Account authentication method and device, storage medium and electronic device

Abstract

The application discloses an account authentication method and device, a storage medium and an electronic device, and relates to the technical field of data processing. The method comprises the following steps: first, obtaining user information and user signaling of a login user; then, generating an anonymous identifier of the user information corresponding to the login user based on the user information; then, extracting an anonymous user information entry corresponding to the anonymous identifier in a blockchain system; finally, determining an account authentication result of the login user according to the anonymous user information entry and the user signaling. According to the application, the anonymous identifier can be generated according to the user information of the login user, and then the anonymous user information entry can be extracted by using the anonymous identifier. The user signaling of the current login user is verified according to the user information stored in the blockchain system, and the account authentication result of the login user is determined, so that the account borrowing and stealing situation can be found in time, the detection capability of the system for irregular behaviors and stealing attack behaviors is improved, and the account authentication accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to an account authentication method, apparatus, storage medium and electronic device. Background Technology

[0002] In recent years, cybersecurity and data security have received increasing attention from society. Account authentication methods can be applied to verify whether a user's identity is legitimate and whether they have permission to access specific resources, thereby improving cybersecurity.

[0003] Currently, the relevant technologies mainly conduct business audits and security audits on account operation records from the perspective of business rules, thereby discovering operations that do not comply with business rules and performing account authentication.

[0004] However, when relevant technologies conduct business audits and security audits based on business rules, if the account operation records do not violate the relevant rules, it is impossible to determine whether the account has been stolen or borrowed, and the accuracy of the account authentication results cannot be guaranteed. Summary of the Invention

[0005] In view of this, this application provides an account authentication method, device, storage medium, and electronic device. The main purpose is to improve the technical problem that when current related technologies conduct business audits and security audits based on business rules, if the account operation records do not violate the relevant rules, it is impossible to determine whether the account has been stolen or borrowed, and the accuracy of the account authentication results cannot be guaranteed.

[0006] Firstly, this application provides an account authentication method, including:

[0007] Obtain user information and user signaling from logged-in users;

[0008] Based on the user information, an anonymous identifier for the user information corresponding to the logged-in user is generated;

[0009] Extract the anonymous user information entries corresponding to the anonymous identifier in the blockchain system;

[0010] Based on the anonymous user information entry and the user signaling, the account authentication result of the logged-in user is determined.

[0011] Secondly, this application provides an account authentication device, including:

[0012] The acquisition module is configured to acquire user information and user signaling of logged-in users;

[0013] The generation module is configured to generate an anonymous identifier for the user information corresponding to the logged-in user based on the user information.

[0014] The acquisition module is configured to extract the anonymous user information entries corresponding to the anonymous identifier in the blockchain system;

[0015] The determination module is configured to determine the account authentication result of the logged-in user based on the anonymous user information entry and the user signaling.

[0016] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the account authentication method described in the first aspect.

[0017] Fourthly, this application provides an electronic device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the computer program to implement the account authentication method described in the first aspect.

[0018] Fifthly, this application provides a computer program product having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the account authentication method described in the first aspect.

[0019] By employing the above technical solution, this application provides an account authentication method, apparatus, storage medium, and electronic device. First, it acquires the user information and user signaling of the logged-in user. Then, based on the user information, it generates an anonymous identifier corresponding to the logged-in user's information. Next, it extracts the anonymous user information entry corresponding to the anonymous identifier in the blockchain system. Finally, based on the anonymous user information entry and the user signaling, it determines the account authentication result of the logged-in user. Compared with existing technologies, this application can generate an anonymous identifier based on the logged-in user's user information, then extract an anonymous user information entry using the anonymous identifier, and verify the user signaling of the currently logged-in user based on the user information stored in the blockchain system to determine the account authentication result. This allows for timely detection of account theft and misuse, improves the system's ability to detect violations and theft attacks, enhances the accuracy of account authentication, and ultimately improves network security.

