Method for measuring the trustworthiness of digital certificates and digital signatures based on eigenvector algorithm

Abstract

The application discloses a method for measuring the credibility of digital certificates and digital signatures based on a feature vector algorithm, and relates to the technical field of computers, which comprises the following steps: analyzing the format field of a digital certificate, extracting field information, and mapping the field information into a plurality of dimensions of credibility factors according to a mapping rule to generate a credibility feature vector; calculating the comprehensive feature strength of the credibility feature vector by using the Euclidean norm to obtain a quantitative credibility value; calculating the credibility of each digital certificate in a certificate chain constituted by digital signature verification to generate a digital certificate credibility sequence according to the quantitative credibility value; and distributing sequentially decreasing weight values to each digital certificate according to the digital certificate credibility sequence and the sequential position of the digital certificate in the link to generate a set of weight values corresponding to the verified digital certificates. The application separates the link measurement of the certificate from the operation, simplifies the design, improves the flexibility and expandability, and enhances the security protection capability of the digital signature verification as a whole.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method for measuring the trustworthiness of digital certificates and digital signatures based on feature vector algorithms. Background Technology

[0002] With the rapid development of the digital age, Public Key Infrastructure (PKI) has become a key technology system for ensuring network communication security and identity authentication. PKI verifies user identity and public keys through digital certificates and key management, establishing secure communication channels and ensuring the confidentiality and integrity of data transmission. Its core relies on Certificate Authorities (CAs) for the issuance, management, and revocation of digital certificates, forming a certificate chain consisting of root certificates and intermediate certificates, extending trust boundaries and achieving hierarchical trust transfer from root certificates to terminal certificates. As the foundation of the PKI system, CA certificates are not only a core element of digital security but also crucial for ensuring national digital sovereignty. By establishing local CA institutions and root certificate systems, nations reduce their dependence on external technologies and ensure the independent control and security of their network infrastructure.

[0003] While the existing PKI system plays a crucial role in the issuance and verification of digital certificates, traditional verification methods remain insufficient when faced with diverse certificate sources, complex link structures, and missing field information. Current processes typically rely on signature validity, certificate revocation status, and link integrity checks, lacking the ability to quantify multi-dimensional trust factors within certificate fields and failing to reflect the weight differences between nodes in the link. Especially in scenarios with abnormal fields or incomplete links, the lack of flexible fault-tolerance mechanisms and dynamic adjustment methods results in insufficient accuracy in overall digital signature trustworthiness assessment. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for measuring the trustworthiness of digital certificates and digital signatures based on feature vector algorithms, which solves the problem in the prior art of accurately quantifying multidimensional trust factors and effectively reflecting the differences in the weights of each certificate in the certificate chain.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, this invention provides a method for measuring the credibility of digital certificates and digital signatures based on a feature vector algorithm. The method includes: parsing the format fields of the digital certificate, extracting field information, and mapping it to multiple dimensions of credibility factors according to mapping rules to generate a credibility feature vector; calculating the comprehensive feature strength of the credibility feature vector using the Euclidean norm to obtain a quantified credibility value; calculating the credibility of each digital certificate in the certificate chain constituting digital signature verification based on the quantified credibility value to generate a digital certificate credibility sequence; assigning sequentially decreasing weight values ​​to each digital certificate according to the digital certificate credibility sequence and the order of the digital certificates in the chain to generate a set of verified weight correspondences; weighting and accumulating the weight values ​​with the quantified credibility values ​​of the digital certificates one by one based on the set of verified weight correspondences, and adding a normalization compensation value to generate a certificate chain credibility score; and using the certificate chain credibility score to optimize the credibility of the set of digital signatures to be verified to generate a digital signature credibility ranking result.

[0008] As a preferred embodiment of the method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm described in this invention, the steps of parsing the format fields of the digital certificate, extracting field information, and mapping it into multiple-dimensional trustworthiness factors according to mapping rules to generate a trustworthiness feature vector are as follows:

[0009] Extract the issuing authority, country / region, certificate purpose, algorithm type, key length, and key hash value from the digital certificate to form a dataset of field information;

[0010] Based on the mapping rules, the field information dataset is converted into multiple feature dimension values ​​required for credibility evaluation, and combined into a multi-dimensional vector form to generate a credibility feature vector.

