CN117688620B - Certificate verification optimization method and system based on big data information security - Google Patents
Certificate verification optimization method and system based on big data information security Download PDFInfo
- Publication number
- CN117688620B CN117688620B CN202410117643.8A CN202410117643A CN117688620B CN 117688620 B CN117688620 B CN 117688620B CN 202410117643 A CN202410117643 A CN 202410117643A CN 117688620 B CN117688620 B CN 117688620B
- Authority
- CN
- China
- Prior art keywords
- certificate
- ciphertext
- sequence
- encryption
- verification
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012795 verification Methods 0.000 title claims abstract description 124
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000005457 optimization Methods 0.000 title claims abstract description 49
- 230000006854 communication Effects 0.000 claims abstract description 56
- 238000004891 communication Methods 0.000 claims abstract description 51
- 238000004458 analytical method Methods 0.000 claims abstract description 25
- 230000003213 activating effect Effects 0.000 claims description 19
- 238000011156 evaluation Methods 0.000 claims description 18
- 238000012512 characterization method Methods 0.000 claims description 8
- 238000010276 construction Methods 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 claims description 5
- 230000009467 reduction Effects 0.000 claims description 4
- 230000011218 segmentation Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 abstract description 11
- 230000006855 networking Effects 0.000 abstract description 6
- 230000035772 mutation Effects 0.000 description 7
- 238000013475 authorization Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000002922 simulated annealing Methods 0.000 description 1
Landscapes
- Storage Device Security (AREA)
Abstract
The application provides a certificate verification optimization method and a system based on big data information security, which relate to the technical field of information security, and the method comprises the following steps: when a user receives an image of a book to be verified, a semantic analysis node of the user side is activated to acquire basic information of the book to be verified, an encryption communication channel is built based on a dynamic variation algorithm, the type and the number of the certificate are transmitted to a service cloud through the encryption communication channel, an information grabbing node is activated, then standard information of the certificate is acquired, a certificate verification result is acquired according to the validity period of the certificate, and the verification result is transmitted to the user side through the encryption communication channel. The application mainly solves the problems that the information is easy to leak in the networking automatic verification, the efficiency is low when a large amount of data is processed, the verification efficiency is low, and the method can not be used for the verification work in batches. By constructing the encryption communication mode, the information can be ensured not to be leaked or tampered in the subsequent communication process, and the safety of the verification process is ensured.
Description
Technical Field
The application relates to the technical field of information security, in particular to a certificate verification optimization method and system based on big data information security.
Background
With the rapid development of the internet and digital technology, big data has become an important resource for various industries. In the context of big data, information security issues are increasingly prominent, facing a variety of threats from both the inside and the outside. The certificate verification is a common security means, but with the increase of data volume, the conventional certificate verification method is difficult to meet the requirement and needs to be optimized. Certificate verification is an important component of information security for validating entity identities, verifying authorizations, and the like. In a big data environment, the efficiency and accuracy of certificate verification have a significant impact on information security. Once certificate verification is problematic, serious consequences such as leakage of sensitive information, malicious attack on the system and the like may be caused.
The current certificate verification mode mainly directly goes to a portal network of a certificate issuing unit to inquire the authenticity of a certificate, and the main disadvantage of the mode is that the verification efficiency is low and the method is not applicable to batch verification work. How to promote the intelligence of certificate verification becomes the main research development direction.
However, in the process of implementing the technical scheme of the embodiment of the application, the inventor discovers that the above technology has at least the following technical problems:
The networking automatic verification is easy to cause information leakage, and the efficiency is low when a large amount of data are processed, so that the verification efficiency is low, and the networking automatic verification cannot be used for batched verification work.
Disclosure of Invention
The application mainly solves the problems that the information is easy to leak in the networking automatic verification, the efficiency is low when a large amount of data is processed, the verification efficiency is low, and the method can not be used for the verification work in batches.
In view of the above problems, the present application provides a certificate verification optimization method and system based on big data information security, and in a first aspect, the present application provides a certificate verification optimization method based on big data information security, the method comprising: when a user side receives an image of a book to be verified, activating a semantic analysis node embedded in the user side to obtain basic information of the book to be verified, wherein the basic information of the book to be verified comprises a certificate type, a certificate number and a first certificate owner; performing local verification according to the certificate type and the certificate number, and building an encryption communication channel of the user side and the service cloud based on a dynamic variation algorithm when the local verification passes; when the certificate type, the certificate number and the first certificate owner are transmitted to the service cloud through the encryption communication channel, an information capturing node embedded in the service cloud is activated, data acquisition is carried out according to the certificate type and the certificate number, and certificate standard information is obtained, wherein the certificate standard information comprises a second certificate owner and a certificate valid period; performing certificate verification according to the certificate validity period, the second certificate owner and the first certificate owner to obtain a certificate verification result; and transmitting the certificate verification result to the user terminal through the encrypted communication channel.
In a second aspect, the present application provides a certificate verification optimization system based on big data information security, the system comprising: the system comprises a to-be-verified book basic information acquisition module, a first certificate identification module and a second certificate identification module, wherein the to-be-verified book basic information acquisition module is used for activating a semantic analysis node embedded in a user side to acquire to-be-verified book basic information when the user side receives an to-be-verified book image, and the to-be-verified book basic information comprises a certificate type, a certificate number and a first certificate owner; the encryption communication channel construction module is used for carrying out local verification according to the certificate type and the certificate number, and when the local verification passes, the encryption communication channel of the user side and the service cloud is constructed based on a dynamic variation algorithm; the certificate standard information acquisition module is used for activating an information capture node embedded in the service cloud when the certificate type, the certificate number and the first certificate owner are transmitted to the service cloud through the encrypted communication channel, and acquiring data according to the certificate type and the certificate number to obtain certificate standard information, wherein the certificate standard information comprises a second certificate owner and a certificate valid period; the certificate verification result acquisition module is used for performing certificate verification according to the certificate validity period, the second certificate owner and the first certificate owner to acquire a certificate verification result; and the result transmission module is used for transmitting the certificate verification result to the user terminal through the encrypted communication channel.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The application provides a certificate verification optimization method and a system based on big data information security, which relate to the technical field of information security, and the method comprises the following steps: when a user receives an image of a book to be verified, a semantic analysis node of the user side is activated to acquire basic information of the book to be verified, an encryption communication channel is built based on a dynamic variation algorithm, the type and the number of the certificate are transmitted to a service cloud through the encryption communication channel, an information grabbing node is activated, then standard information of the certificate is acquired, a certificate verification result is acquired according to the validity period of the certificate, and the verification result is transmitted to the user side through the encryption communication channel.