[0020] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating an account authentication method provided in an embodiment of this application is shown;

[0024] Figure 2 A flowchart illustrating an account authentication method provided in an embodiment of this application is shown;

[0025] Figure 3 A flowchart illustrating an example provided in an embodiment of this application is shown;

[0026] Figure 4 A flowchart illustrating an example provided in an embodiment of this application is shown;

[0027] Figure 5 A flowchart illustrating an example provided in an embodiment of this application is shown;

[0028] Figure 6 A schematic diagram of the structure of an account authentication device provided in an embodiment of this application is shown. Detailed Implementation

[0029] The embodiments of this application will now be described in more detail with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0030] To address the technical issue that current technologies, when conducting business audits and security audits based on business rules, cannot determine whether an account has been stolen or borrowed if the account operation records do not violate relevant rules, thus compromising the accuracy of account authentication results, this embodiment provides an account authentication method, such as... Figure 1 As shown, the method includes:

[0031] Step 101: Obtain the user information and user signaling of the logged-in user.

[0032] In this embodiment, user information of the user currently attempting to log in to the operator's system can be obtained through legitimate means, such as login account, mobile phone number, and user signaling corresponding to the user information, such as user status, attached base station status, and service acceptance status.

[0033] Step 102: Based on user information, generate an anonymous identifier for the user information corresponding to the logged-in user.

[0034] For example, an anonymous identifier generation algorithm can be used to convert the mobile phone number in the user information into an anonymous identifier. The anonymous identifier can be used to anonymize the user information. The anonymous identifier can be numbers, letters, etc. In this way, the real user information will no longer appear in the user signaling information. The user information cannot be deduced from the anonymous identifier, thereby protecting user privacy, reducing the risk of account theft and misuse, improving network security, and improving the efficiency of finding user information, thereby improving the efficiency of account authentication.

[0035] Step 103: Extract the anonymous user information entries corresponding to the anonymous identifiers in the blockchain system.

[0036] For example, legitimate user signaling from all legitimate users can be collected in real time, and anonymous identifiers and anonymous user information entries can be generated for legitimate users. The anonymous user information entries are then stored in the blockchain system according to the anonymous identifiers. When a user attempts to log in, the anonymous identifier of the logged-in user can be obtained, and the corresponding anonymous user information entry in the blockchain system can be extracted based on the anonymous identifier. If the currently logged-in user is a legitimate user, the information corresponding to the logged-in user's signaling should be consistent with the information corresponding to the anonymous user information entry. This method of extracting anonymous user information entries through anonymous identifiers can verify the user signaling of the currently logged-in user based on the user information stored in the blockchain system, which helps to protect system security and improve the accuracy of account authentication.

[0037] Step 104: Determine the account authentication result of the logged-in user based on the anonymous user information entries and user signaling.

[0038] For example, account borrowing and theft authentication can be performed by comparing the information in the anonymous user information entries with the information corresponding to the current user signaling. This determines whether the currently logged-in user has authentication risks. Based on the judgment result, the logged-in user is authenticated in real time. If the currently logged-in user has authentication risks, the user may have engaged in illegal or theft activities, indicating that account borrowing and theft have occurred. In this way, the authentication result can be determined as authentication failure. In this way, the blockchain system can promptly detect account borrowing and theft, improve the system's ability to detect illegal and theft activities, and enhance network security.

[0039] Compared with existing technologies, this embodiment can generate an anonymous identifier based on the user information of the logged-in user, then use the anonymous identifier to extract anonymous user information entries, and verify the user signaling of the currently logged-in user based on the user information stored in the blockchain system to determine the account authentication result of the logged-in user. This allows for the timely detection of account borrowing and theft, improves the system's ability to detect violations and theft attacks, enhances the accuracy of account authentication, and ultimately improves network security.

[0040] To further illustrate the specific implementation process of the method in this embodiment, this embodiment provides the following: Figure 2 The specific method shown includes:

[0041] Step 201: Obtain the user information and user signaling of the logged-in user.

[0042] For example, when a logged-in user attempts to access the operator's system via a client (computer, mobile phone), the user's information and signaling can be recorded by verifying the SMS verification code of the mobile phone number bound to the logged-in user. The user information may include, but is not limited to, account, mobile phone number, etc., and the signaling may include, but is not limited to, operation behavior, regional information, and characteristic value information (such as Internet Protocol Address (IP) and Media Access Control Address (MAC)).

[0043] Step 202: Based on user information, generate an anonymous identifier for the user information corresponding to the logged-in user.

[0044] In this embodiment, a preset algorithm can be used to process the mobile phone number in the user information to generate an irregular anonymous identifier. The anonymous user information entries may include, but are not limited to, anonymous user information identifiers, historical activity areas, historical feature values, population attributes (such as high-risk groups), usage frequency, borrowing frequency, historical trajectory information (activity time period, activity area), etc.

[0045] Step 203: Obtain the legitimate user information and legitimate user signaling of the legitimate login user.

[0046] In this embodiment, to determine whether the currently logged-in user is a legitimate user, the legitimate user information and legitimate user signaling of the legitimate logged-in user can be obtained based on the operator's unified authentication service. The legitimate user information may include, but is not limited to, the legitimate logged-in user's account and mobile phone number, etc., and the legitimate user signaling may include, but is not limited to, the legitimate logged-in user's user status, attached base station status, service acceptance status, etc. The legitimate logged-in user can be a user whose account authentication has been passed and who has not been borrowed or stolen.

[0047] Step 204: Generate the target anonymity identifier corresponding to the legitimate user information based on the circuit anonymity algorithm of the user information.

[0048] In this embodiment, a circuit anonymization algorithm (user information XOR circuit anonymization algorithm) can be used to generate a target anonymization identifier (user information anonymization XOR ID (Identifier, ID)) for each legitimate login user. By anonymizing user information, user privacy can be protected, while improving the efficiency of finding relevant information of legitimate login users. The target anonymization identifier can be a unique anonymized identifier for legitimate login users.

[0049] Optionally, step 204 may include: performing an XOR operation on the legitimate user information and the system random number to obtain a random number; selecting a randomly generated point in a preset hyperbola based on the random number, and using the ordinate value of the randomly generated point as the target anonymous identifier.

[0050] Specifically, such as Figure 3 As shown, user information (INFO) can be processed using the SHA256 algorithm to generate 256-bit data A, and a random system number M can be generated. Then, data A and the random system number M are XORed to obtain a random number C. A random point N is then selected on the BLS-381 hyperbola G1, where N = G0 XC, and G0 is the generation point of hyperbola G1. The y-coordinate (value) of the random point N in the hyperbola G1 coordinate system is used as the target anonymous identifier (user information anonymous XOR ID). In this way, the target anonymous identifier is randomized, reducing the risk of account theft and improving user information security.

[0051] Step 205: Convert legitimate user signaling into an anonymous signaling set.

[0052] Optionally, step 205 may specifically include: anonymizing legitimate user signaling to generate an anonymous signaling set.

[0053] For example, after generating a target anonymous identifier for a legitimate login user, the collected legitimate user signaling can be stored in the blockchain system according to the target anonymous identifier. All signaling information of the legitimate login user can be anonymized to generate an anonymous signaling set, so as to protect user information and reduce the risk of account misuse.

[0054] Step 206: Organize the anonymous signaling set to generate anonymous user information entries corresponding to the target anonymous identifier.

[0055] Optionally, step 206 may specifically include: using an anonymous signaling analysis algorithm to perform iterative analysis and calculation on the anonymous signaling set to generate anonymous user information entries corresponding to the target anonymous identifier.

[0056] Anonymous signaling analysis algorithms may include, but are not limited to, eigenvalue analysis algorithms, customer traffic analysis algorithms, crowd distribution analysis algorithms, and migration analysis algorithms. The specific calculation process is as follows:

[0057] a) Eigenvalue analysis algorithm:

[0058] a1) Use the unique identifier of the mobile phone user as the feature value:

[0059] The system collects all users' mobile phone IMSN signaling in real time through legal means, obtains the anonymous XOR ID of user information based on the IMSN signaling, and obtains each user's anonymous XOR ID, data usage information, package information, call time information, contact information, and call topic information through the operator's unified authentication network.

[0060] a2) Using workplace and residence as feature values:

[0061] By legally collecting all users' mobile phone Work_Base and Live_Base signaling in real time, the geographical location information of the workplace corresponding to the anonymous XOR ID of the user information is determined based on the Work_Base signaling. The industry to which the user belongs is determined by the workplace registration database and map database, and the user's population information (region, industry) is marked according to the place of residence, providing regional population filtering services, regional population attribute services, and population density early warning services.

[0062] a3) Use the number of days the workplace appears as a characteristic value:

[0063] We collect all users' mobile phone ON-WSD signaling in real time through legal means. If a user's ON-WSD signaling is less than 60 days old, it means that the user has a high degree of job mobility and unstable employment. We then use the ON-WSD signaling to identify the user's demographic attributes (job stability).

[0064] a4) Use the place of household registration and the place of work as characteristic values:

[0065] By legally collecting all users' mobile phone Work_Base signaling and Cen_Reg signaling in real time, we determine whether the user is a migrant population. The migrant population is divided into high-frequency migrant population (changing work location more than 3 times a year), medium-frequency migrant population (changing work location 2 to 3 times a year), and low-frequency migrant population (changing work location less than 1 time a year). Population attributes (population mobility) are identified based on Work_Base signaling and Cen_Reg signaling.

[0066] Using the above four sets of feature value algorithms, we can obtain the user's anonymous XOR ID, traffic information, plan information, call time information, contact information, call topic information, and user demographic attributes (region, industry, job stability, population mobility). Based on the Work_Base signaling, we can provide the user's historical trajectory information (time period A, region B).

[0067] b) Customer traffic analysis algorithm:

[0068] Based on the eigenvalue analysis algorithm, the traffic information of anonymous XOR IDs of user information is analyzed. The traffic types are divided into (news, games, videos, documents, shopping, industry information, and frequently used websites). This can determine the main traffic types of anonymous XOR IDs of user information and identify the user attributes (main interests and business directions).

[0069] c) Population distribution analysis algorithm:

[0070] By collecting users' mobile phone CITY signaling in real time through legal means, we can identify users within each CITY. We then analyze the demographic attributes (region, industry, job stability, population mobility, main interests, and business focus) of anonymously XORed IDs of user information within the CITY.

[0071] d) Transfer analysis algorithm:

[0072] By legally collecting mobile phone CITY signaling and mobile phone ON-WSD signaling from all users in real time, if the CITY value of the anonymous XOR ID of user information changes to a new value and remains unchanged for more than 30 days, it can be considered as a migration. Statistical calculations are performed according to the code database of all cities nationwide to provide services for entering and leaving the region, the source of the regional population, and the destination of the regional population.

[0073] In this embodiment, anonymous signaling data is analyzed by an anonymous signaling analysis algorithm. Based on the historical trajectory data of the anonymous XOR ID of user information ([time period 1, region 1], [time period 2, region 2], ..., [time period M, region M]), the historical feature values ​​of the anonymous XOR ID of user information (phone model, package information, data usage information, call duration, number of contacts, call topic, etc.), and the population to which the anonymous XOR ID of user information belongs (population 1, population 2, ..., population n), the system summarizes them into anonymous user information entries.

[0074] Step 207: Store the anonymous user information entry corresponding to the target anonymous identifier in the blockchain system.

[0075] In this embodiment, the anonymous user information entries corresponding to the target anonymous identifier can be updated in real time and stored in the blockchain system to protect user information and improve the efficiency and accuracy of account authentication.

[0076] Step 208: Extract the anonymous user information entries corresponding to the anonymous identifiers in the blockchain system.

[0077] In this embodiment, the anonymous identifier is compared with the target anonymous identifier in the blockchain system. If the anonymous identifier matches the target anonymous identifier, the anonymous user information entry corresponding to the target anonymous identifier in the blockchain system can be extracted. If the anonymous identifier does not match the target anonymous identifier, the currently logged-in user may have abnormal behavior, and it can be determined that the current logged-in user has failed authentication and is not allowed to log in.

[0078] Step 209: Determine the account authentication result of the logged-in user based on the anonymous user information entries and user signaling.

[0079] Optionally, step 209 may include: determining whether there is an authentication risk for the logged-in user by comparing the user signaling with the anonymous user information entry; if there is an authentication risk for the logged-in user, then determining that the login user's authentication fails; if there is no authentication risk for the logged-in user, then determining that the login user's authentication passes.

[0080] Optionally, by comparing user signaling with anonymous user information entries, it can be determined whether the logged-in user faces authentication risks, including: determining whether the logged-in user's operation behavior is normal; and / or determining whether the logged-in user's regional information is within the range of location changes in the historical activity area; and / or determining whether the logged-in user's characteristic value information is in the historical characteristic values; and / or determining whether the logged-in user's demographic attributes are high-risk groups; and / or determining whether the logged-in user's usage frequency is higher than a preset usage frequency threshold; and / or determining whether the logged-in user's borrowing frequency is higher than a preset borrowing frequency threshold.

[0081] For example, such as Figure 4 As shown, an account borrowing and theft authentication algorithm based on anonymous XOR ID of user information is illustrated. When performing real-time account authentication based on blockchain, an anonymous identifier (anonymous XOR ID of user information) is generated based on the user information of the currently logged-in user. The target anonymous identifier that matches the anonymous identifier is found in the blockchain system, and all information entries corresponding to the target anonymous identifier are extracted. By comparing the user signaling of the currently logged-in user with the on-chain information, account borrowing and theft authentication is performed. Based on the authentication result, it is determined whether the logged-in user is allowed to log in.

[0082] Specifically, the operator's security interface service can be called to monitor the borrowing and theft of the current user's bound mobile phone number. Then, it can be determined one by one whether the logged-in user's account operation behavior is normal, and / or whether the current logged-in user's regional information is within the range of changes in the historical activity area of ​​the logged-in user, and / or whether the current logged-in user's corresponding population attribute in the blockchain is a high-risk group (such as a person with a bad credit record, a person with a criminal record, or a foreign national), and / or whether the current logged-in user's IP and MAC information are in the historical characteristic values ​​of the logged-in user, and / or whether the current logged-in user's usage frequency is higher than the preset usage frequency threshold (M times / month), and / or whether the user's borrowing frequency is higher than the preset borrowing frequency threshold (N times / month).

[0083] If a logged-in user's account operation behavior is abnormal, and / or the current logged-in user's regional information is not within the range of changes in the user's historical activity region, and / or the current logged-in user's corresponding population attribute in the blockchain is a high-risk group, and / or the current logged-in user's IP and MAC information are not in the user's historical characteristic values, and / or the current logged-in user's usage frequency is higher than the preset usage frequency threshold, and / or the user's borrowing frequency is higher than the preset borrowing frequency threshold, then it can be determined that the logged-in user is suspected of theft, and authentication (signature) fails.

[0084] If the logged-in user's account operation behavior is normal, the current logged-in user's regional information is within the range of changes in the user's historical activity region, the current logged-in user's corresponding population attribute in the blockchain is not a high-risk group, the current logged-in user's IP and MAC information are within the user's historical characteristic values, the current logged-in user's usage frequency is not higher than the preset usage frequency threshold, and the user's borrowing frequency is not higher than the set preset borrowing frequency threshold, then the logged-in user can be determined to be a legitimate logged-in user, and the authorization signature is approved.

[0085] For example, when a user attempts to log in or perform a security audit on their account, the operator's security interface service is invoked. Based on the signaling status of the anchored login user, the parameters of the invoked interface are compared with the signaling status associated with the parameters at the current or specified time to identify the risk of account theft or misuse during login and return the risk information. If the operator's security interface service returns information indicating account theft, authentication fails. For account theft, measures such as alerting security personnel for handling, setting system settings to refuse login, and cutting off login network paths can be used to control the risk. For account theft, the administrator can be alerted for manual intervention to monitor the theft or misuse of login users.

[0086] Optionally, after determining that the login user's authentication fails, the process may also include: increasing the authentication risk value of the login user and recording the account authentication result in the blockchain system.

[0087] Specifically, when a user attempts to log in to the operator's network, the system can monitor and evaluate the user based on on-chain information and perform real-time authentication. If the user's usage frequency exceeds a preset usage frequency threshold, and the authentication fails, the authentication risk value of the user will be increased by 1. When the user is re-authenticated and the authentication is successful, the authentication risk value of the user can be reduced. The system improves its rules for automatically identifying account theft through self-learning, so that account risk information can be returned in a timely manner during security audits. Based on different authentication risk values, technical means such as refusing login or issuing warnings can be adopted to enhance system security. Automatic module authorization is used to minimize the authorization effect, achieve accurate identification and monitoring of account theft, improve system security, and ensure the accuracy and authenticity of operator network identification.

[0088] For example, such as Figure 5 The diagram illustrates the architecture of an account borrowing and theft monitoring and blockchain-based real-time authentication system. It includes a user login module, a signaling anonymization module for legitimate users, an anonymous user information blockchain module, and an account borrowing and theft authentication module. The user login module obtains user information of the user currently attempting to log into the operator's network via SMS verification. The signaling anonymization module converts the signaling data of the legitimate user into anonymous signaling and generates anonymous user information entries for each user, storing them on the blockchain. The anonymous user information blockchain module writes these entries to the blockchain, allowing access to all on-chain information about the user by querying their mobile phone number. The account borrowing and theft authentication module retrieves user information from the user login module, obtains all information entries related to the anonymous XOR ID of the user information on the blockchain, and performs authentication based on an account borrowing and theft authentication algorithm using the anonymous XOR ID of the user information.

[0089] Compared with existing technologies, this embodiment can use anonymous signaling analysis algorithms to monitor and analyze the borrowing and theft of logged-in users, and record the analysis results on the blockchain. Based on the authentication risk value of logged-in users, corresponding technical means are adopted to enhance system security and ensure the accuracy and authenticity of operator network identification.

[0090] Furthermore, as Figure 1 and Figure 2 The specific implementation of the method shown in this embodiment provides an account authentication device, such as... Figure 6 As shown, the device includes: an acquisition module 31, a generation module 32, and a determination module 33.

[0091] Module 31 is configured to acquire user information and user signaling of logged-in users;

[0092] The generation module 32 is configured to generate an anonymous identifier for the user information corresponding to the logged-in user based on the user information.

[0093] Module 31 is configured to extract anonymous user information entries corresponding to anonymous identifiers in the blockchain system.

[0094] Module 33 is configured to determine the account authentication result of the logged-in user based on the anonymous user information entry and user signaling.

[0095] In some examples of this embodiment, the acquisition module 31 is further configured to acquire the legitimate user information and legitimate user signaling of the legitimate logged-in user; generate a target anonymous identifier corresponding to the legitimate user information according to the circuit anonymization algorithm of the user information; convert the legitimate user signaling into an anonymous signaling set; organize the anonymous signaling set to generate an anonymous user information entry corresponding to the target anonymous identifier; and store the anonymous user information entry corresponding to the target anonymous identifier in the blockchain system.

