data to the central system or edge computing nodes through wireless communication technology, and extracts valuable information and patterns from large amounts of data through machine learning, artificial intelligence, big data analysis and other technologies to achieve a comprehensive integration of the Internet of Things and financial data processing.

[0004] For example, the patent application with publication number: CN 1 1 3 5 3 8 0 3 6A discloses a digital management system, method and device for 5G, AI edge computing and the Internet of Things, including: the digital management system includes an intelligent interactive device and a first camera with a face recognition function, an intelligent station and a central processing end, which are arranged in a regional outlet or an off-line financial service station or a financial cabin; wherein the first camera obtains customer information of customers in the regional outlets, and the intelligent interactive device obtains customer information and customer behavior data of customers in the regional outlets through interaction; the intelligent station is connected to the intelligent interactive device and the first camera respectively through the Internet of Things, and performs AI edge computing based on customer information and customer behavior data to provide business recommendations and in-depth management services for customers; the central processing end is connected to the intelligent station through 5G mobile communication to obtain processed customer information and customer behavior data, and manage, store and display them.

[0005] For example, the invention patent announcement with announcement number: CN 1 1 5 7 6 0 1 1 9B discloses a financial payment supervision system and method based on data processing and feature recognition, which includes: collecting and processing data in different dimensions from the merchant itself and the business operation to provide reliable data support for the subsequent payment risk analysis and evaluation of different targets; integrating and calculating the data of different dimensions at the time of target payment with the corresponding label weights and the type weights corresponding to the merchant, obtaining the corresponding payment risk value through data integration, and evaluating and classifying the target's payment risk based on the payment risk value, so as to dynamically manage and prompt the target's payment in the future.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] When financial institutions share and collaborate with each other or with other industry organizations, it is crucial to ensure the security of shared data. Financial institutions tend to adopt more advanced privacy protection technologies, blockchain technologies, and enhanced artificial intelligence algorithms to achieve more extensive and efficient data sharing while ensuring data security and compliance.

[0008] In the existing technology, because financial data usually involves highly sensitive personal information and business secrets, security in the data sharing process has become the primary concern. Although financial data sharing brings many advantages, financial institutions still face a series of challenges and risks when implementing financial data sharing, such as data interoperability issues between different systems and technical implementation difficulties of encryption and access control, which increase the complexity of data sharing and the problem of low security when financial institutions share financial data. Summary of the invention

[0009] The embodiments of the present application solve the problem of low security when financial institutions share financial data in the prior art by providing a financial data processing and evaluation method, system and device based on the Internet of Things, thereby improving the security of financial institutions when sharing financial data.

[0010] The embodiment of the present application provides a financial data processing and evaluation method, system and device based on the Internet of Things, comprising the following steps: S1, pre-processing the acquired financial data to be shared to obtain financial shared data, wherein the financial shared data is used to describe the data content shared by the financial institution; S2, performing data anonymization processing on the financial shared data to obtain financial anonymous data, performing data anonymization evaluation based on the financial shared data and the financial anonymous data to obtain an anonymous evaluation index, judging whether to perform data encryption of the financial anonymous data based on the anonymous evaluation index and a preset anonymous threshold range, wherein the anonymous evaluation index is used to comprehensively quantify the degree of compliance with data anonymization; S3, after performing data encryption of the financial anonymous data, obtaining financial encrypted data after the financial anonymous data is encrypted, evaluating the strength of the data encryption to obtain an encryption strength effect evaluation value, wherein the encryption strength effect evaluation value is used to comprehensively quantify the degree of compliance with the encryption strength when the financial anonymous data is encrypted; S4, performing a comprehensive evaluation based on the encryption strength effect evaluation value and a preset encryption strength effect threshold range to obtain a comprehensive evaluation result, and judging whether to transmit the financial encrypted data based on the comprehensive evaluation result, and providing feedback if the financial encrypted data is not transmitted, wherein the comprehensive evaluation result is used to judge whether to transmit the financial encrypted data.

[0011] Furthermore, the specific method for acquiring the financial shared data is as follows: obtaining first financial shared data by performing data cleaning on the shared financial data; obtaining second financial shared data by performing data transformation on the first financial shared data; and obtaining financial shared data by performing data integration and data sampling on the second financial shared data.

[0012] Furthermore, the specific steps of performing data anonymization processing on the financial shared data are as follows: A1, data marking the financial shared data to obtain financial shared sensitive data and financial shared non-sensitive data, and replacing the original identifier of the financial shared sensitive data with a random identifier; A2, performing sensitive data preprocessing on the financial shared sensitive data to obtain anonymously processed financial shared sensitive data, and the sensitive data preprocessing includes data forgery, data generalization, and data segmentation processing; A3, performing data aggregation and data perturbation processing on the anonymously processed financial shared sensitive data and financial shared non-sensitive data to obtain financial anonymous data.