The application mainly solves the problems that the information is easy to leak in the networking automatic verification, the efficiency is low when a large amount of data is processed, the verification efficiency is low, and the method can not be used for the verification work in batches. By constructing the encryption communication mode, the information can be ensured not to be leaked or tampered in the subsequent communication process, and the safety of the verification process is ensured.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
In order to more clearly illustrate the application or the technical solutions of the prior art, the following brief description will be given of the drawings used in the description of the embodiments or the prior art, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained from the drawings provided without the inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of a certificate verification optimization method based on big data information security according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for constructing the encrypted communication channel in the certificate verification optimization method based on big data information security according to the embodiment of the application;
FIG. 3 is a schematic flow chart of a method for obtaining an encryption key sequence and a decryption key sequence in a certificate verification optimization method based on big data information security according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a certificate verification optimizing system based on big data information security according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a to-be-verified book basic information acquisition module 10, an encrypted communication channel construction module 20, a certificate standard information acquisition module 30, a certificate verification result acquisition module 40 and a result transmission module 50.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The application mainly solves the problems that the information is easy to leak in the networking automatic verification, the efficiency is low when a large amount of data is processed, the verification efficiency is low, and the method can not be used for the verification work in batches. By constructing the encryption communication mode, the information can be ensured not to be leaked or tampered in the subsequent communication process, and the safety of the verification process is ensured.
For a better understanding of the foregoing technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments of the present invention:
In a first embodiment, as shown in fig. 1, a certificate verification optimization method based on big data information security is applied to a certificate verification optimization system, where the system includes a user end and a service cloud, and the method includes:
When a user side receives an image of a book to be verified, activating a semantic analysis node embedded in the user side to obtain basic information of the book to be verified, wherein the basic information of the book to be verified comprises a certificate type, a certificate number and a first certificate owner;
In particular, when a user receives an image of a certificate to be verified, this image may contain important information about the certificate. To extract this information, the client activates the embedded semantic analysis node. A semantic analysis node is a program or module that is dedicated to analyzing and understanding semantic information in text or images. By activating the semantic analysis node, the user side can extract basic information of the book to be verified, wherein the basic information comprises a certificate type, a certificate number and a first certificate owner. This information is critical to verifying the authenticity and validity of the certificate. Certificate type: this generally refers to which type of certificate to be verified is, e.g. an academic certificate, a professional qualification certificate, etc. Certificate number: each certificate has a unique number that identifies the certificate. By checking the certificate number, it is possible to confirm whether the certificate exists truly, and prevent forgery or falsification of the certificate of another person. First certificate owner: this is typically referred to as the person holding the certificate. Confirming the first certificate owner helps to confirm whether the certificate belongs to the person, which is important to prevent the certificate from being spoofed or misused. After obtaining the basic information, the user side may further perform verification operations, such as comparing with an authority database, verifying a certificate authority, and the like, to finally confirm the authenticity and validity of the certificate.
Performing local verification according to the certificate type and the certificate number, and building an encryption communication channel of the user side and the service cloud based on a dynamic variation algorithm when the local verification passes;
Specifically, when the user receives the image of the certificate to be verified and extracts the certificate type and the certificate number, local verification is performed. The local verification refers to certificate verification directly performed at a user terminal, and does not involve interaction with a service cloud. According to the extracted certificate type and the certificate number, the user side can compare whether the certificate information is consistent with the known correct certificate information or judge whether the certificate is valid or not through other verification methods. If the local authentication is passed, i.e. the certificate is confirmed to be valid, an encrypted communication channel needs to be established between the user side and the service cloud. In this process, dynamic mutation algorithms are used to ensure the security of the communication. Dynamic mutation algorithms are encryption techniques that protect information from malicious attacks or theft by introducing random variations (mutations) in the original information. The algorithm is characterized in that variation is carried out during each communication, so that the communication content is different each time, and even small variation can have a large influence, so that an attacker is difficult to guess and crack the communication content. By setting up an encryption communication channel of the user side and the service cloud, information can be ensured not to be leaked or tampered in the subsequent communication process. Thus, the image to be verified and other important information can be transmitted more safely, and the smooth progress of the whole verification process is ensured.
When the certificate type, the certificate number and the first certificate owner are transmitted to the service cloud through the encryption communication channel, an information capturing node embedded in the service cloud is activated, data acquisition is carried out according to the certificate type and the certificate number, and certificate standard information is obtained, wherein the certificate standard information comprises a second certificate owner and a certificate valid period;
Specifically, first, after the certificate type, the certificate number, and the first certificate owner are transmitted to the service cloud through the encrypted communication channel, the information is securely stored or processed. The service cloud then activates the embedded information capture node, a program or module that is dedicated to capturing and organizing information from various sources. The information capture node can collect data according to the transmitted certificate type and the transmitted certificate number. This may include retrieving relevant information from a database, retrieving data from other systems, or retrieving up-to-date data for a certificate directly from an authority. Through these data collection operations, the information capture node may obtain standard information for the certificate. Such standard information may include the second certificate owner and the certificate expiration date. Second certificate owner: this generally refers to another person who owns the certificate in addition to the first certificate owner. Confirming the second certificate owner is important to prevent the certificate from being used or transferred erroneously. Certificate expiration date: each certificate has its expiration date, and after this period, the certificate may expire. Validating the expiration date of a certificate can help determine whether the certificate is still valid, which is important to prevent the use of expired certificates. After obtaining the standard information, the service cloud may further verify and process the information, such as comparing with authority data, updating certificate information, etc., to finally confirm the authenticity, validity, and current holder of the certificate.