[0011] As a preferred embodiment of the method for measuring the credibility of digital certificates and digital signatures based on the feature vector algorithm described in this invention, the specific steps for calculating the comprehensive feature strength of the credibility feature vector using the Euclidean norm to obtain the quantified credibility value are as follows.

[0012] The confidence factors in each dimension of the confidence feature vector are squared to obtain a sequence of squared values.

[0013] The summation of the squared value sequence yields the total dimensional contribution value. The square root of the total dimensional contribution value is then performed to generate the comprehensive feature strength, which is used as the quantitative credibility value of the digital certificate.

[0014] As a preferred embodiment of the method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm described in this invention, the specific steps for calculating the trustworthiness of each digital certificate in the certificate chain formed by digital signature verification based on the quantified trustworthiness value to generate a digital certificate trustworthiness sequence are as follows.

[0015] Parse the digital signature structure and extract the certificate chain according to the verification dependency order;

[0016] The quantitative credibility value of each digital certificate in the certificate chain is combined with the sequential position of the digital certificate in the chain to form a structured scoring node;

[0017] Based on the structured scoring nodes, and using a fault-tolerance mechanism, the credibility values ​​of digital certificates with missing fields are adjusted to the corresponding reduced range and marked as low credibility, and the updated structured scoring nodes are output.

[0018] Based on the updated structured scoring nodes, all quantitative credibility values ​​are arranged in order of their sequence in the link, and the output is a digital certificate credibility sequence.

[0019] As a preferred embodiment of the method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm described in this invention, the specific steps are as follows: Based on the trustworthiness sequence of digital certificates and their sequential position in the link, assigning sequentially decreasing weight values ​​to each digital certificate to generate a set of verified weight correspondences.

[0020] Read the sequence length of the digital certificate trust sequence, generate weight values ​​that correspond to the length of the digital certificate trust sequence and decrease in order, and output a list of weight values;

[0021] The list of weight values ​​is assigned sequentially to each digital certificate in the digital certificate trust sequence according to the link order. By utilizing strict decreasing property, a set of weights corresponding to verified certificates is generated.

[0022] As a preferred embodiment of the method for measuring the credibility of digital certificates and digital signatures based on the feature vector algorithm described in this invention, the following steps are taken: based on the set of verified weights, the weight values ​​are weighted and accumulated with the quantified credibility value of the digital certificate, and a normalized compensation value is added to generate a certificate chain credibility score.

[0023] Based on the verified set of weights, extract the weight value and corresponding quantified credibility value of each digital certificate, and output a list of weight-credibility pairs.

[0024] Read the weight value and corresponding quantified credibility value of each pair of digital certificates in the weight and credibility pair list in sequence, calculate the weighted contribution value, and output the weighted contribution value sequence;

[0025] The weighted contribution value sequence is accumulated and combined with the normalized compensation value to generate a certificate chain credibility score.

[0026] As a preferred embodiment of the method for measuring the credibility of digital certificates and digital signatures based on the feature vector algorithm described in this invention, the following steps are taken: sequentially reading the weight value and corresponding quantized credibility value of each pair of digital certificates in the weight and credibility pair list, calculating the weighted contribution value, and outputting the weighted contribution value sequence.

[0027] Read the list of weight and credibility pairs, extract the weight value and corresponding quantified credibility value of each pair of digital certificates, and calculate to obtain the weighted contribution value;

[0028] Store each pair of weighted contribution values ​​into an ordered set in sequence, and output the sequence of weighted contribution values.

[0029] As a preferred embodiment of the method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm described in this invention, the specific steps for optimizing the trustworthiness of the set of digital signatures to be verified using certificate chain trustworthiness scoring to generate a digital signature trustworthiness ranking result are as follows.

[0030] Digital signature information is extracted from user-uploaded signature files and communication data to obtain a set of digital signatures to be verified;

[0031] Sort the set of digital signatures to be verified in descending order based on the certificate chain trust score, and output the preliminary sorting result;

[0032] For digital signatures with the same score in the initial ranking results, a second ranking is performed by combining timestamp verification features to generate a digital signature credibility ranking result.

[0033] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm as described in the first aspect of the present invention.

[0034] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm as described in the first aspect of the present invention.