[0013] Furthermore, the specific steps of performing data anonymization evaluation based on financial shared data and financial anonymous data to obtain an anonymous evaluation index are as follows: dividing and numbering the financial shared data to obtain financial shared data blocks, and obtaining anonymous time data for anonymization of the financial shared data blocks, wherein the anonymous time data includes an anonymous start time and an anonymous end time; obtaining a corresponding original data set according to the type of financial shared data, and obtaining an anonymous data retained value set according to the data anonymization processing of the financial shared data within a preset time interval, wherein the original data set includes a financial shared data value set, an original sensitive data value set, and an original non-sensitive data value set, and the anonymous data retained value set. The value set includes an anonymous processing sensitive data value set and an anonymous processing non-sensitive data value set; a sample set of financial shared data to be tested is obtained by performing simple random sampling on financial shared data, and a re-entry attack is performed on the sample of financial shared data to be tested to obtain a re-identified financial shared data sample set; the anonymous time data of the anonymized financial shared data block, the original data set, the anonymous data retained value set, the sample set of financial shared data to be tested, and the re-identified financial shared data sample set are combined with the anonymization processing time reference deviation and evaluation weights obtained from a preset database to obtain an anonymous evaluation index, and the evaluation weights include data retention weights, data processing weights, and re-identification weights.

[0014] Furthermore, the anonymous evaluation index is calculated using the following formula:

[0015]

[0016] Where N represents the anonymous evaluation index, q f Represents the original non-sensitive data value set, qm Represents the original sensitive data value set, Indicates a set of anonymous non-sensitive data values. represents the set of anonymously processed sensitive data values, i represents the number of the financial shared data block, i = 1, 2, ..., K, K represents the total number of financial shared data blocks, represents the anonymity end time of the ith financial shared data block, represents the anonymity start time of the ith financial shared data block, Δt represents the anonymization processing time reference deviation, and q g represents the sample set of financial shared data to be tested, q c Represents the re-identification financial shared data sample set, γ represents the data retention weight, β represents the data processing weight, and δ represents the re-identification weight.

[0017] Furthermore, a specific method for obtaining the encryption strength effect evaluation value is as follows: financial anonymous data is divided into blocks and numbered to obtain financial anonymous data blocks, the number of data bits of the financial anonymous data blocks and encryption time data for data encryption processing are obtained, and the encryption time data include encryption start time and encryption end time; the key length for data encryption of the financial anonymous data is obtained, and brute force cracking is performed on the financial encrypted data to obtain the attempted decryption time; the encryption strength effect evaluation value is obtained through the encryption time data, key length and attempted decryption time of the financial anonymous data and in combination with the data encryption processing time reference deviation, attempted decryption time reference deviation and evaluation weights obtained from a preset database, and the evaluation weights include key length weight, anti-attack performance weight and encryption efficiency weight.

[0018] Furthermore, the encryption strength effect evaluation value is calculated using the following formula:

[0019]

[0020] Where J represents the encryption strength effect evaluation value, ω represents the key length weight, L represents the key length, σ represents the anti-attack performance weight, c represents the decryption attempt time, Δc represents the decryption attempt time reference deviation, ε represents the encryption efficiency weight, y represents the number of the financial anonymous data block, y=1,2,...,Y, Y represents the total number of financial anonymous data blocks, Indicates the encryption end time of the yth financial anonymous data block, represents the encryption start time of the yth financial anonymous data block, Δs represents the reference deviation of the data encryption processing time, Q y Represents the number of data bits of the y-th financial anonymous data block.

[0021] Furthermore, the specific steps for determining whether to transmit financial encrypted data based on the comprehensive evaluation results are as follows: obtain an encryption strength effect evaluation value and a preset encryption strength effect threshold range, compare the encryption strength effect evaluation value with the preset encryption strength effect threshold range, and if the encryption strength effect evaluation value is within the preset encryption strength effect threshold range, transmit the financial encrypted data; otherwise, automatically update the key length, and at the same time trigger the early warning mechanism to feed back the encryption strength effect evaluation value to remind the preset personnel to change the encryption algorithm and perform data encryption strength evaluation again to obtain the encryption strength effect evaluation value, until the encryption strength effect evaluation value is within the preset encryption strength effect threshold range.

[0022] The embodiment of the present application provides a financial data processing and evaluation system based on the Internet of Things, including: a data processing module, an anonymous evaluation module, an encryption evaluation module, and a comprehensive judgment module; wherein the data processing module is used to pre-process the acquired financial data to be shared to obtain financial shared data, and the financial shared data is used to describe the data content that the financial institution needs to share; the anonymous evaluation module is used to perform data anonymization processing on the financial shared data to obtain financial anonymous data, perform data anonymization evaluation based on the financial shared data and the financial anonymous data to obtain an anonymous evaluation index, and judge whether to perform data encryption of the financial anonymous data based on the anonymous evaluation index and a preset anonymous threshold range, and the anonymous evaluation index is used to comprehensively quantify the data anonymity. The encryption evaluation module is used to obtain the financial encrypted data after the financial anonymous data is encrypted, evaluate the strength of the data encryption to obtain the encryption strength effect evaluation value, and the encryption strength effect evaluation value is used to comprehensively quantify the compliance degree of the encryption strength when the financial anonymous data is encrypted; the comprehensive judgment module is used to perform a comprehensive evaluation according to the encryption strength effect evaluation value and a preset encryption strength effect threshold range to obtain a comprehensive evaluation result, and judge whether to transmit the financial encrypted data based on the comprehensive evaluation result, and if the financial encrypted data is not transmitted, feedback is provided, and the comprehensive evaluation result is used to judge whether to transmit the financial encrypted data.