Performing certificate verification according to the certificate validity period, the second certificate owner and the first certificate owner to obtain a certificate verification result;
Specifically, in performing the certificate verification, a plurality of factors, such as the certificate validity period, the second certificate owner, and the first certificate owner, need to be considered. These factors may help determine the authenticity and validity of the certificate. First, the expiration date of a certificate is an important consideration. If the certificate has expired, it may be invalid. Thus, the service cloud will check the validity period of the certificate to determine if it is still valid. Second, the second certificate owner is also an important consideration. If the second certificate owner is confirmed to be legitimate, the certificate may be valid. Thus, the service cloud verifies the identity and authorization of the owner of the second certificate to determine if the certificate is valid. Finally, the first certificate owner is also an important consideration. If the first certificate owner is confirmed to be legitimate, the certificate may be valid. Thus, the service cloud verifies the identity and authorization of the owner of the first certificate to determine if the certificate is valid. After the factors are integrated, the service cloud performs a final verification step, namely, logic judgment is performed according to the information, and a certificate verification result is obtained. This result is typically a definitive conclusion such as "the certificate is valid" or "the certificate is invalid". After the certificate verification result is obtained, the service cloud end can send the result to the user end, so that the user end can take corresponding operation according to the result. For example, if the certificate is valid, the client may further process business logic associated with the certificate, and if the certificate is invalid, the client may reject the request or prompt the user that the certificate is invalid.
And transmitting the certificate verification result to the user terminal through the encrypted communication channel.
Specifically, after the certificate verification is completed, the service cloud obtains a certificate verification result. This result is important to the user's end because it can tell the user if the certificate is valid, thereby helping them make a corresponding decision. In order to ensure that the result can be safely transmitted to the client, the service cloud transmits the result through the previously established encrypted communication channel. The encryption communication channel can ensure that information is not stolen or tampered in the transmission process, thereby ensuring the safety and the accuracy of the certificate verification result. After the certificate verification result is transmitted to the user terminal, the user terminal receives the result and takes corresponding operation according to the result. For example, if the certificate is valid, the client may further process the business logic associated with the certificate; if the certificate is invalid, the user may reject the request or prompt the user that the certificate is invalid. In summary, by transmitting the certificate verification result to the user side, the user can learn the authenticity and validity of the certificate, thereby making a correct decision.
Further, when a user side receives an image of a book to be verified, the method activates a semantic analysis node embedded in the user side to obtain basic information of the book to be verified, wherein the basic information of the book to be verified comprises a certificate type, a certificate number and a first certificate owner, and the method comprises the following steps:
the book image to be verified at least comprises a certificate front image and a certificate back image;
The user side interacts with the user to obtain the certificate type, and verification backtracking is performed based on a local database to obtain verification history data;
When the data volume of the verification history data is equal to 0, activating a character recognition channel of the semantic analysis node, and performing semantic analysis on the certificate front image and the certificate back image to obtain the certificate number and the identity information of the first certificate owner;
activating a portrait segmentation node of the semantic analysis node, and performing portrait edge cutting on the front image of the certificate to obtain first certificate portrait information;
and adding the first certificate owner identity information and the first certificate portrait information into the first certificate owner.
Specifically, when verifying an image of a book to be verified, detailed information in the image needs to be acquired. The certificate front image typically contains the main information of the certificate, such as the certificate type, the certificate number, and the identity information of the first certificate owner, etc. While the certificate back image may contain other information related to the certificate front image, such as the identity of the issuing authority, the expiration date of the certificate, etc. By analyzing and processing the certificate front side image and the certificate back side image, important information such as a certificate number, identity information of the first certificate owner and the like, which are important for verifying the authenticity and validity of the certificate, can be extracted. And the certificate type is obtained through interaction between the user side and the user. This may involve user input or automatic recognition techniques. And performing verification backtracking based on the local database to obtain verification history data. This may involve comparing previously stored certificate information, such as historical data relating to the certificate type, certificate number and the first certificate owner. And if the data quantity of the verification historical data is 0, namely no available historical data exists, activating a character recognition channel of the semantic analysis node. The channel is mainly used for carrying out character recognition on the front image of the certificate and the back image of the certificate, so that the certificate number and the identity information of the owner of the first certificate are extracted. And activating a portrait segmentation node of the semantic analysis node, and performing portrait edge cutting on the front image of the certificate so as to obtain portrait information of the first certificate. And adding the extracted first certificate owner information and the first certificate portrait information to the first certificate owner information. This can be used for further verification or comparison with information in the database. Through the steps, the image of the book to be verified is comprehensively analyzed and extracted, important certificate information and identity information are obtained, and basic data are provided for subsequent verification and processing. Meanwhile, a local database and a semantic analysis technology are combined, and the accuracy and the efficiency of verification are improved.
Further, as shown in fig. 2, the method of the present application performs local verification according to the certificate type and the certificate number, and when the local verification passes, establishes an encrypted communication channel between the client and the service cloud based on a dynamic mutation algorithm, including:
Backtracking is carried out based on a local database according to the certificate type and the certificate number, and a certificate repeated identifier is obtained, wherein the certificate repeated identifier comprises a non-repeated on-duty certificate or a repeated on-duty certificate;
When the repeated identification of the certificate is the repeated-off-duty-free certificate, the local verification is passed, a ciphertext database is activated, and an encryption sequence optimization space is constructed;
based on the dynamic variation algorithm, optimizing in the encryption sequence optimization space to obtain an encryption key sequence and a decryption key sequence;
And sending the decryption key sequence to the service cloud through a short message operator, and constructing the encryption communication channel based on the encryption key sequence.