[0035] The beneficial effects of this invention are as follows: By extracting and mapping multi-dimensional features from digital certificate format fields and combining them with Euclidean norms for comprehensive calculation, the credibility of digital certificates is accurately quantified, effectively improving the objectivity and scientific nature of credibility assessment; by adopting a decreasing weight allocation and tail padding technology based on certificate chain position, a unified measurement of certificate chains of different lengths is achieved, accurately reflecting the trust transfer process and certificate weight differences; a weight verification and correction mechanism is introduced to ensure the rationality and stability of credibility measurement results; separating measurement and usage operations simplifies the design, improves flexibility and scalability, and enhances the overall security of digital signature verification, meeting the application needs of complex and ever-changing digital security environments. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of the method for measuring the credibility of digital certificates and digital signatures based on the feature vector algorithm in this invention.

[0038] Figure 2 This is a flowchart of the certificate chain credibility sequence generation process in this invention.

[0039] Figure 3 This is a flowchart of the fault tolerance mechanism in this invention.

[0040] Figure 4 This is a flowchart of the credibility threshold and digital signature verification in this invention. Detailed Implementation

[0041] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0042] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0043] 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 phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0044] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for measuring the trustworthiness of digital certificates and digital signatures based on a feature vector algorithm, including the following steps:

[0045] S1. Parse the format fields of the digital certificate, extract the field information, and map it into multiple-dimensional credibility factors according to the mapping rules to generate a credibility feature vector.

[0046] S1.1 Extract the issuing authority, country / region, certificate purpose, algorithm type, key length, and key hash value from the digital certificate to form a field information dataset.

[0047] Specifically, when processing digital certificates, the process involves parsing the certificate's format structure, locating and extracting the identification information corresponding to the "Issuing Authority" field; reading the "Country / Region" field and extracting the country or region code; extracting the "Certificate Purpose" field to obtain the declared function / purpose of the digital certificate, such as "for digital signature," "for key exchange," or "for authentication"; extracting the "Algorithm Type" field to identify the encryption algorithm used in the digital certificate, such as "RSA," "ECC," or "SM2"; reading the "Key Length" field to obtain the length of the key used for encryption operations, such as 2048 bits or 256 bits; and finally, extracting the "Key Hash Value" field to obtain the digest value used to verify the integrity of the public key content. These six extracted results are then used as field information for "Issuing Authority," "Country / Region," "Certificate Purpose," "Algorithm Type," "Key Length," and "Key Hash Value," respectively, and combined in order to form a field information dataset.

[0048] S1.2 Based on the mapping rules, the field information dataset is converted into multiple feature dimension values ​​required for credibility evaluation, and combined into a multi-dimensional vector form to generate a credibility feature vector.

[0049] Specifically, based on mapping rules, the "Issuing Authority" field information in the field information dataset is matched with the issuing authority credibility scoring table and converted into corresponding feature dimension values. For example, "Certification Authority" corresponds to a value of 0.9, and "Commercial Organization" corresponds to a value of 0.6. The "Country / Region" field information is matched with the country / region trust level table and converted into numerical feature dimension values. For example, "CN" corresponds to a value of 0.95, and "US" corresponds to a value of 0.85. The "Certificate Purpose" field information is mapped with the purpose weight table and converted into corresponding credibility factor values. For example, "Used for digital signatures" corresponds to a value of 0.95. The value for "Name" is 1.0, and the value for "Used for Encrypted Transmission" is 0.8. The "Algorithm Type" field is mapped to an algorithm security level table; for example, "RSA" corresponds to a value of 0.85, and "ECC" corresponds to a value of 0.95. The "Key Length" field is mapped to hierarchical feature dimension values ​​based on numerical ranges; for example, a length of 2048 bits corresponds to a value of 0.8, and a length of 4096 bits corresponds to a value of 0.95. The "Key Hash Value" field calculates the number of hash bits and maps them according to bit ranges; for example, 256 bits corresponds to a value of 0.9, and 512 bits corresponds to a value of 0.95. These six feature dimension values ​​are arranged in a fixed order: "Issuing Authority," "Country / Region," "Certificate Purpose," "Algorithm Type," "Key Length," and "Key Hash Value," forming a trustworthiness feature vector.

[0050] It should also be noted that setting mapping rules involves specific steps including collecting the credibility scores or weight tables corresponding to each field, such as the credibility rating table of issuing institutions, the trust level list of countries and regions, the weight standards for certificate uses, the security level of encryption algorithms, and the security classification of key lengths; matching the field information with the corresponding scoring table, and determining the corresponding feature dimension values ​​based on the field values; for numerical fields such as key length and key hash value, using an interval mapping method to map the actual values ​​to feature value intervals; and standardizing all mapping results to ensure that the feature dimension values ​​are within a comparable range, ultimately forming a multi-dimensional vector for credibility calculation.