[0023] An embodiment of the present application provides an electronic device, which includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to implement a financial data processing and evaluation method based on the Internet of Things.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. Anonymity evaluation index is obtained by evaluating the financial shared data after anonymization, and then data encryption is performed based on the anonymity evaluation index and a preset anonymity threshold range. Finally, the strength of data encryption is evaluated to obtain an encryption strength effect evaluation value and determine whether to transmit financial encrypted data. This achieves anonymization evaluation and accurate evaluation of encryption strength when financial institutions share financial data, thereby improving the security of financial institutions when sharing financial data, and effectively solving the problem of low security of financial institutions when sharing financial data in the prior art.

[0026] 2. Financial anonymous data is obtained by anonymizing the financial shared data, and then an anonymous evaluation index is obtained based on the financial shared data and the financial anonymous data. Finally, data encryption of the financial anonymous data is performed based on the anonymous evaluation index and the preset anonymous threshold range, thereby realizing the quantification of the degree of data anonymization compliance and achieving a more accurate evaluation of data anonymization.

[0027] 3. The encryption strength effect evaluation value is obtained by evaluating the strength of data encryption, and then a comprehensive evaluation is performed based on the encryption strength effect evaluation value and the preset encryption strength effect threshold range to obtain a comprehensive evaluation result. Finally, based on the comprehensive evaluation result, it is determined whether to transmit financial encrypted data, thereby realizing the digitization of the degree of encryption strength compliance during data encryption, and further realizing a more accurate evaluation of data encryption strength. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flowchart of a financial data processing and evaluation method based on the Internet of Things provided in an embodiment of the present application;

[0029] Figure 2 A schematic diagram of the structure of a financial data processing and evaluation system based on the Internet of Things provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiments of the present application solve the problem of low security of financial institutions when sharing financial data in the prior art by providing a financial data processing and evaluation method, system and equipment based on the Internet of Things. The financial shared data is obtained by preprocessing the acquired financial data to be shared, and the financial shared data is evaluated after data anonymization to obtain an anonymous evaluation index. Based on the anonymous evaluation index and a preset anonymous threshold range, it is determined whether to perform data encryption. Then, the strength of the data encryption is evaluated to obtain an encryption strength effect evaluation value and it is determined whether to transmit the financial encrypted data, thereby improving the security of financial institutions when sharing financial data.

[0031] The technical solution in the embodiment of the present application is to solve the problem of low security when the above-mentioned financial institutions share financial data. The overall idea is as follows:

[0032] By anonymizing financial sharing data to obtain financial anonymous data and performing evaluation and judgment to execute data encryption of the financial anonymous data, and by evaluating and judging the strength of data encryption to transmit financial encrypted data, the effect of improving the security of financial institutions when sharing financial data is achieved.

[0033] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0034] like Figure 1 As shown, it is a flow chart of a financial data processing and evaluation method based on the Internet of Things provided by an embodiment of the present application, and the method includes the following steps: S1, pre-processing the acquired financial data to be shared to obtain financial shared data, the financial data to be shared represents the data that the financial institution needs to transmit within a preset time period, and the financial shared data is used to describe the data content shared by the financial institution; S2, performing data anonymization processing on the financial shared data to obtain financial anonymous data, performing data anonymization evaluation based on the financial shared data and the financial anonymous data to obtain an anonymous evaluation index, judging whether to perform data encryption of the financial anonymous data based on the anonymous evaluation index and a preset anonymous threshold range, and the anonymous evaluation index is used to comprehensively quantify the degree of compliance of the data anonymization; S3, after performing data encryption of the financial anonymous data, obtaining financial encrypted data after the financial anonymous data is encrypted, evaluating the strength of the data encryption to obtain an encryption strength effect evaluation value, and the encryption strength effect evaluation value is used to comprehensively quantify the degree of compliance of the encryption strength when the financial anonymous data is encrypted; S 4. Perform a comprehensive evaluation based on the encryption strength effect evaluation value and the preset encryption strength effect threshold range to obtain a comprehensive evaluation result, and determine whether to transmit financial encrypted data based on the comprehensive evaluation result. If the financial encrypted data is not transmitted, feedback is provided. The comprehensive evaluation result is used to determine whether to transmit financial encrypted data.

[0035] In this embodiment, the financial data to be shared represents the data to be transmitted by the financial institution within a preset time period. Since the financial data contains a large amount of sensitive information, the financial institution has low security when sharing the financial data and is prone to information leakage. Among them, whether to perform data encryption of the financial anonymous data is determined based on the anonymity evaluation index and the preset anonymity threshold range. If it is not performed, that is, the anonymity evaluation index exceeds the preset anonymity threshold range, feedback is given to remind the preset personnel to readjust the anonymization strategy, such as increasing data disturbance; thereby reducing the risk of leakage of financial data when sharing.

[0036] It should be understood that the specific method for obtaining financial shared data is as follows: first financial shared data is obtained by performing data cleaning on shared financial data; second financial shared data is obtained by performing data transformation on the first financial shared data; and financial shared data is obtained by performing data integration and data sampling on the second financial shared data.