Specifically, first, according to the provided certificate type and the provided certificate number, the service cloud performs backtracking query in the local database. This retrospective query is to confirm whether the certificate already exists, i.e. whether there is a duplicate certificate. If the query result is a non-duplicate on-duty certificate, indicating that the certificate is valid, the service cloud builds an encryption sequence optimization space by activating a ciphertext database. The encryption sequence optimization space is based on a dynamic mutation algorithm, and optimization operation can be performed in the encryption sequence optimization space to obtain the optimal encryption key sequence and decryption key sequence. After obtaining the key sequences, the service cloud end sends the decryption key sequences to the user end through the short message service provider. The user terminal can use the decryption key sequence to decrypt the encrypted information sent by the service cloud. Meanwhile, the service cloud end also uses the encryption key sequence to construct an encryption communication channel. The encryption communication channel can ensure that information cannot be leaked or tampered in the subsequent communication process, thereby ensuring the safety of communication. If the query results are repeated on duty of the certificate, indicating that the certificate is invalid, the service cloud may reject the request or prompt the user that the certificate is invalid.
Further, as shown in fig. 3, the method of the present application performs optimization in the encryption sequence optimization space based on the dynamic mutation algorithm to obtain an encryption key sequence and a decryption key sequence, and includes:
The encryption sequence optimization space is configured on an offline optimizing computing node, and a ciphertext data set is extracted, wherein any ciphertext of the ciphertext data set is provided with an encryption key and a decryption key;
traversing the ciphertext data set to perform security coefficient identification to generate a ciphertext security coefficient set;
and based on the encryption sequence length identification, carrying out combined optimization on the ciphertext security coefficient set to obtain the encryption key sequence and the decryption key sequence.
Specifically, firstly, the encryption sequence optimization space is configured in an offline optimizing computing node, and the node can be a server or a computer with strong computing power. Then, a ciphertext data set is extracted from the database, the data set comprising a series of ciphertext, each ciphertext having a corresponding encryption key and decryption key. Next, the ciphertext data set is traversed and each ciphertext is identified for a security coefficient. This security factor may be an indicator of the security of the ciphertext, such as the complexity, length, etc. of the ciphertext. The generated set of security coefficients contains security coefficient information for each ciphertext. And then, based on the encryption sequence length identification, carrying out combined optimization on the ciphertext security coefficient set. This process may use some optimization algorithm, such as genetic algorithm, simulated annealing algorithm, etc. By this combined optimization procedure, an optimal encryption key sequence and decryption key sequence can be obtained. After these key sequences are obtained, the sequences can be used to encrypt and decrypt information. This process may be performed online or offline, depending on the particular application scenario and requirements. By configuring an offline optimizing calculation node, extracting a ciphertext data set and identifying a security coefficient, and then carrying out combined optimization by using an optimization algorithm, an optimal encryption key sequence and decryption key sequence can be obtained, so that the security of information is ensured.
Further, the method of the present application traverses the ciphertext data set to identify the security coefficient, and generates the ciphertext security coefficient set, and the method comprises the following steps:
Constructing a safety coefficient evaluation function:
；
Wherein, Representing the safety coefficient of any ciphertext,/>Characterizing any ciphertext,/>Characterizing ciphertext A Slave~/>Frequency of choice within time interval,/>Characterizing the service duration of ciphertext A,/>As a function of the reduction of the length of service,/>A is a scaling degree adjusting parameter of the service duration; and traversing the ciphertext data set to identify the safety coefficient according to the safety coefficient evaluation function, and generating the ciphertext safety coefficient set.
Specifically, first, a safety factor evaluation function is constructed, which accepts three parameters: the method comprises the steps of representing the variable of the safety coefficient of any ciphertext, representing the variable of any ciphertext and representing the variable of the selected frequency of the ciphertext A in the time interval from t1 to t 2. The function returns an evaluation result based on the factors of the safety coefficient, the selected frequency, the service duration and the like of the ciphertext. The function firstly calculates the selected frequency of the ciphertext, and then calculates the safety coefficient according to the selected frequency and the service duration. The selection frequency refers to the number of times a certain ciphertext is selected in a period of time, and the service duration refers to the service time length of the certain ciphertext. The function also considers a narrowing function that narrows the length of service to a certain extent to avoid an adverse effect on the safety factor from excessive growth of the length of service. And then traversing the ciphertext data set by using the security coefficient evaluation function, and carrying out security coefficient identification on each ciphertext. For each ciphertext, its security coefficient is calculated and stored in a ciphertext security coefficient set. Finally, the generated ciphertext security coefficient set contains the security coefficient information of each ciphertext, and can be used for subsequent encryption and decryption operations. This information may be stored in a database or file for later use.
Furthermore, the method of the present application performs combined optimization on the ciphertext security coefficient set based on the encryption sequence length identifier to obtain the encryption key sequence and the decryption key sequence, and includes: constructing an encryption sequence fitness function:
,
,
Wherein, Characterizing the fitness of a kth encrypted ciphertext combining sequence,/>Characterizing a kth encrypted ciphertext combination sequence,/>Ciphertext security coefficients that characterize the ith sequential ciphertext of the kth encrypted ciphertext combination sequence,Ciphertext of the ith order characterizing the kth encrypted ciphertext combining sequence/>~/>The time interval is deployed to the frequency of the ith sequence, N is the total number of ciphertexts of the kth ciphertexts combined sequence, and corresponds to the length identification of the ciphertexts;
combining the ciphertext data sets according to the encryption sequence length identification to generate a plurality of encryption ciphertext combined sequences;
And optimizing the plurality of encrypted ciphertext combining sequences based on the encryption sequence fitness function and the ciphertext security coefficient set to obtain the encryption key sequence and the decryption key sequence.