[0096] In some examples of this embodiment, the generation module 32 is specifically configured to perform an XOR operation on the legitimate user information and the system random number to obtain a random number; select a random point in a preset hyperbola based on the random number, and use the ordinate value of the random point as the target anonymity identifier.

[0097] In some examples of this embodiment, the acquisition module 31 is specifically configured to anonymize legitimate user signaling and generate an anonymous signaling set.

[0098] In some examples of this embodiment, the generation module 32 is specifically configured to perform iterative analysis and calculation on the anonymous signaling set using an anonymous signaling analysis algorithm to generate anonymous user information entries corresponding to the target anonymous identifier.

[0099] In some examples of this embodiment, the determining module 33 is specifically configured to determine whether there is an authentication risk for the logged-in user by comparing the user signaling with the anonymous user information entry; if there is an authentication risk for the logged-in user, then the authentication of the logged-in user is determined to fail; if there is no authentication risk for the logged-in user, then the authentication of the logged-in user is determined to pass.

[0100] In some examples of this embodiment, user signaling includes operational behavior, and / or regional information, and / or feature value information; anonymous user information entries include user information anonymity identifier, and / or historical activity area, and / or historical feature value, and / or population attribute, and / or usage frequency, and / or borrowing frequency; the determination module 33 is specifically configured to determine whether the logged-in user's operational behavior is normal; and / or determine whether the logged-in user's regional information is within the range of location changes in the historical activity area; and / or determine whether the logged-in user's feature value information is in the historical feature values; and / or determine whether the logged-in user's population attribute is a high-risk group; and / or determine whether the logged-in user's usage frequency is higher than a preset usage frequency threshold; and / or determine whether the logged-in user's borrowing frequency is higher than a preset borrowing frequency threshold.

[0101] In some examples of this embodiment, the determining module 33 is further configured to increase the authentication risk value of the logged-in user and record the account authentication result in the blockchain system.

[0102] It should be noted that other corresponding descriptions of the functional units involved in the account authentication device provided in this embodiment can be found in [reference needed]. Figure 1 and Figure 2 The corresponding description in [the document] will not be repeated here.

[0103] Based on the above, Figure 1 and Figure 2 Accordingly, this embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. Figure 1 and Figure 2 The method shown.

[0104] Based on the above, Figure 1 and Figure 2 Accordingly, this embodiment also provides a computer program product having a computer program stored thereon, which, when executed by a processor, implements the above-described method. Figure 1 and Figure 2 The method shown.

[0105] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0106] Based on the above, Figure 1 and Figure 2 The method shown, and Figure 6To achieve the above objectives, this application also provides an electronic device, such as a personal computer, server, laptop, smartphone, intelligent robot, or other intelligent terminal, as illustrated in the virtual device embodiment. This device includes a storage medium and a processor; the storage medium stores a computer program; and the processor executes the computer program to implement the above-described virtual device. Figure 1 and Figure 2 The method shown.

[0107] Optionally, the aforementioned physical devices may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0108] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0109] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0110] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented using software plus necessary general-purpose hardware platforms, or it can be implemented using hardware. By applying the technical solution of this embodiment, this application can generate an anonymous identifier based on the user information of the logged-in user, then use the anonymous identifier to extract anonymous user information entries, and verify the user signaling of the currently logged-in user based on the user information stored in the blockchain system to determine the account authentication result of the logged-in user. This allows for the timely detection of account borrowing and theft, improves the system's ability to detect violations and theft attacks, enhances the accuracy of account authentication, and thus improves network security.

[0111] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0112] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. An account authentication method, characterized in that, include: Obtain user information and user signaling from logged-in users; Based on the user information, an anonymous identifier for the user information corresponding to the logged-in user is generated; Extract the anonymous user information entries corresponding to the anonymous identifier in the blockchain system; Based on the anonymous user information entries and the user signaling, the account authentication result of the logged-in user is determined; The method further includes, before extracting the anonymous user information entry corresponding to the anonymous identifier in the blockchain system: Obtain legitimate user information and legitimate user signaling from legitimate login users; Based on the circuit anonymization algorithm of the user information, a target anonymization identifier corresponding to the legitimate user information is generated; Convert the legitimate user signaling into an anonymous signaling set; Based on the anonymous signaling set, an anonymous user information entry corresponding to the target anonymous identifier is generated; The anonymous user information entries corresponding to the target anonymous identifier are stored in the blockchain system.

2. The method according to claim 1, characterized in that, The step of generating the target anonymity identifier corresponding to the legitimate user information based on the user information circuit anonymity algorithm includes: The legitimate user information is XORed with a system random number to obtain a random number; Based on the random number, a randomly generated point is selected in the preset hyperbola, and the ordinate value of the randomly generated point is used as the target anonymous identifier.

3. The method according to claim 1, characterized in that, The step of converting the legitimate user signaling into an anonymous signaling set includes: The legitimate user signaling is anonymized to generate an anonymous signaling set; The step of organizing the anonymous signaling set to generate anonymous user information entries corresponding to the target anonymous identifier includes: An anonymous signaling analysis algorithm is used to perform iterative analysis and calculation on the anonymous signaling set to generate anonymous user information entries corresponding to the target anonymous identifier.

4. The method according to claim 1, characterized in that, The step of determining the account authentication result of the logged-in user based on the anonymous user information entry and the user signaling includes: By comparing the user signaling with the anonymous user information entries, it can be determined whether the logged-in user faces authentication risks. If the logged-in user has authentication risks, then the login user's authentication is determined to fail. If the logged-in user does not pose an authentication risk, then the login user's authentication is deemed successful.

5. The method according to claim 4, characterized in that, The user signaling includes operational behavior, and / or regional information, and / or feature value information; the anonymous user information entries include user information anonymity identifier, and / or historical activity area, and / or historical feature value, and / or population attribute, and / or usage frequency, and / or borrowing frequency. The step of determining whether the logged-in user faces authentication risks by comparing the user signaling with the anonymous user information entries includes: Determine whether the logged-in user's operation is normal; and / or, Determine whether the logged-in user's region information is within the range of location changes in the historical activity region; and / or, Determine whether the feature value information of the logged-in user is in the historical feature values; and / or, Determine whether the logged-in user's demographic attributes indicate a high-risk group; and / or, Determine whether the usage frequency of the logged-in user is higher than a preset usage frequency threshold; and / or, Determine whether the borrowing frequency of the logged-in user is higher than a preset borrowing frequency threshold.

6. The method according to claim 4, characterized in that, After determining that the login user authentication failed, the method further includes: Increase the authentication risk value of the logged-in user and record the account authentication result in the blockchain system.

7. An account authentication device, characterized in that, include: The acquisition module is configured to acquire user information and user signaling of logged-in users; The generation module is configured to generate an anonymous identifier for the user information corresponding to the logged-in user based on the user information. The acquisition module is configured to extract the anonymous user information entries corresponding to the anonymous identifier in the blockchain system; The determination module is configured to determine the account authentication result of the logged-in user based on the anonymous user information entry and the user signaling; Specifically, the acquisition module is further configured to, before extracting the anonymous user information entry corresponding to the anonymous identifier in the blockchain system, acquire the legitimate user information and legitimate user signaling of the legitimate logged-in user, generate the target anonymous identifier corresponding to the legitimate user information according to the circuit anonymization algorithm of the user information, convert the legitimate user signaling into an anonymous signaling set, organize the anonymous signaling set according to the anonymous signaling set, generate the anonymous user information entry corresponding to the target anonymous identifier, and store the anonymous user information entry corresponding to the target anonymous identifier in the blockchain system.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

9. An electronic device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

10. A computer program product having a computer program stored thereon, characterized in that, When the computer program product is executed by a processor, it implements the method of any one of claims 1 to 6.

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