[0037] Among them, data cleaning refers to using visualization tools (such as heat maps) to identify missing values ​​in shared financial data and filling them through interpolation, using statistical methods (such as box plots) to identify outliers and delete them, and then using moving average to smooth the data to reduce the impact of noise; among them, data transformation refers to standardizing and normalizing the First Financial shared data through ZS core standardization; among them, data integration refers to unifying field names and data types through data mapping and conversion, checking data duplication through primary keys and then deleting duplicate records, and using SQL (Structured Query Language) connections to merge data; data sampling refers to obtaining sample data through simple random sampling; that is, preprocessing includes data cleaning, data transformation, data integration and data sampling, which improves the quality of financial shared data.

[0038] Furthermore, the specific steps of data anonymization processing for financial shared data are as follows: A1, data marking of financial shared data to obtain financial shared sensitive data and financial shared non-sensitive data, and replacing the original identifier of the financial shared sensitive data with a random identifier; A2, sensitive data preprocessing of the financial shared sensitive data to obtain anonymous financial shared sensitive data, and the sensitive data preprocessing includes data forgery, data generalization and data segmentation processing; A3, data aggregation and data perturbation processing of the anonymous financial shared sensitive data and financial shared non-sensitive data to obtain financial anonymous data.

[0039] In this embodiment, data labeling refers to the use of data classification algorithms or regular expressions to automatically identify sensitive fields; the labeling results are used to distinguish between financial shared sensitive data and financial shared non-sensitive data; data forgery refers to the use of randomly generated or rule-generated pseudo data, such as replacing a credit card number with a random number that conforms to the credit card number format; data segmentation processing refers to dividing financial shared sensitive data into different parts, such as storing them separately according to the attributes of the data (such as an ID card); data aggregation refers to aggregating data by time period or geographic area, such as aggregating data on financial activities by month or region; data perturbation processing refers to the use of data perturbation algorithms (such as differential privacy) to introduce small random changes or noise into the data; anonymization of financial shared data is achieved, thereby reducing the potential risks caused by data leakage and ensuring data security.

[0040] Furthermore, the specific steps of performing data anonymization evaluation based on the financial shared data and the financial anonymous data to obtain the anonymous evaluation index are as follows: the financial shared data is divided into blocks and numbered to obtain financial shared data blocks, and anonymous time data of the financial shared data blocks for anonymization processing is obtained, and the anonymous time data includes the anonymous start time and the anonymous end time; according to the type of financial shared data, the corresponding original data set is obtained, and according to the data anonymization processing of the financial shared data within a preset time interval, the anonymous data retained value set is obtained, and the original data set includes the financial shared data value set, the original sensitive data value set and the original non-sensitive data value set, and the anonymous data retained value set It includes an anonymous processing sensitive data value set and an anonymous processing non-sensitive data value set; obtaining a sample set of financial shared data to be tested by performing simple random sampling on financial shared data, and performing a re-entry attack on the financial shared data samples to be tested to obtain a re-identified financial shared data sample set; the anonymous time data of the anonymized financial shared data block, the original data set, the anonymous data retained value set, the sample set of financial shared data to be tested, and the re-identified financial shared data sample set are combined with the anonymization processing time reference deviation and evaluation weight obtained from a preset database to obtain an anonymous evaluation index, and the evaluation weight includes data retention weight, data processing weight and re-identification weight.

[0041] It is important to further understand that the anonymous evaluation index is calculated using the following formula:

[0042]

[0043] Where N represents the anonymous evaluation index, q f Represents the original non-sensitive data value set, q m Represents the original sensitive data value set, Indicates a set of anonymous non-sensitive data values. represents the set of anonymously processed sensitive data values, i represents the number of the financial shared data block, i = 1, 2, ..., K, K represents the total number of financial shared data blocks, represents the anonymity end time of the ith financial shared data block, represents the anonymity start time of the ith financial shared data block, Δt represents the anonymization processing time reference deviation, and q g represents the sample set of financial shared data to be tested, q c represents the re-identification financial shared data sample set, γ represents the data retention weight, β represents the data processing weight, δ represents the re-identification weight, express and (q f ∪q m ), l(q f ∪qm )| means q f With q m The number of elements in the union of .

[0044] In this embodiment, the original sensitive data value set is used to describe the value set of financial shared sensitive data, the original non-sensitive data value set is used to describe the value set of financial shared non-sensitive data, the anonymous processing sensitive data value set is used to describe the value set of financial shared sensitive data after data anonymization processing, and the anonymous processing non-sensitive data value set is used to describe the value set of financial shared non-sensitive data after data anonymization processing; wherein, the original sensitive data values, the original non-sensitive data values, the anonymous processing sensitive data values ​​and the anonymous processing non-sensitive data values ​​do not have the same elements; wherein, the result of summing and averaging the collected anonymized historical time data represents the anonymization processing time reference deviation; wherein, simple random sampling can generate random numbers through the RANDOM() function; wherein, the reentrancy attack is an attacker taking advantage of the vulnerability that the smart contract does not correctly handle recursive calls when calling external contract functions. Specifically, the attack can be launched by deploying the Vulnerable contract and the Attacker contract in the Ethereum test network or the local environment, and then launching the attack through the attack function of the Attacker contract.