Specifically, first, an encryption sequence fitness function is constructed, which accepts two parameters: a variable representing the fitness of the kth encrypted ciphertext combining sequence, a variable representing the kth encrypted ciphertext combining sequence. The function returns an fitness value based on factors such as ciphertext security coefficient and deployment frequency of the kth encrypted ciphertext combining sequence. The function firstly calculates the total number N of ciphertext of the kth encrypted ciphertext combination sequence, and then calculates the fitness value of each sequence according to the ciphertext safety coefficient and the deployment frequency. These fitness values are combined to form the fitness of the kth encrypted ciphertext combination sequence. And then, combining the ciphertext data sets according to the encryption sequence length identification to generate a plurality of encryption ciphertext combined sequences. And then optimizing a plurality of encrypted ciphertext combining sequences based on the encryption sequence fitness function and the ciphertext security coefficient set. Finally, the obtained encryption key sequence and decryption key sequence are used in the actual encryption and decryption operations. These key sequences may be stored in a database or file for later use. The optimal encryption key sequence and decryption key sequence can be obtained through the steps of constructing the fitness function, generating a plurality of encryption ciphertext combination sequences, optimizing and the like, so that the information security is ensured. Further, the method of the present application optimizes the plurality of encrypted ciphertext combining sequences based on the encrypted sequence fitness function and the ciphertext security coefficient set to obtain the encrypted key sequence and the decrypted key sequence, and includes:
Constructing a sequence similarity evaluation function:
，
Wherein, Representing the similarity coefficient of the k+m encrypted ciphertext combination sequence and the k+l encrypted ciphertext combination sequence,/>Characterizing the same ciphertext quantity of a k+mth encrypted ciphertext combination sequence and a k+1th encrypted ciphertext combination sequence,/>Representing the number of ciphertexts with the same order and the same order of the k+m encrypted ciphertext combination sequence and the k+l encrypted ciphertext combination sequence,/>For the first weight,/>For the second weight,/>Characterization of encryption sequence Length,/>Characterization of the k+mth encrypted ciphertext combination sequence,/>Characterizing the k+1st encrypted ciphertext combination sequence;
and setting a sequence similarity coefficient threshold value, wherein the sequence similarity coefficient of any two encrypted ciphertext combination sequences of the plurality of encrypted ciphertext combination sequences is smaller than the sequence similarity coefficient threshold value.
Specifically, first, a sequence similarity evaluation function is constructed, which accepts six parameters: the method comprises the steps of representing variables of similarity coefficients of a k+m encrypted ciphertext combining sequence and a k+l encrypted ciphertext combining sequence, representing the same ciphertext quantity of the k+m encrypted ciphertext combining sequence and the k+l encrypted ciphertext combining sequence, representing the variable of a first weight, representing the variable of a second weight, representing the length of the encrypted sequence, representing the k+m encrypted ciphertext combining sequence and representing the k+l encrypted ciphertext combining sequence. The function returns a similarity coefficient value that is calculated based on the parameters described above. The function first calculates the same ciphertext number and the same ciphertext number in the same order of the two encrypted ciphertext combination sequences, and then calculates the similarity coefficient according to the two numbers, the first weight, the second weight and the encryption sequence length. Next, a sequence similarity coefficient threshold is set. This threshold is a preset value that is used to determine whether the two encrypted ciphertext combining sequences are sufficiently similar. And finally, traversing any two combined sequences of the plurality of encrypted ciphertext combined sequences, calculating the similarity coefficients of the two combined sequences, and judging whether the similarity coefficients are larger than or equal to a set sequence similarity coefficient threshold value. If so, the two combined sequences are considered to be similar, too close, and cannot be added to the ciphertext combined sequence to be selected. If not, it can be added to the ciphertext combination sequence to be selected.
In a second embodiment, based on the same inventive concept as the certificate verification optimization method based on big data information security in the foregoing embodiment, as shown in fig. 4, the present application provides a certificate verification optimization system based on big data information security, the system comprising:
The to-be-verified book basic information acquisition module 10 is used for activating a semantic analysis node embedded in the user side to acquire to-be-verified book basic information when the user side receives an to-be-verified book image, wherein the to-be-verified book basic information comprises a certificate type, a certificate number and a first certificate owner;
The encryption communication channel construction module 20 is configured to perform local verification according to the certificate type and the certificate number, and when the local verification passes, construct an encryption communication channel of the user side and the service cloud based on a dynamic mutation algorithm;
the certificate standard information acquisition module 30 is configured to activate an information capture node embedded in the service cloud when the certificate type, the certificate number and the first certificate owner are transmitted to the service cloud through the encrypted communication channel, and acquire certificate standard information according to the data acquisition of the certificate type and the certificate number, where the certificate standard information includes a second certificate owner and a certificate validity period;
a certificate verification result obtaining module 40, where the certificate verification result obtaining module 40 is configured to perform certificate verification according to the certificate validity period, the second certificate owner, and the first certificate owner, and obtain a certificate verification result;
And the result transmission module 50 is configured to transmit the certificate verification result to the user terminal through the encrypted communication channel.
Further, the system comprises:
The first certificate portrait information acquisition module at least comprises a certificate front image and a certificate back image; the user side interacts with the user to obtain the certificate type, and verification backtracking is performed based on a local database to obtain verification history data; when the data volume of the verification history data is equal to 0, activating a character recognition channel of the semantic analysis node, and performing semantic analysis on the certificate front image and the certificate back image to obtain the certificate number and the identity information of the first certificate owner; activating a portrait segmentation node of the semantic analysis node, and performing portrait edge cutting on the front image of the certificate to obtain first certificate portrait information; and adding the first certificate owner identity information and the first certificate portrait information into the first certificate owner.
Further, the system comprises:
The encryption communication channel construction module is used for backtracking based on a local database according to the certificate type and the certificate number to obtain a certificate repeated identifier, wherein the certificate repeated identifier comprises a non-repeated on-duty certificate or a repeated on-duty certificate; when the repeated identification of the certificate is the repeated-off-duty-free certificate, the local verification is passed, a ciphertext database is activated, and an encryption sequence optimization space is constructed; based on the dynamic variation algorithm, optimizing in the encryption sequence optimization space to obtain an encryption key sequence and a decryption key sequence; and sending the decryption key sequence to the service cloud through a short message operator, and constructing the encryption communication channel based on the encryption key sequence.