[0051] The data sources and basis for the pre-set certificate authority credibility rating table include: based on the historical certificate usage of certificate authorities, statistics on the long-term validity, revocation frequency, and erroneous issuance of issued certificates; collecting the actual credibility rating performance of certificate authorities in different certificate chains, and summarizing the average rating level corresponding to different certificate authorities; and through numerical analysis of the correspondence between certificate authorities and the overall credibility of multiple digital certificate chains, determining the degree of influence of certificate authorities on credibility in the actual environment, and generating the certificate authority credibility rating table.

[0052] S2. Using the Euclidean norm, calculate the comprehensive feature strength of the credibility feature vector to obtain the quantified credibility value.

[0053] S2.1. Squaring the confidence factors of each dimension in the confidence feature vector to obtain a sequence of squared values.

[0054] Specifically, read the confidence factors of each dimension in the confidence feature vector in sequence, square each confidence factor, and calculate the corresponding square value; repeat the steps until the confidence factors of all dimensions have been squared; arrange all the calculated square values ​​in the original dimensional order to form a sequence of square values.

[0055] It should also be noted that the credibility factors for each dimension originate from the format fields of the digital certificate. By parsing fields such as the issuing authority, country / region, certificate purpose, algorithm type, key length, and key hash value in the digital certificate, the field information is converted into corresponding credibility factor values ​​according to mapping rules. Specific steps include: extracting various field information from the digital certificate and constructing a field information dataset; mapping each field in the field information dataset to a corresponding credibility factor according to the mapping rules; arranging the credibility factors in a fixed order, corresponding to the six dimensions of "issuing authority," "country / region," "certificate purpose," "algorithm type," "key length," and "key hash value," and combining them to form the dimensions of the credibility feature vector.

[0056] S2.2 Summing the sequence of squared values ​​yields the sum of dimensional contribution values. The sum of dimensional contribution values ​​is then squared to generate the comprehensive feature strength, which is used as the quantitative credibility value of the digital certificate.

[0057] Specifically, all squared values ​​in the sequence are summed using addition to obtain the total dimensional contribution value. The sum of the dimensional contribution values ​​is then square-rooted using arithmetic square root operation to generate the comprehensive feature strength, which serves as the quantified credibility value for the digital certificate. The expression is as follows:

[0058]

[0059] Where t represents the quantified credibility value of the digital certificate, n represents the dimension and number of feature values ​​of the credibility feature vector, and v j This represents the j-th dimension feature value in the credibility feature vector, where j represents the index of the dimension feature value in the credibility feature vector.

[0060] S3. Based on the quantified credibility value, calculate the credibility of each digital certificate in the certificate chain formed by digital signature verification, and generate a digital certificate credibility sequence.

[0061] S3.1 Parse the digital signature structure and extract the certificate chain according to the verification dependency order.

[0062] Specifically, when parsing the digital signature structure, the signature path information in the digital signature is read, and each digital certificate in the signature path is extracted in turn; for each digital certificate, the unique identifier and related fields are obtained and arranged in order according to the signature path; all the extracted digital certificates are connected in the chain order to generate a complete certificate chain.

[0063] S3.2 Combine the quantitative credibility value of each digital certificate in the certificate chain with the sequential position of the digital certificate in the chain to form a structured scoring node.

[0064] Specifically, the quantified trust value of each digital certificate in the certificate chain is read sequentially, and the corresponding digital certificate's link order position in the certificate chain is obtained. The quantified trust value and the corresponding link order position are combined according to a unified data structure to form a structured scoring node containing the unique identifier of the digital certificate, the quantified trust value, and the link order position. This operation is repeated until all digital certificates in the certificate chain have formed corresponding structured scoring nodes.

[0065] S3.3 Based on the structured scoring node, using the fault tolerance mechanism, adjust the credibility value of digital certificates with missing fields to the corresponding reduced range and mark them as low credibility, and output the updated structured scoring node.

[0066] Specifically, the process sequentially reads the digital certificate field information contained in the structured scoring nodes, performs an integrity check on the field content of each digital certificate to determine if any fields are missing. If a field is missing, a fallback factor value corresponding to the missing field is retrieved according to a predefined fault tolerance rule table. For example, the fallback factor is 0.15 when the "algorithm type" field is missing and 0.20 when the "certificate purpose" field is missing. The corresponding fallback factor value is subtracted from the quantized credibility value in the current structured scoring node, and the adjusted result is the corrected credibility value. The corrected credibility value is updated to the original credibility value position in the structured scoring node, and a low credibility state is marked in the structured scoring node, completing the field missing correction process. This process is repeated until all structured scoring nodes are processed, and finally, the updated structured scoring node containing the corrected credibility value and the low credibility mark is output.

[0067] It should also be explained that the specific steps of the predefined fault tolerance rule table are as follows: Collect multiple digital certificate samples with complete and standardized formats, and compile a list of common fields, including "Issuing Authority," "Country / Region," "Certificate Purpose," "Algorithm Type," "Key Length," and "Key Hash Value"; Perform a credibility impact analysis on each field, and statistically analyze the range of credibility score changes corresponding to the missing fields in historical digital certificates. For example, by comparing the credibility difference between samples with complete fields and samples with missing fields, the actual impact of missing fields on credibility can be quantified; Sort the fields according to the strength of their impact on the overall credibility score, and classify the fields into importance levels. For example, "Issuing Authority" has a high impact level, and "Key Hash Value" has a medium impact level; Assign numerical fallback factors according to the importance level of the fields. For example, fields with a high importance level correspond to a fallback factor of 0.25, medium to 0.15, and low to 0.05. Compile a table showing the one-to-one correspondence between fields and their corresponding fallback factors, and save it as a fault tolerance rule table for use in subsequent field missing correction operations.

[0068] S3.4. Based on the updated structured scoring nodes, arrange all the quantified credibility values ​​in order of the digital certificate in the link, and output the digital certificate credibility sequence.

[0069] Specifically, based on the updated structured scoring nodes, the quantitative credibility value corresponding to each digital certificate is read sequentially according to the order of the digital certificates in the link; the read quantitative credibility values ​​are arranged sequentially according to the link order to form an ordered list of digital certificate credibility values; and the digital certificate credibility sequence is output as the digital certificate credibility sequence, thus completing the generation of the digital certificate credibility sequence.

[0070] S4. Based on the digital certificate trust sequence and the order of the digital certificates in the link, assign successively decreasing weight values ​​to each digital certificate to generate a set of weights that have been verified.

[0071] S4.1 Read the sequence length of the digital certificate trust sequence, generate weight values ​​that correspond to the length of the digital certificate trust sequence and decrease in order, and output a list of weight values.

[0072] Specifically, the process involves reading the digital certificate trustworthiness sequence, counting the number of digital certificates in the sequence, and obtaining the sequence length. Based on the sequence length, the weight value is calculated using the following expression:

[0073] w i =2 (K-i)r ;

[0074] Among them, w irepresents the weight value of the i-th digital certificate, K represents the maximum length of the supported certificate chain, r represents the weight scaling factor, and i represents the index variable of the digital certificate;

[0075] It should also be noted that K is used to determine the range of weight distribution from the trust anchor to the end entity and the change span of the exponential benchmark; r is used to control the decay slope of the exponential weight, where r = 1 represents standard decay, 0 < r < 1 represents gentle decay, and r > 1 represents steep decay; the value range of i is 1 ≤ i ≤ K;

[0076] Through the exponential decay mechanism, ensure that the weight value strictly decreases as the position in the certificate chain moves downwards, without external verification; calculate all weight values in ascending order of i, and finally output a list of weight values.

[0077] S4.2. Assign the list of weight values to each digital certificate in the digital certificate credibility sequence in order of the link, and use the strictly decreasing property to generate a set of weights corresponding to qualified verifications.

[0078] Specifically, read all the digital certificates in the digital certificate credibility sequence, and number them in order of their appearance in the certificate chain; according to the total number of digital certificates, starting from the highest bit, assign a decreasing weight value to each digital certificate in order, for example, the first certificate is assigned the largest weight value, the second certificate is assigned the second largest weight value, and so on, until the last certificate is assigned the smallest weight value; then form a one-to-one correspondence between the quantified credibility value of each digital certificate and the corresponding weight value, and combine them in the order of the certificate chain into a structured data set containing the unique identifier of the digital certificate, the quantified credibility value, and the weight value; finally, output the structured data set as the set of weights corresponding to qualified verifications.

[0079] S5. Based on the set of weights corresponding to qualified verifications, accumulate the weight values and the quantified credibility values of the digital certificates one by one, and add a normalization compensation value to generate a credibility score for the certificate chain.

[0080] S5.1. Based on the set of weights corresponding to qualified verifications, extract the weight value and the corresponding quantified credibility value of each digital certificate, and output a list of weight-credibility pairs.

[0081] Specifically, based on the set of weights corresponding to qualified verifications, in accordance with the order of the digital certificates in the digital certificate credibility sequence, read the weight value and the quantified credibility value corresponding to each digital certificate respectively, and form a weight-credibility pair for each group of corresponding weight values and quantified credibility values in a one-to-one matching manner for the digital certificates, and append them to the list of weight-credibility pairs in turn until all digital certificates have been extracted and paired, and finally output a complete list of weight-credibility pairs.

[0082] For example, if the set of valid weights is [0.5, 0.4, 0.3] and the digital certificate credibility sequence is [0.8, 0.6, 0.7], then the generated list of weight and credibility pairs is [(0.5, 0.8), (0.4, 0.6), (0.3, 0.7)].

[0083] S5.2. Read the weight value and corresponding quantified credibility value of each pair of digital certificates in the weight and credibility pair list in sequence, calculate the weighted contribution value, and output the weighted contribution value sequence.

[0084] S5.2.1 Read the list of weight and credibility pairs, extract the weight value and corresponding quantified credibility value of each pair of digital certificates, and calculate to obtain the weighted contribution value.

[0085] Specifically, the weight value and corresponding quantified credibility value of each pair of digital certificates in the weight and credibility pair list are read sequentially, and the weighted contribution value is calculated as follows:

[0086] a i =w i ×c i

[0087] Among them, a i w represents the weighted contribution value of the i-th digital certificate. i c represents the weight and credibility value corresponding to the i-th digital certificate in the list. i This represents the quantized credibility value corresponding to the i-th digital certificate in the weight and credibility pair list, where i represents the index of the digital certificate in the weight and credibility pair list.

[0088] S5.2.2 Store each pair of weighted contribution values ​​into an ordered set in sequence, and output the weighted contribution value sequence.

[0089] Specifically, initialize an empty list of weighted contribution values; for each weighted contribution value, append it to the end of the list; repeat the steps until all weighted contribution values ​​are stored in the list; output a sequence of weighted contribution values ​​containing all weighted contribution values ​​in the same order.

[0090] S5.3. Accumulate the weighted contribution value sequence and combine it with the normalized compensation value to generate a certificate chain credibility score.

[0091] Specifically, each weighted contribution value in the weighted contribution value sequence is read sequentially, and all weighted contribution values ​​are summed to obtain the cumulative total value. Based on the certificate chain length and weight position, and combined with the corresponding value read from the pre-set normalized compensation value, the cumulative total value is adjusted to generate the adjusted total score. The normalized compensation value is dynamically set according to the certificate chain length to ensure that the scores of certificate chains of different lengths are mapped to a unified value range. The final output total score serves as the certificate chain credibility score, which satisfies the following characteristics: it reflects the positional influence of each certificate in the chain, ensures that chains of different lengths or composed of different certificates are mapped to unique values, achieving injective mapping; and guarantees that the certificate chain credibility strictly decreases monotonically as the chain extends, maintaining the monotonicity of the score. The expression is:

[0092]

[0093] Where L represents the certificate chain credibility score, m represents the length of the weighted contribution value sequence, and padding represents the compensation value used to normalize the credibility of certificate chains of different lengths.

[0094] It should also be noted that the pre-set normalized compensation value is determined based on the potential issues of uneven weight distribution or insufficient influence of specific nodes in the digital certificate trustworthiness assessment. A reasonable compensation range is determined through statistical analysis of historical data and security requirement assessment. The specific steps include collecting trustworthiness score samples from multiple certificate chains, using statistical analysis methods to calculate the mean, variance, and frequency distribution of the scores, identifying the central tendency and dispersion of the scores, determining a reasonable range of compensation values ​​based on the statistical results and actual security level requirements, selecting appropriate compensation values ​​to adjust the final score, ensuring that the score results are smoother and reflect changes in the overall trustworthiness of the chain, and storing the compensation values ​​in a fixed manner for use in subsequent score calculations.

[0095] S6. Utilize the certificate chain credibility score to optimize the credibility of the set of digital signatures to be verified and generate a digital signature credibility ranking result.

[0096] S6.1 Extract digital signature information from the user-uploaded signature file and communication data to obtain a set of digital signatures to be verified.

[0097] Specifically, the system reads the user-uploaded signature file and communication data file, identifies the file format type (e.g., PEM, DER, or encapsulation format containing PKCS#7 structure), performs structure parsing on the read file to locate fields containing digital signature content (e.g., "Signature", "SignedData", or "PKCS7-Signature"), identifies and extracts the original signature value, signature algorithm identifier, signature timestamp, and signer identifier from the structured data, extracts the signature-containing data segments sequentially according to the segmentation order in the communication session, performs structure matching on each data segment to check if it contains a standard format digital signature structure, and encapsulates all extracted digital signature values ​​and associated fields into digital signature data items to be verified, and organizes them into a set of digital signatures to be verified.

[0098] S6.2 Sort the set of digital signatures to be verified in descending order according to the certificate chain credibility score, and output the preliminary sorting result.

[0099] Specifically, the process involves reading the certificate chain credibility score corresponding to each digital signature in the set of digital signatures to be verified; establishing a data structure for each digital signature, containing its unique identifier and corresponding certificate chain credibility score; storing all data structures containing certificate chain credibility scores into a list structure; sorting the list structure according to the certificate chain credibility score field in descending order, placing digital signatures with higher certificate chain credibility scores at the top; maintaining the original input order if any digital signatures in the set have the same certificate chain credibility score during the sorting process; and finally outputting the sorted set of digital signatures as the preliminary sorting result.

[0100] S6.3 For digital signatures with the same score in the initial sorting results, perform a secondary sorting based on the timestamp verification feature to generate a digital signature credibility ranking result.

[0101] Specifically, the process involves reading all digital signatures with the same certificate chain credibility score from the initial sorting results; for each digital signature, extracting the timestamp verification feature value recorded in the digital signature, including the timestamp generation time field; storing all digital signatures with the same score and their corresponding timestamp generation times in a temporary sorting structure as key-value pairs; sorting the digital signatures in ascending order according to the value of the timestamp generation time field, so that digital signatures with earlier timestamp generation times are listed first; overwriting the parts with the same score in the original initial sorting results with the sorted results, and merging them with the remaining sorting results to form a complete sorted list; finally, outputting the sorted list as the digital signature credibility sorting result.

[0102] This embodiment also provides a computer device applicable to the method of measuring the trustworthiness of digital certificates and digital signatures based on feature vector algorithms, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method of measuring the trustworthiness of digital certificates and digital signatures based on feature vector algorithms as proposed in the above embodiment.

[0103] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0104] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for measuring the trustworthiness of digital certificates and digital signatures based on a feature vector algorithm as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0105] In summary, this invention achieves accurate quantification of digital certificate credibility by: extracting and mapping multi-dimensional features from digital certificate format fields, combining this with Euclidean norm for comprehensive calculation, thus effectively improving the objectivity and scientific rigor of credibility assessment; employing a decreasing weight allocation and tail padding technique based on certificate chain position to achieve unified measurement of certificate chains of different lengths, accurately reflecting the trust transfer process and certificate weight differences; introducing a weight verification and correction mechanism to ensure the rationality and stability of credibility measurement results; and separating measurement from usage operations to simplify the design, improve flexibility and scalability, and enhance the overall security capabilities of digital signature verification, meeting the application needs of complex and ever-changing digital security environments.

[0106] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm, characterized in that: include, The format fields of the digital certificate are parsed, the field information is extracted, and the information is mapped into multiple dimensions of credibility factors according to the mapping rules to generate a credibility feature vector. Using the Euclidean norm, the comprehensive feature strength of the credibility feature vector is calculated to obtain the quantified credibility value; Based on the quantified credibility value, the credibility of each digital certificate in the certificate chain formed by digital signature verification is calculated to generate a digital certificate credibility sequence. Based on the digital certificate trust sequence and the order of the digital certificates in the link, assign successively decreasing weight values ​​to each digital certificate to generate a set of weights that have been verified. Based on the verified set of weights, the weight values ​​are weighted and accumulated with the quantitative credibility value of the digital certificate, and a normalized compensation value is added to generate a certificate chain credibility score. The credibility of the digital signature set to be verified is optimized by using the certificate chain credibility score, and a digital signature credibility ranking result is generated.

2. The method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm as described in claim 1, characterized in that: The process of parsing the format fields of the digital certificate, extracting field information, and mapping it into multiple-dimensional credibility factors according to mapping rules to generate a credibility feature vector is as follows. Extract the issuing authority, country / region, certificate purpose, algorithm type, key length, and key hash value from the digital certificate to form a dataset of field information; Based on the mapping rules, the field information dataset is converted into multiple feature dimension values ​​required for credibility evaluation, and combined into a multi-dimensional vector form to generate a credibility feature vector.

3. The method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm as described in claim 2, characterized in that: The method of using the Euclidean norm to calculate the comprehensive feature strength of the credibility feature vector to obtain the quantified credibility value is as follows: The confidence factors in each dimension of the confidence feature vector are squared to obtain a sequence of squared values. The summation of the squared value sequence yields the total dimensional contribution value. The square root of the total dimensional contribution value is then performed to generate the comprehensive feature strength, which is used as the quantitative credibility value of the digital certificate.

4. The method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm as described in claim 3, characterized in that: The process of calculating the credibility of each digital certificate in the certificate chain formed by digital signature verification based on the quantified credibility value, and generating a digital certificate credibility sequence, involves the following specific steps. Parse the digital signature structure and extract the certificate chain according to the verification dependency order; The quantitative credibility value of each digital certificate in the certificate chain is combined with the sequential position of the digital certificate in the chain to form a structured scoring node; Based on the structured scoring nodes, and using a fault-tolerance mechanism, the credibility values ​​of digital certificates with missing fields are adjusted to the corresponding reduced range and marked as low credibility, and the updated structured scoring nodes are output. Based on the updated structured scoring nodes, all quantitative credibility values ​​are arranged in order of their sequence in the link, and the output is a digital certificate credibility sequence.

5. The method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm as described in claim 4, characterized in that: The step involves assigning progressively decreasing weight values ​​to each digital certificate based on its trustworthiness sequence and its position in the link, thereby generating a set of verified weights. The specific steps are as follows: Read the sequence length of the digital certificate trust sequence, generate weight values ​​that correspond to the length of the digital certificate trust sequence and decrease in order, and output a list of weight values; The list of weight values ​​is assigned sequentially to each digital certificate in the digital certificate trust sequence according to the link order. By utilizing strict decreasing property, a set of weights corresponding to verified certificates is generated.

6. The method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm as described in claim 5, characterized in that: The process involves weighting and summing the weight values ​​against the quantified credibility values ​​of the digital certificates, one by one, based on the verified set of weights, and adding a normalized compensation value to generate a certificate chain credibility score. The specific steps are as follows: Based on the verified set of weights, extract the weight value and corresponding quantified credibility value of each digital certificate, and output a list of weight-credibility pairs. Read the weight value and corresponding quantified credibility value of each pair of digital certificates in the weight and credibility pair list in sequence, calculate the weighted contribution value, and output the weighted contribution value sequence; The weighted contribution value sequence is accumulated and combined with the normalized compensation value to generate a certificate chain credibility score.

7. The method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm as described in claim 6, characterized in that: The specific steps are as follows: Read the weight value and corresponding quantized credibility value of each pair of digital certificates in the weight and credibility pair list sequentially, calculate the weighted contribution value, and output the weighted contribution value sequence. Read the list of weight and credibility pairs, extract the weight value and corresponding quantified credibility value of each pair of digital certificates, and calculate to obtain the weighted contribution value; Store each pair of weighted contribution values ​​into an ordered set in sequence, and output the sequence of weighted contribution values.

8. The method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm as described in claim 6, characterized in that: The process of using certificate chain credibility scoring to optimize the credibility of the set of digital signatures to be verified and generating a digital signature credibility ranking result involves the following steps: Digital signature information is extracted from user-uploaded signature files and communication data to obtain a set of digital signatures to be verified; Sort the set of digital signatures to be verified in descending order based on the certificate chain trust score, and output the preliminary sorting result; For digital signatures with the same score in the initial ranking results, a second ranking is performed by combining timestamp verification features to generate a digital signature credibility ranking result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for measuring the trustworthiness of digital certificates and digital signatures based on the feature vector algorithm as described in any one of claims 1 to 8.

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