[0045] Among them, the data retention weight is used to describe the influence of the retention of data before and after the anonymization process on the anonymity evaluation index. Specifically, the data retention weight is the weight corresponding to the retention of the data before and after the anonymization process preset in the preset database (that is, the ratio of the amount of retained data to the total amount of data), which represents the numerical value of the influence of the retention of the data before and after the anonymization process on the anonymity evaluation index. When used, the weight corresponding to the retention of the preset data before and after the anonymization process can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the retention of the data before and after the anonymization process and the weight corresponding to the retention of the data before and after the anonymization process preset in the preset database form a mapping set, and the retention of the real-time data before and after the anonymization process is input into the mapping set to obtain the corresponding weight. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0046] Among them, the data processing weight is used to describe the influence of the time required for anonymization processing on the anonymity evaluation index. Specifically, the data processing weight is the weight corresponding to the time required for anonymization processing preset in the preset database, which represents the numerical value of the influence of the time required for anonymization processing on the anonymity evaluation index. When used, the weight corresponding to the preset time required for anonymization processing can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the time required for anonymization processing and the weight corresponding to the time required for anonymization processing preset in the preset database form a mapping set, and the time required for real-time anonymization processing is input into the mapping set to obtain the corresponding weight. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0047] Among them, the re-identification weight is used to describe the degree of influence of the risk of re-identification of data after anonymization processing on the anonymity evaluation index. Specifically, the re-identification weight is the weight corresponding to the risk of re-identification of data after anonymization processing preset in the preset database (that is, the ratio of the sample size of re-identification to the sample size to be tested), which represents the numerical value of the degree of influence of the risk of re-identification of data after anonymization processing on the anonymity evaluation index. When used, the weight corresponding to the preset risk of re-identification of data after anonymization processing can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the risk of re-identification of data after anonymization processing and the weight corresponding to the risk of re-identification of data after anonymization processing preset in the preset database form a mapping set, and the risk of re-identification of data after real-time anonymization processing is input into the mapping set to obtain the corresponding weight, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1]; a more accurate assessment of the degree of data anonymization compliance is achieved.

[0048] Through the correlation and mutual influence between the anonymity evaluation index and the sample set of financial shared data to be tested and the sample set of re-identified financial shared data, the anonymity evaluation index is obtained through comprehensive analysis, which can accurately quantify the degree of compliance with data anonymization; among them, when the sample set of financial shared data to be tested is a certain value, as the sample set of re-identified financial shared data increases, the anonymity evaluation index gradually decreases; and when the sample set of re-identified financial shared data is a certain value, as the sample set of financial shared data to be tested increases, the anonymity evaluation index also increases, thereby realizing the numerical evaluation of the degree of compliance with data anonymization.

[0049] Furthermore, a specific method for obtaining an encryption strength effect evaluation value is as follows: financial anonymous data is divided into blocks and numbered to obtain financial anonymous data blocks, the number of data bits of the financial anonymous data blocks and encryption time data for data encryption processing are obtained, and the encryption time data includes the encryption start time and the encryption end time; the key length for data encryption of the financial anonymous data is obtained, and brute force cracking is performed on the financial encrypted data to obtain the attempted decryption time; the encryption strength effect evaluation value is obtained through the encryption time data, key length and attempted decryption time of the financial anonymous data and in combination with the data encryption processing time reference deviation, attempted decryption time reference deviation and evaluation weights obtained from a preset database, and the evaluation weights include the key length weight, the anti-attack performance weight and the encryption efficiency weight.

[0050] It is important to understand that the encryption strength effect evaluation value is calculated using the following formula:

[0051]

[0052] Where J represents the encryption strength effect evaluation value, ω represents the key length weight, L represents the key length, σ represents the anti-attack performance weight, c represents the decryption attempt time, Δc represents the decryption attempt time reference deviation, ε represents the encryption efficiency weight, y represents the number of the financial anonymous data block, y=1,2,...,Y, Y represents the total number of financial anonymous data blocks, Indicates the encryption end time of the yth financial anonymous data block, represents the encryption start time of the yth financial anonymous data block, Δs represents the reference deviation of the data encryption processing time, Q y Represents the number of data bits of the y-th financial anonymous data block.

[0053] In this embodiment, brute force cracking determines the number of key combinations that need to be tried through the key length of the encryption algorithm, and attempts to decrypt the encrypted data and obtains the attempted decryption time through hash verification; wherein, the data encryption processing time reference deviation is represented by the result of summing and averaging the collected historical time data of data encryption; wherein, the attempted decryption time reference deviation is represented by the result of summing and averaging the collected historical time data of attempted decryption.

[0054] Among them, the key length weight is used to describe the influence of key strength on the encryption strength effect evaluation value. Specifically, the key length weight is the weight corresponding to the key strength (i.e., the length of the key) preset in the preset database, which represents the numerical value of the influence of the key strength on the encryption strength effect evaluation value. When used, the weight corresponding to the preset key strength can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the key strength and the weight corresponding to the key strength value preset in the preset database form a mapping set, and the real-time key strength is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0055] Among them, the anti-attack performance is used to describe the influence of the anti-attack performance on the encryption strength effect evaluation value. Specifically, the anti-attack performance weight is the weight corresponding to the anti-attack performance preset in the preset database (i.e., the decryption attempt time corresponding to brute force cracking), which represents the numerical value of the influence of the anti-attack performance on the encryption strength effect evaluation value. When used, the weight corresponding to the preset anti-attack performance can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the anti-attack performance and the weight of the anti-attack performance value preset in the preset database form a mapping set, and the real-time anti-attack performance is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0056] Among them, the encryption efficiency weight is used to describe the influence of encryption efficiency on the encryption strength effect evaluation value. Specifically, the encryption efficiency weight is the weight corresponding to the encryption efficiency preset in the preset database (that is, the ratio of encryption time to the number of encrypted data bits), which represents the numerical value of the influence of encryption efficiency on the encryption strength effect evaluation value. When used, the weight corresponding to the preset encryption efficiency can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the encryption efficiency and the weight corresponding to the numerical value of the encryption efficiency preset in the preset database form a mapping set, and the real-time encryption efficiency is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1]; a more accurate evaluation of the data encryption strength effect is achieved.

[0057] Specific assumptions: the key length weight is 0.5, the anti-attack performance weight is 0.3, the encryption efficiency weight is 0.2, the decryption attempt time reference deviation is 5 seconds, the data encryption processing time reference deviation is 2 seconds, the decryption attempt time is 10 seconds, the total number of financial anonymous data blocks is 1, and the number of data bits of the financial anonymous data blocks is 1024. The encryption strength effect evaluation value can be calculated by the above method. The change statistics of the encryption strength effect evaluation value are shown in Table 1:

[0058] Table 1 Statistics of changes in encryption strength effect evaluation values

[0059]

[0060] The encryption strength effect evaluation value is used to evaluate the degree of compliance of encryption strength when financial anonymous data is encrypted; among them, the encryption strength effect evaluation value includes parameters in multiple aspects, and each parameter is related, not independent, and not obtained by simple combination and addition. For example, even if the difference between the encryption end time and the encryption start time is large, it does not necessarily mean that the encryption strength effect evaluation value is large. At this time, the influence of the key length must also be considered; as shown in Table 1, as the key length increases, the encryption strength effect evaluation value increases accordingly. When the key length is the same, the greater the encryption time (the difference between the encryption end time and the encryption start time), the greater the corresponding encryption strength effect evaluation value, such as the first group of data and the second group of data; the numerical evaluation of the data encryption strength effect is realized, and then a more accurate evaluation of the data encryption strength effect is realized, ensuring the security of data transmission.

[0061] Furthermore, the specific steps for determining whether to transmit financial encrypted data based on the comprehensive evaluation results are as follows: obtain an encryption strength effect evaluation value and a preset encryption strength effect threshold range, compare the encryption strength effect evaluation value with the preset encryption strength effect threshold range, and if the encryption strength effect evaluation value is within the preset encryption strength effect threshold range, transmit the financial encrypted data; otherwise, automatically update the key length, and at the same time trigger the early warning mechanism to feed back the encryption strength effect evaluation value to remind the preset personnel to change the encryption algorithm and perform data encryption strength evaluation again to obtain the encryption strength effect evaluation value, until the encryption strength effect evaluation value is within the preset encryption strength effect threshold range.

[0062] In this embodiment, the preset encryption strength effect threshold range is set by professionals according to standards in the field. For example, the preset encryption strength effect threshold range can be determined by setting the key length. Generally, the key length should be set to 10.0 to 5.1.2. The transmission of financial encrypted data can be securely transmitted through the transport layer security protocol. Once the early warning mechanism is triggered, feedback is provided to the preset personnel via email or text message, thereby improving the reliability of financial encrypted data transmission.

[0063] like Figure 2As shown, it is a structural schematic diagram of the financial data processing and evaluation system based on the Internet of Things provided in an embodiment of the present application. The financial data processing and evaluation system based on the Internet of Things provided in an embodiment of the present application includes: a data processing module, an anonymous evaluation module, an encrypted evaluation module, and a comprehensive judgment module; wherein the data processing module is used to pre-process the acquired financial data to be shared to obtain financial shared data, the financial data to be shared represents the data to be transmitted by the financial institution within a preset time period, and the financial shared data is used to describe the data content that the financial institution needs to share; the anonymous evaluation module is used to perform data anonymization processing on the financial shared data to obtain financial anonymous data, perform data anonymization evaluation based on the financial shared data and the financial anonymous data to obtain an anonymous evaluation index, and judge based on the anonymous evaluation index and the preset anonymous threshold range. Whether to perform data encryption of financial anonymous data, the anonymity evaluation index is used to comprehensively quantify the degree of compliance with data anonymization; the encryption evaluation module is used to obtain the financial encrypted data after the financial anonymous data is encrypted, and evaluate the strength of the data encryption to obtain the encryption strength effect evaluation value, and the encryption strength effect evaluation value is used to comprehensively quantify the degree of compliance of the encryption strength when the financial anonymous data is encrypted; the comprehensive judgment module is used to perform a comprehensive evaluation based on the encryption strength effect evaluation value and the preset encryption strength effect threshold range to obtain a comprehensive evaluation result, and judge whether to transmit the financial encrypted data based on the comprehensive evaluation result. If the financial encrypted data is not transmitted, feedback is provided, and the comprehensive evaluation result is used to judge whether to transmit the financial encrypted data.

[0064] In this embodiment, the data processing module is used to perform preliminary processing and preparation on the original data obtained from the financial institution; wherein, the anonymous evaluation module anonymizes the processed data and evaluates the anonymization effect; wherein, the encryption evaluation module encrypts the anonymized data and evaluates the encryption strength; wherein, the comprehensive judgment module integrates the results of the anonymous evaluation and the encryption evaluation to make a decision on whether to transmit the data; thereby achieving the security of financial data processing and transmission.

[0065] An embodiment of the present application also provides an electronic device, comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, a financial data processing and evaluation method based on the Internet of Things is triggered.

[0066] In this embodiment, the functions that the program needs to implement include obtaining financial shared data and performing anonymization processing, evaluating data anonymization to obtain an anonymity evaluation index and determining whether to encrypt the data, evaluating the strength of data encryption and determining whether to transmit financial encrypted data based on the comprehensive evaluation results; and achieving accurate evaluation of the degree of anonymity and encryption compliance of financial data.

[0067] To summarize, the embodiment of the present application obtains an anonymous evaluation index after performing data anonymization processing on the financial shared data, then performs data encryption based on the anonymous evaluation index and a preset anonymous threshold range, and finally evaluates the strength of the data encryption to obtain an encryption strength effect evaluation value and determines whether to transmit the financial encrypted data, thereby achieving anonymization evaluation and accurate evaluation of encryption strength when financial institutions share financial data, thereby achieving improved security when financial institutions share financial data, and effectively solving the problem of low security of financial institutions when sharing financial data in the prior art.

[0068] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0070] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0072] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0073] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A financial data processing and evaluation method based on the Internet of Things, characterized in that: The following steps are involved: S1, preprocessing the acquired financial data to be shared to obtain financial shared data, where the financial shared data is used to describe the data content shared by the financial institution; S2, performing data anonymization processing on the financial shared data to obtain financial anonymous data, performing data anonymization evaluation based on the financial shared data and the financial anonymous data to obtain an anonymity evaluation index, and judging whether to perform data encryption of the financial anonymous data based on the anonymity evaluation index and a preset anonymity threshold range, wherein the anonymity evaluation index is used to comprehensively quantify the degree of compliance with data anonymization; S3, after performing data encryption of the financial anonymous data, obtaining financial encrypted data after the financial anonymous data is encrypted, evaluating the strength of the data encryption to obtain an encryption strength effect evaluation value, wherein the encryption strength effect evaluation value is used to comprehensively quantify the degree of compliance of the encryption strength when the financial anonymous data is encrypted; S 4, performing a comprehensive evaluation according to the encryption strength effect evaluation value and the preset encryption strength effect threshold range to obtain a comprehensive evaluation result, and judging whether to transmit the financial encrypted data based on the comprehensive evaluation result, and providing feedback if the financial encrypted data is not to be transmitted, wherein the comprehensive evaluation result is used to judge whether to transmit the financial encrypted data.

2. The method for evaluating financial data processing based on the Internet of Things as claimed in claim 1, characterized in that: The specific method for obtaining the financial shared data is as follows: The first financial shared data is obtained by performing data cleaning on the shared financial data; Performing data transformation on the first financial shared data to obtain second financial shared data; The second financial shared data is subjected to data integration and data sampling to obtain financial shared data.

3. The financial data processing and evaluation method based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of anonymizing the financial shared data are as follows: A 1. Mark the financial shared data to obtain financial shared sensitive data and financial shared non-sensitive data, and replace the original identifier of the financial shared sensitive data with a random identifier; A 2, performing sensitive data preprocessing on the financial shared sensitive data to obtain anonymous financial shared sensitive data, wherein the sensitive data preprocessing includes data forgery, data generalization, and data segmentation processing; A 3, perform data aggregation and data perturbation processing on the anonymously processed financial shared sensitive data and financial shared non-sensitive data to obtain financial anonymous data.

4. The method for financial data processing and evaluation based on the Internet of Things as claimed in claim 3, characterized in that: The specific steps of performing data anonymization evaluation based on financial shared data and financial anonymous data to obtain an anonymous evaluation index are as follows: The financial shared data is divided into blocks and numbered to obtain financial shared data blocks, and anonymous time data of anonymization of the financial shared data blocks is obtained, wherein the anonymous time data includes an anonymous start time and an anonymous end time; Acquire a corresponding original data set according to the type of financial shared data, and obtain an anonymous data retention value set according to the data anonymization processing of the financial shared data within a preset time interval, wherein the original data set includes a financial shared data value set, an original sensitive data value set, and an original non-sensitive data value set, and the anonymous data retention value set includes an anonymized sensitive data value set and an anonymized non-sensitive data value set; A sample set of financial shared data to be tested is obtained by performing simple random sampling on the financial shared data, and a re-entry attack is performed on the sample of financial shared data to be tested to obtain a sample set of re-identified financial shared data; The anonymous time data of the anonymized financial shared data block, the original data set, the anonymous data retained value set, the financial shared data sample set to be tested, and the re-identified financial shared data sample set are combined with the anonymization processing time reference deviation and evaluation weight obtained from the preset database to obtain an anonymous evaluation index, wherein the evaluation weight includes data retention weight, data processing weight and re-identification weight.

5. The method for evaluating financial data processing based on the Internet of Things as claimed in claim 4, characterized in that: The anonymous evaluation index is calculated using the following formula: Where N represents the anonymous evaluation index, q f Represents the original non-sensitive data value set, q m Represents the original sensitive data value set, Indicates a set of anonymous non-sensitive data values. represents the set of anonymously processed sensitive data values, i represents the number of the financial shared data block, i = 1, 2, ..., K, K represents the total number of financial shared data blocks, represents the anonymity end time of the ith financial shared data block, represents the anonymity start time of the ith financial shared data block, Δt represents the anonymization processing time reference deviation, and q g represents the sample set of financial shared data to be tested, q c Represents the re-identification financial shared data sample set, γ represents the data retention weight, β represents the data processing weight, and δ represents the re-identification weight.

6. The method for financial data processing and evaluation based on the Internet of Things as claimed in claim 1, characterized in that: The specific method for obtaining the encryption strength effect evaluation value is as follows: Divide and number the financial anonymous data to obtain financial anonymous data blocks, obtain the number of data bits of the financial anonymous data blocks and encryption time data for data encryption processing, wherein the encryption time data includes the encryption start time and the encryption end time; Obtain the key length for data encryption of anonymous financial data, and perform brute force cracking on the encrypted financial data to obtain the decryption attempt time; The encryption strength effect evaluation value is obtained by using the encryption time data, key length and attempted decryption time of the financial anonymous data and combining the data encryption processing time reference deviation, attempted decryption time reference deviation and evaluation weights obtained from a preset database. The evaluation weights include key length weight, anti-attack performance weight and encryption efficiency weight.

7. The method for financial data processing and evaluation based on the Internet of Things as claimed in claim 6, characterized in that: The encryption strength effect evaluation value is calculated using the following formula: Where J represents the encryption strength effect evaluation value, ω represents the key length weight, L represents the key length, σ represents the anti-attack performance weight, c represents the decryption attempt time, Δc represents the decryption attempt time reference deviation, ε represents the encryption efficiency weight, y represents the number of the financial anonymous data block, y=1,2,...,Y, Y represents the total number of financial anonymous data blocks, Indicates the encryption end time of the yth financial anonymous data block, represents the encryption start time of the yth financial anonymous data block, Δs represents the reference deviation of the data encryption processing time, Q y Represents the number of data bits of the y-th financial anonymous data block.

8. The method for financial data processing and evaluation based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of determining whether to transmit financial encrypted data based on the comprehensive evaluation results are as follows: Obtaining an encryption strength effect evaluation value and a preset encryption strength effect threshold range, comparing the encryption strength effect evaluation value with the preset encryption strength effect threshold range, and if the encryption strength effect evaluation value is within the preset encryption strength effect threshold range, transmitting the financial encrypted data; Otherwise, the key length is automatically updated, and the early warning mechanism is triggered to provide feedback on the encryption strength effect evaluation value to remind the preset personnel to change the encryption algorithm and perform data encryption strength evaluation again to obtain the encryption strength effect evaluation value, until the encryption strength effect evaluation value is within the preset encryption strength effect threshold range.

9. The financial data processing and evaluation system based on the Internet of Things is characterized by: include: Data processing module, anonymous evaluation module, encrypted evaluation module, comprehensive judgment module; The data processing module is used to pre-process the acquired financial data to be shared to obtain financial shared data, and the financial shared data is used to describe the data content that the financial institution needs to share; The anonymous evaluation module is used to perform data anonymization processing on the financial shared data to obtain financial anonymous data, perform data anonymization evaluation based on the financial shared data and the financial anonymous data to obtain an anonymous evaluation index, and determine whether to perform data encryption of the financial anonymous data based on the anonymous evaluation index and a preset anonymous threshold range. The anonymous evaluation index is used to comprehensively quantify the degree of compliance with data anonymization; The encryption evaluation module is used to obtain financial encrypted data after the financial anonymous data is encrypted, evaluate the strength of the data encryption to obtain an encryption strength effect evaluation value, and the encryption strength effect evaluation value is used to comprehensively quantify the degree of compliance of the encryption strength when the financial anonymous data is encrypted; The comprehensive judgment module is used to perform a comprehensive evaluation based on the encryption strength effect evaluation value and the preset encryption strength effect threshold range to obtain a comprehensive evaluation result, and judge whether to transmit financial encrypted data based on the comprehensive evaluation result. If the financial encrypted data is not transmitted, feedback is provided. The comprehensive evaluation result is used to judge whether to transmit financial encrypted data.

10. An electronic device comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein: When the computer program instructions are executed by the processor, the electronic device is triggered to execute the financial data processing and evaluation method based on the Internet of Things as described in any one of claims 1-8.

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