Further, the system comprises:
the encryption key sequence and decryption key sequence acquisition module is used for configuring the encryption sequence optimization space in an offline optimization computing node and extracting a ciphertext data set, wherein any ciphertext of the ciphertext data set is provided with an encryption key and a decryption key; traversing the ciphertext data set to perform security coefficient identification to generate a ciphertext security coefficient set; and based on the encryption sequence length identification, carrying out combined optimization on the ciphertext security coefficient set to obtain the encryption key sequence and the decryption key sequence.
Further, the system comprises:
the safety coefficient set acquisition module is used for constructing a safety coefficient evaluation function: ; wherein/> Representing the safety coefficient of any ciphertext,/>Characterizing any ciphertext,/>Characterizing ciphertext A slave/>~/>Frequency of choice within time interval,/>Characterizing the service duration of ciphertext A,/>As a function of the reduction of the length of service,/>A is a scaling degree adjusting parameter of the service duration; and traversing the ciphertext data set to identify the safety coefficient according to the safety coefficient evaluation function, and generating the ciphertext safety coefficient set.
Further, the system comprises:
the encryption key sequence and decryption key sequence acquisition module is used for constructing an encryption sequence fitness function: , Wherein/> Characterizing the fitness of a kth encrypted ciphertext combining sequence,/>Characterizing a kth encrypted ciphertext combination sequence,/>Ciphertext security coefficient characterizing the ith order ciphertext of a kth encrypted ciphertext combination sequence,/>Ciphertext characterizing the ith order of the kth encrypted ciphertext combining sequence~/>The time interval is deployed to the frequency of the ith sequence, N is the total number of ciphertexts of the kth ciphertexts combined sequence, and corresponds to the length identification of the ciphertexts; combining the ciphertext data sets according to the encryption sequence length identification to generate a plurality of encryption ciphertext combined sequences; and optimizing the plurality of encrypted ciphertext combining sequences based on the encryption sequence fitness function and the ciphertext security coefficient set to obtain the encryption key sequence and the decryption key sequence.
Further, the system comprises:
the similarity evaluation module is used for constructing a sequence similarity evaluation function: Wherein, the method comprises the steps of, wherein, Representing the similarity coefficient of the k+mth encrypted ciphertext combination sequence and the k+1th encrypted ciphertext combination sequence,/>Characterizing the same ciphertext quantity of the k+mth and k+l encrypted ciphertext combining sequences,/>Representing the number of ciphertexts with the same order and the same order of the k+m encrypted ciphertext combination sequence and the k+l encrypted ciphertext combination sequence,/>For the first weight,/>For the second weight,/>Characterization of encryption sequence Length,/>Characterization of the k+mth encrypted ciphertext combination sequence,/>Characterizing the k+l encrypted ciphertext combination sequence; and setting a sequence similarity coefficient threshold value, wherein the sequence similarity coefficient of any two encrypted ciphertext combination sequences of the plurality of encrypted ciphertext combination sequences is smaller than the sequence similarity coefficient threshold value.
Through the foregoing detailed description of the certificate verification optimizing method based on big data information security, those skilled in the art can clearly know the certificate verification optimizing system based on big data information security in this embodiment, and for the system disclosed in the embodiment, since the system corresponds to the embodiment disclosure device, the description is simpler, and relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (3)
1. The certificate verification optimization method based on big data information security is characterized by being applied to a certificate verification optimization system, wherein the system comprises a user side and a service cloud, and comprises the following steps:
When a user side receives an image of a book to be verified, activating a semantic analysis node embedded in the user side to obtain basic information of the book to be verified, wherein the basic information of the book to be verified comprises a certificate type, a certificate number and a first certificate owner;
performing local verification according to the certificate type and the certificate number, and building an encryption communication channel of the user side and the service cloud based on a dynamic variation algorithm when the local verification passes;
wherein, still include:
Backtracking is carried out based on a local database according to the certificate type and the certificate number, and a certificate repeated identifier is obtained, wherein the certificate repeated identifier comprises a non-repeated on-duty certificate or a repeated on-duty certificate;
When the repeated identification of the certificate is the repeated-off-duty-free certificate, the local verification is passed, a ciphertext database is activated, and an encryption sequence optimization space is constructed;
based on the dynamic variation algorithm, optimizing in the encryption sequence optimization space to obtain an encryption key sequence and a decryption key sequence;
the decryption key sequence is sent to the service cloud through a short message operator, and the encryption communication channel is constructed based on the encryption key sequence;
The encryption sequence optimization space is configured on an offline optimizing computing node, and a ciphertext data set is extracted, wherein any ciphertext of the ciphertext data set is provided with an encryption key and a decryption key;
traversing the ciphertext data set to perform security coefficient identification to generate a ciphertext security coefficient set;
based on the encryption sequence length identification, carrying out combined optimization on the ciphertext security coefficient set to obtain the encryption key sequence and the decryption key sequence;
Constructing a safety coefficient evaluation function:
；
Wherein, Representing the safety coefficient of any ciphertext,/>Characterizing any ciphertext,/>Characterizing ciphertext A slave/>~/>Frequency of choice within time interval,/>Characterizing the service duration of ciphertext A,/>As a function of the reduction of the length of service,/>A is a scaling degree adjusting parameter of the service duration;
Traversing the ciphertext data set to perform safety coefficient identification according to the safety coefficient evaluation function, and generating the ciphertext safety coefficient set;
Constructing an encryption sequence fitness function:
,
,
Wherein, Characterizing the fitness of a kth encrypted ciphertext combining sequence,/>Characterizing a kth encrypted ciphertext combination sequence,/>Ciphertext security coefficient characterizing the ith order ciphertext of a kth encrypted ciphertext combination sequence,/>Ciphertext of the ith order characterizing the kth encrypted ciphertext combining sequence/>~/>The time interval is deployed to the frequency of the ith sequence, N is the total number of ciphertexts of the kth ciphertexts combined sequence, and corresponds to the length identification of the ciphertexts;
combining the ciphertext data sets according to the encryption sequence length identification to generate a plurality of encryption ciphertext combined sequences;
Optimizing the plurality of encrypted ciphertext combining sequences based on the encryption sequence fitness function and the ciphertext security coefficient set to obtain the encryption key sequence and the decryption key sequence;
Constructing a sequence similarity evaluation function:
，
Wherein, Representing the similarity coefficient of the k+m encrypted ciphertext combination sequence and the k+l encrypted ciphertext combination sequence,/>Characterizing the same ciphertext quantity of the k+mth and k+l encrypted ciphertext combining sequences,/>Representing the number of ciphertexts with the same order and the same order of the k+m encrypted ciphertext combination sequence and the k+l encrypted ciphertext combination sequence,/>For the first weight,/>For the second weight,/>Characterization of encryption sequence Length,/>Characterization of the k+mth encrypted ciphertext combination sequence,/>Characterizing the k+l encrypted ciphertext combination sequence;
Setting a sequence similarity coefficient threshold value, wherein the sequence similarity coefficient of any two encrypted ciphertext combination sequences of the plurality of encrypted ciphertext combination sequences is smaller than the sequence similarity coefficient threshold value; when the certificate type, the certificate number and the first certificate owner are transmitted to the service cloud through the encryption communication channel, an information capturing node embedded in the service cloud is activated, data acquisition is carried out according to the certificate type and the certificate number, and certificate standard information is obtained, wherein the certificate standard information comprises a second certificate owner and a certificate valid period;
performing certificate verification according to the certificate validity period, the second certificate owner and the first certificate owner to obtain a certificate verification result;
And transmitting the certificate verification result to the user terminal through the encrypted communication channel.
2. The method of claim 1, wherein when a user side receives an image of a book to be verified, activating a semantic analysis node embedded in the user side to obtain basic information of the book to be verified, wherein the basic information of the book to be verified includes a certificate type, a certificate number and a first certificate owner, and the method comprises:
the book image to be verified at least comprises a certificate front image and a certificate back image;
The user side interacts with the user to obtain the certificate type, and verification backtracking is performed based on a local database to obtain verification history data;
When the data volume of the verification history data is equal to 0, activating a character recognition channel of the semantic analysis node, and performing semantic analysis on the certificate front image and the certificate back image to obtain the certificate number and the identity information of the first certificate owner;
activating a portrait segmentation node of the semantic analysis node, and performing portrait edge cutting on the front image of the certificate to obtain first certificate portrait information;
and adding the first certificate owner identity information and the first certificate portrait information into the first certificate owner.
3. A credential verification optimization system based on big data information security, the system comprising:
The system comprises a to-be-verified book basic information acquisition module, a first certificate identification module and a second certificate identification module, wherein the to-be-verified book basic information acquisition module is used for activating a semantic analysis node embedded in a user side to acquire to-be-verified book basic information when the user side receives an to-be-verified book image, and the to-be-verified book basic information comprises a certificate type, a certificate number and a first certificate owner;
The encryption communication channel construction module is used for carrying out local verification according to the certificate type and the certificate number, and when the local verification passes, the encryption communication channel of the user side and the service cloud is constructed based on a dynamic variation algorithm;
The certificate standard information acquisition module is used for activating an information capture node embedded in the service cloud when the certificate type, the certificate number and the first certificate owner are transmitted to the service cloud through the encrypted communication channel, and acquiring data according to the certificate type and the certificate number to obtain certificate standard information, wherein the certificate standard information comprises a second certificate owner and a certificate valid period;
The certificate verification result acquisition module is used for performing certificate verification according to the certificate validity period, the second certificate owner and the first certificate owner to acquire a certificate verification result;
the result transmission module is used for transmitting the certificate verification result to the user side through the encrypted communication channel;
Further, the system comprises:
The encryption communication channel construction module is used for backtracking based on a local database according to the certificate type and the certificate number to obtain a certificate repeated identifier, wherein the certificate repeated identifier comprises a non-repeated on-duty certificate or a repeated on-duty certificate; when the repeated identification of the certificate is the repeated-off-duty-free certificate, the local verification is passed, a ciphertext database is activated, and an encryption sequence optimization space is constructed; based on the dynamic variation algorithm, optimizing in the encryption sequence optimization space to obtain an encryption key sequence and a decryption key sequence; the decryption key sequence is sent to the service cloud through a short message operator, and the encryption communication channel is constructed based on the encryption key sequence;
Further, the system comprises:
The encryption key sequence and decryption key sequence acquisition module is used for configuring the encryption sequence optimization space in an offline optimization computing node and extracting a ciphertext data set, wherein any ciphertext of the ciphertext data set is provided with an encryption key and a decryption key; traversing the ciphertext data set to perform security coefficient identification to generate a ciphertext security coefficient set; based on the encryption sequence length identification, carrying out combined optimization on the ciphertext security coefficient set to obtain the encryption key sequence and the decryption key sequence;
Further, the system comprises:
the safety coefficient set acquisition module is used for constructing a safety coefficient evaluation function: ; wherein/> Representing the safety coefficient of any ciphertext,/>Characterizing any ciphertext,/>Characterizing ciphertext A slave/>~/>Frequency of choice within time interval,/>Characterizing the service duration of ciphertext A,/>As a function of the reduction of the length of service,/>A is a scaling degree adjusting parameter of the service duration; traversing the ciphertext data set to perform safety coefficient identification according to the safety coefficient evaluation function, and generating the ciphertext safety coefficient set;
Further, the system comprises:
the encryption key sequence and decryption key sequence acquisition module is used for constructing an encryption sequence fitness function: ,/> Wherein/> Characterizing the fitness of a kth encrypted ciphertext combining sequence,/>Characterizing the kth encrypted ciphertext combination sequence,Ciphertext security coefficient characterizing the ith order ciphertext of a kth encrypted ciphertext combination sequence,/>Ciphertext of the ith order characterizing the kth encrypted ciphertext combining sequence/>~/>The time interval is deployed to the frequency of the ith sequence, N is the total number of ciphertexts of the kth ciphertexts combined sequence, and corresponds to the length identification of the ciphertexts; combining the ciphertext data sets according to the encryption sequence length identification to generate a plurality of encryption ciphertext combined sequences; optimizing the plurality of encrypted ciphertext combining sequences based on the encryption sequence fitness function and the ciphertext security coefficient set to obtain the encryption key sequence and the decryption key sequence;
Further, the system comprises:
the similarity evaluation module is used for constructing a sequence similarity evaluation function: Wherein, the method comprises the steps of, wherein, Representing the similarity coefficient of the k+m encrypted ciphertext combination sequence and the k+l encrypted ciphertext combination sequence,/>Characterizing the same ciphertext quantity of the k+mth and k+l encrypted ciphertext combining sequences,/>Representing the number of ciphertexts with the same order and the same order of the k+m encrypted ciphertext combination sequence and the k+l encrypted ciphertext combination sequence,/>For the first weight,/>For the second weight,/>Characterization of encryption sequence Length,/>Characterization of the k+mth encrypted ciphertext combination sequence,/>Characterizing the k+l encrypted ciphertext combination sequence; and setting a sequence similarity coefficient threshold value, wherein the sequence similarity coefficient of any two encrypted ciphertext combination sequences of the plurality of encrypted ciphertext combination sequences is smaller than the sequence similarity coefficient threshold value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410117643.8A CN117688620B (en) | 2024-01-29 | 2024-01-29 | Certificate verification optimization method and system based on big data information security |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410117643.8A CN117688620B (en) | 2024-01-29 | 2024-01-29 | Certificate verification optimization method and system based on big data information security |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117688620A CN117688620A (en) | 2024-03-12 |
| CN117688620B true CN117688620B (en) | 2024-04-23 |
Family
ID=90135505
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410117643.8A Active CN117688620B (en) | 2024-01-29 | 2024-01-29 | Certificate verification optimization method and system based on big data information security |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117688620B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103078858A (en) * | 2012-12-31 | 2013-05-01 | 上海同岩土木工程科技有限公司 | Web service and signature certificate-based software trial authorization method |
| CN110708170A (en) * | 2019-12-13 | 2020-01-17 | 腾讯科技（深圳）有限公司 | Data processing method and device and computer readable storage medium |
| CN110912693A (en) * | 2019-11-22 | 2020-03-24 | 福建金密网络安全测评技术有限公司 | Digital certificate format compliance detection system |
| CN115942262A (en) * | 2022-11-30 | 2023-04-07 | 厦门讯美网络科技有限公司 | Short message service system with data security mechanism and method thereof |
| CN117081736A (en) * | 2023-08-23 | 2023-11-17 | 支付宝(杭州)信息技术有限公司 | Key distribution method, key distribution device, communication method, and communication device |
| CN117195972A (en) * | 2023-09-12 | 2023-12-08 | 中国电子科技集团公司第三十研究所 | Ciphertext neural network construction method and system based on CKS |
-
2024
- 2024-01-29 CN CN202410117643.8A patent/CN117688620B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103078858A (en) * | 2012-12-31 | 2013-05-01 | 上海同岩土木工程科技有限公司 | Web service and signature certificate-based software trial authorization method |
| CN110912693A (en) * | 2019-11-22 | 2020-03-24 | 福建金密网络安全测评技术有限公司 | Digital certificate format compliance detection system |
| CN110708170A (en) * | 2019-12-13 | 2020-01-17 | 腾讯科技（深圳）有限公司 | Data processing method and device and computer readable storage medium |
| CN115942262A (en) * | 2022-11-30 | 2023-04-07 | 厦门讯美网络科技有限公司 | Short message service system with data security mechanism and method thereof |
| CN117081736A (en) * | 2023-08-23 | 2023-11-17 | 支付宝(杭州)信息技术有限公司 | Key distribution method, key distribution device, communication method, and communication device |
| CN117195972A (en) * | 2023-09-12 | 2023-12-08 | 中国电子科技集团公司第三十研究所 | Ciphertext neural network construction method and system based on CKS |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117688620A (en) | 2024-03-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10848318B2 (en) | System for authenticating certificate based on blockchain network, and method for authenticating certificate based on blockchain network by using same | |
| CN107749848B (en) | Internet of things data processing method and device and Internet of things system | |
| RU2343639C2 (en) | Highly reliable biometric device | |
| CN108737326B (en) | Method, system, device and electronic equipment for token verification | |
| US9485098B1 (en) | System and method of user authentication using digital signatures | |
| US9940446B2 (en) | Anti-piracy protection for software | |
| CN101163009A (en) | System, server, terminal and tamper resistant device for authenticating a user | |
| CN111800378B (en) | Login authentication method, device, system and storage medium | |
| CN108701308B (en) | System for issuing public certificate based on blockchain, and method for issuing public certificate based on blockchain using same | |
| Nagaraju et al. | SecAuthn: Provably secure multi-factor authentication for the cloud computing systems | |
| CN112329519A (en) | Safe online fingerprint matching method | |
| CN113676348A (en) | Network channel cracking method, device, server and storage medium | |
| CN104639528A (en) | DBA (database administrator) mobile client counterattack method and DBA mobile client counterattack device | |
| CN104881595B (en) | The self-help remote unlocking method managed based on PIN code | |
| WO2022048589A1 (en) | Data acquisition method, apparatus and device, and medium | |
| CN110572392A (en) | Identity authentication method based on HyperLegger network | |
| CN112383401B (en) | User name generation method and system for providing identity authentication service | |
| CN113849797A (en) | Method, device, equipment and storage medium for repairing data security vulnerability | |
| KR101856530B1 (en) | Encryption system providing user cognition-based encryption protocol and method for processing on-line settlement, security apparatus and transaction approval server using thereof | |
| CN117688620B (en) | Certificate verification optimization method and system based on big data information security | |
| WO2021107755A1 (en) | A system and method for digital identity data change between proof of possession to proof of identity | |
| CN114944921A (en) | Login authentication method and device, electronic equipment and storage medium | |
| CN113657322B (en) | Face recognition method and system based on public key ciphertext comparison | |
| CN116866093B (en) | Identity authentication method, identity authentication device, and readable storage medium | |
| CN114466358B (en) | User identity continuous authentication method and device based on zero trust |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |