WO2020034760A1 - 一种身份信息的识别方法及装置 - Google Patents

一种身份信息的识别方法及装置 Download PDF

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Publication number
WO2020034760A1
WO2020034760A1 PCT/CN2019/092482 CN2019092482W WO2020034760A1 WO 2020034760 A1 WO2020034760 A1 WO 2020034760A1 CN 2019092482 W CN2019092482 W CN 2019092482W WO 2020034760 A1 WO2020034760 A1 WO 2020034760A1
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WIPO (PCT)
Prior art keywords
account
information
identity information
account information
user data
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Ceased
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PCT/CN2019/092482
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English (en)
French (fr)
Inventor
陈弢
李超
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Alibaba Group Holding Ltd
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Alibaba Group Holding Ltd
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Priority to SG11202010698WA priority Critical patent/SG11202010698WA/en
Priority to EP19849124.3A priority patent/EP3780541B1/en
Publication of WO2020034760A1 publication Critical patent/WO2020034760A1/zh
Priority to US17/103,186 priority patent/US11605087B2/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
    • G06Q20/36—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using electronic wallets or electronic money safes
    • G06Q20/367—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using electronic wallets or electronic money safes involving electronic purses or money safes
    • G06Q20/3674—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using electronic wallets or electronic money safes involving electronic purses or money safes involving authentication
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4014—Identity check for transactions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
    • H04L63/0815—Network architectures or network communication protocols for network security for authentication of entities providing single-sign-on or federations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1441—Countermeasures against malicious traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1441—Countermeasures against malicious traffic
    • H04L63/1483—Countermeasures against malicious traffic service impersonation, e.g. phishing, pharming or web spoofing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/2866—Architectures; Arrangements
    • H04L67/30—Profiles
    • H04L67/306—User profiles

Definitions

  • the embodiments of the present specification relate to the field of Internet technologies, and in particular, to a method and a device for identifying identity information.
  • the identity information when identifying whether or not the identity information is used by others, it can usually be determined according to the use environment of the account. For example, if an account is authenticated on the same device as a large number of other accounts, then the identity information corresponding to that account is likely to be fraudulent. However, if the use environment of the account changes, this method will not be able to effectively identify the identity information being used. Therefore, an effective method for identifying identity information is urgently needed.
  • the purpose of the embodiments of the present specification is to provide a method and a device for identifying identity information, which can improve the identification efficiency of identity information.
  • a method for identifying identity information includes:
  • a risk value of the target identity information in the user data is determined, and based on the determined risk value, it is determined whether the target identity information is at risk of fraud.
  • a device for identifying identity information includes:
  • a data obtaining unit configured to obtain user data, where the user data includes identity information and account information of the user;
  • An association relationship establishing unit configured to establish an association relationship between the bound account information and identity information in the user data, and establish an association relationship between two account information having common characteristics in the user data;
  • An identifying unit is configured to determine a risk value of target identity information in the user data according to the established association relationship, and determine whether the target identity information is at risk of fraud based on the determined risk value.
  • An apparatus for identifying identity information includes a memory and a processor.
  • the memory is configured to store a computer program.
  • the method for identifying identity information is implemented.
  • an association relationship between identity information and account information in the user data, and account information and account information may be established.
  • the correlation between identity information and account information can be reflected in: account information is registered or authenticated based on the associated identity information; the correlation between different account information can be reflected in: two account information have common characteristics.
  • the two account information having common characteristics may include, for example, situations in which the two account information interact, the two account information are bound to the same object, and the two account information are logged in on the same device.
  • the technical solution provided by one or more embodiments of this specification analyzes the correlation between the identity information and the account information to determine whether the identity information is fraudulent. Even if a fraudulent user changes the use environment of an account, the method can identify whether the account is in an abnormal state from the presence of common features between the account and other accounts, thereby improving the identification efficiency of identity information.
  • FIG. 1 is a flowchart of a method for identifying identity information provided in this specification
  • FIG. 2 is a relationship graph corresponding to a normal user provided in this specification
  • FIG. 3 is a relationship map containing fraudulent account information provided in this specification
  • FIG. 4 is a functional module schematic diagram of the identification information identification device provided in this specification.
  • FIG. 5 is a schematic structural diagram of an identification device for identity information provided in this specification.
  • the embodiments of the present specification provide a method and a device for identifying identity information.
  • a method for identifying identity information provided by an embodiment of the present specification may include the following steps.
  • S1 Acquire user data, where the user data includes identity information and account information of the user.
  • the user data may be data to be analyzed, and the user data may be obtained from a background server of each website.
  • the user data may include account information, and the account information may be, for example, an account name registered by the user, or a digital ID of the account name in a website.
  • the account information may be, for example, an account name registered by the user, or a digital ID of the account name in a website.
  • users can fill in their own identity information together, or authenticate their identity information after registering an account.
  • the registered account information and authenticated identity information can be bound in the background server of the website.
  • the identity information may be information such as identity card information, passport information, medical insurance card information, and driver's license information that can uniquely identify the identity of the user.
  • S3 Establish an association relationship between the bound account information and identity information in the user data, and establish an association relationship between two account information having common characteristics in the user data.
  • the bound account information and identity information in the user data can signify that the account information is authenticated by the identity information. Therefore, there can be an association between the bound account information and the identity information. .
  • the interactive behavior may be behaviors such as mutual transfer, sending chat information, and co-payment / receiving the same funds.
  • the two account information may be considered to have common characteristics.
  • Binding The same object can refer to two account information binding the same shipping address, binding the same mobile phone number, binding the same email address, and so on.
  • the two account information can also be considered that the two account information have common characteristics.
  • the two account information with common characteristics can also include other more situations.
  • the above-mentioned examples in this embodiment are not exhaustive. Those skilled in the art will understand the essence of the technical solution of this embodiment. Other situations with common characteristics added under the circumstances should also belong to the protection scope of this embodiment.
  • an association relationship between the bound account information and identity information in the user data may be established, and an association relationship between two account information having common characteristics in the user data may be established.
  • the account information and identity information may be represented in the form of a relationship graph, and the association relationship between the account information and the account information. Please refer to Figure 2.
  • account information and identity information can be represented by account nodes and identity nodes, respectively, and there are two accounts that have a connection between the bound account nodes and identity nodes, and have common characteristics. There are also connections between nodes. In this way, each piece of information in the user data is characterized by a node, and after a connection is added between the associated nodes, a relationship map corresponding to the user data can be established.
  • S5 Determine the risk value of the target identity information in the user data according to the established association relationship, and determine whether there is a risk of fraud in the target identity information based on the determined risk value.
  • the established association relationship can be analyzed to obtain the difference between the normal user and the identity fraudster, and based on the analysis obtained difference To identify identity information that may be at risk of fraud.
  • the relationship spectrum diagram shown in FIG. 2 is taken as an example, and FIG. 2 is a relationship diagram of identity information and account information of a normal user.
  • the user's identity information is represented by an identity node.
  • the user authenticates account information A and account information B through his own identity information. Therefore, both the node of account information A and the node of account information B are connected to the identity node. line.
  • a connection can be established between a node of account information A and a node of account information C, a connection can be established between a node of account information A and a node of account information D, and account information C can be established.
  • a connection is established between the node of the account and the node of the account information D.
  • connections between other account nodes can be established based on common characteristics.
  • FIG. 3 it is assumed that the identity information of the user in FIG. 2 has been fraudulently used by the fraudulent user, and the fraudulent user has created the account information H using the identity information of the user, and the fraudulent user has used the account information H and two other account information I And J carried out the transfer. Then, because the faker and the user are two different subjects, the relationship network between the two different subjects is likely to have no intersection, which is reflected in the relationship map.
  • the account group where the account information used by the faker is located is related to the user.
  • the account group where the account information used is usually not associated.
  • the path connecting the first account node and the second account node may include the path with the least number of connections as the shortest path between the first account node and the second account node .
  • the paths connecting account node C and account node B in Figure 3 can include at least the path from C to B, the path from C to D to A to the identity node to B, and the path from C to A to There are multiple paths such as the path from the identity node to B.
  • the path containing the least amount of data is the path from C to B, which contains only one connection. Therefore, the connection between account node C and account node B
  • the shortest path is the path from C to B directly.
  • the shortest path from account node A to account node I should be the path from A to the identity node to H to I, which includes 3 connections. It can be seen that, in some shortest paths, identity nodes (such as the shortest paths of A and I), and in some shortest paths, identity nodes (such as the shortest paths of C and B) may not be included.
  • the shortest path between different account nodes in the account group associated with the account information is likely to not include the identity node of the user. For example, in FIG. 3, node C to node B, node C to node F, node G to node A, and node E to node A.
  • the shortest path between these nodes does not include the identity node.
  • the account group associated with the account information created by the fraudulent user if the account node in the account group is to generate the shortest path with the account node in the account group associated with the user, then the generated shortest path often contains The user's identity node.
  • the shortest paths from account nodes H, I, and J to any account node in account node A to account node G in FIG. 3 will all include identity nodes. It can be seen that if the identity information of the user is not used, the identity node corresponding to the identity information of the user will not be included in a large number of shortest paths. However, if the identity information of the user is fraudulent, the identity nodes corresponding to the identity information of the user will be included in more shortest paths.
  • this embodiment can calculate the risk value of the target identity node in the relationship graph.
  • the target identity node may be an identity node that is currently to be analyzed.
  • the number of shortest paths in which the target identity node representing the target identity information is located may be counted in the established relationship graph, and the shortest path in the relationship graph may be counted. The total number. Then, the ratio between the number of the shortest paths where the target identity node is located and the total number of the shortest paths may be determined as the risk value of the target identity information. It can be seen that the more the shortest path the target identity node is in, the higher the corresponding risk value.
  • the determined risk value may be compared with a specified threshold value, and if the determined risk value is greater than the specified threshold value, it is determined that the target identity information has a fraud risk.
  • the specified threshold may be a value obtained by calculating a relationship graph of a large number of normal users. For example, the ratio between the number of shortest paths and the total number of identity nodes where the identity node is calculated according to the relationship graph of normal users is usually not higher than 0.4, then you can add 0.4 or a certain redundancy value on the basis of 0.4.
  • the value of the specified threshold can be flexibly set according to the actual application scenario, which is not limited in this specification.
  • each identity node appearing in the relationship graph can be processed in the manner described above, so as to screen out identity information that is at risk of fraud.
  • various account information bound to the identity information with fraud risk may be extracted from the user data. Then you can analyze each account information one by one to filter out suspicious account information that may be created by a fraudulent user.
  • screening suspicious account information it can be determined whether the current account information is authenticated in the same device with a large number of other account information at the same time. If so, the current account information may be suspicious account information created by a fraudulent user.
  • an authentication device corresponding to the current account information may be determined according to a historical operation record of the current account information.
  • the historical authentication information generated by the authentication device may be queried, and the number of accounts authenticated in the authentication device may be counted from the historical authentication information. If the counted number is greater than or equal to a specified number threshold, then Determining that the current account information is suspicious account information.
  • the specified number threshold can be flexibly set according to the actual situation. For example, the average number of authentications of normal authentication devices can be counted, and then the average number of authentications or a certain redundancy value can be added to the average number of authentications as the designation. Number threshold.
  • an embodiment of the present specification further provides a device for identifying identity information, including:
  • a data obtaining unit configured to obtain user data, where the user data includes identity information and account information of the user;
  • An association relationship establishing unit configured to establish an association relationship between the bound account information and identity information in the user data, and establish an association relationship between two account information having common characteristics in the user data;
  • An identifying unit is configured to determine a risk value of target identity information in the user data according to the established association relationship, and determine whether the target identity information is at risk of fraud based on the determined risk value.
  • the association relationship establishing unit includes:
  • a relationship graph establishment module is configured to establish a relationship graph corresponding to the user data, where the relationship graph includes an identity node for characterizing identity information in the user data and an account node for characterizing account information in the user data Among them, there is a connection between the bound account node and the identity node, and there is a connection between the two account nodes that have common characteristics.
  • the path connecting the first account node and the second account node includes the path with the least number of connections as one of the first account node and the second account node.
  • the identification unit includes:
  • the shortest path statistics module is configured to count, in the established relationship graph, the number of shortest paths in which the target identity node representing the target identity information is located, and count the total number of shortest paths in the relationship graph;
  • a ratio calculation module is configured to determine a ratio between the number of shortest paths where the target identity node is located and the total number of the shortest paths as a risk value of the target identity information.
  • the identification unit includes:
  • a threshold value comparison module is configured to compare the determined risk value with a specified threshold value, and if the determined risk value is greater than the specified threshold value, determine that the target identity information has a fraud risk.
  • the apparatus further includes:
  • a suspicious account screening unit is configured to extract account information bound to the target identity information from the user data if the target identity information is at risk of fraud, and to bind the target information from the user data. Suspicious account information is filtered from the account information.
  • the suspicious account screening unit includes:
  • An authentication device determining module configured to determine the authentication device corresponding to the current account information for the current account information in the account information bound to the target identity information
  • the authentication quantity statistics module is configured to count the number of accounts authenticated in the authentication device. If the counted number is greater than or equal to a specified number threshold, it is determined that the current account information is suspicious account information.
  • an embodiment of the present specification further provides a device for identifying identity information, which includes a memory and a processor.
  • the memory is used to store a computer program.
  • the identification device may include a processor, an internal bus, and a memory.
  • the memory may include a memory and a non-volatile memory.
  • the processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it.
  • the recognition device may further include more or fewer components than those shown in FIG. 5, for example, may further include other processing hardware, such as a GPU (Graphics Processing Unit, image processor), Shows different configurations.
  • processing hardware such as a GPU (Graphics Processing Unit, image processor), Shows different configurations.
  • GPU Graphics Processing Unit, image processor
  • this application does not exclude other implementations, such as logic devices or a combination of software and hardware.
  • the processor may include a central processing unit (CPU) or a graphics processor (GPU), and of course, it may also include other single-chip computers, logic gate circuits, integrated circuits, etc. that have logical processing capabilities, or Proper combination.
  • the memory described in the embodiment of the present application may be a memory device for storing information.
  • the device that can store binary data can be memory; in integrated circuits, a circuit with a storage function that does not have a physical form can also be a memory, such as RAM, FIFO, etc .; in the system, it has physical form of storage
  • the device can also be called a memory.
  • the memory may also be implemented in a cloud storage manner. The specific implementation manner is well limited in this specification.
  • an association relationship between identity information and account information in the user data, and account information and account information may be established.
  • the correlation between identity information and account information can be reflected in: account information is registered or authenticated based on the associated identity information; the correlation between different account information can be reflected in: two account information have common characteristics.
  • the two account information having common characteristics may include, for example, situations in which the two account information interact, the two account information are bound to the same object, and the two account information are logged in on the same device.
  • the technical solution provided by one or more embodiments of the present specification determines whether the identity information is fraudulent by analyzing the correlation between the identity information and the account information. Even if a fraudulent user changes the use environment of an account, the method can identify whether the account is in an abnormal state from the presence of common features between the account and other accounts, thereby improving the identification efficiency of identity information.
  • a programmable logic device Programmable Logic Device (PLD)
  • PLD Programmable Logic Device
  • FPGA Field Programmable Gate Array
  • HDL Hardware Description Language
  • VHDL Very-High-Speed Integrated Circuit Hardware Description Language
  • Verilog Verilog
  • the device, module, or unit described in the foregoing embodiments may be specifically implemented by a computer chip or entity, or may be implemented by a product having a certain function.
  • a typical implementation device is a computer.
  • the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or A combination of any of these devices.
  • the embodiments of the present invention may be provided as a method, an apparatus, or a computer program product. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
  • computer-usable storage media including, but not limited to, disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, so that the instructions generated by the processor of the computer or other programmable data processing device are used to generate instructions Means for implementing the functions specified in one or more flowcharts and / or one or more blocks of the block diagrams.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to work in a particular manner such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, the instructions
  • the device implements the functions specified in one or more flowcharts and / or one or more blocks of the block diagram.
  • These computer program instructions can also be loaded on a computer or other programmable data processing device, so that a series of steps can be performed on the computer or other programmable device to produce a computer-implemented process, which can be executed on the computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more flowcharts and / or one or more blocks of the block diagrams.
  • a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.
  • processors CPUs
  • input / output interfaces output interfaces
  • network interfaces network interfaces
  • memory volatile and non-volatile memory
  • Memory may include non-persistent memory, random access memory (RAM), and / or non-volatile memory in computer-readable media, such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
  • RAM random access memory
  • ROM read-only memory
  • flash RAM flash memory
  • Computer-readable media includes both permanent and non-persistent, removable and non-removable media.
  • Information can be stored by any method or technology.
  • Information may be computer-readable instructions, data structures, modules of a program, or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), and read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, Magnetic tape cartridges, magnetic tape magnetic disk storage, graphene storage, or other magnetic storage devices or any other non-transmission media can be used to store information that can be accessed by computing devices.
  • computer-readable media does not include temporary computer-readable media, such as modulated data signals and carrier waves.
  • program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types.
  • This specification can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected through a communication network.
  • program modules may be located in local and remote computer storage media, including storage devices.

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Abstract

本说明书实施例提供一种身份信息的识别方法及装置，其中，所述方法包括：获取用户数据，所述用户数据中包括用户的身份信息和账户信息；建立所述用户数据中相绑定的账户信息和身份信息之间的关联关系，并建立所述用户数据中存在共性特征的两个账户信息之间的关联关系；根据建立的所述关联关系，确定所述用户数据中目标身份信息的风险值，并基于确定的所述风险值，判断所述目标身份信息是否存在冒用风险。本说明书描述的技术方案，能够提高身份信息的识别效率。

Description

一种身份信息的识别方法及装置 技术领域
本说明书实施例涉及互联网技术领域，特别涉及一种身份信息的识别方法及装置。
背景技术
随着互联网技术的不断发展，网络中的信息安全问题也日益凸显。目前，用户的身份信息可能会被不法分子盗用。不法分子会利用盗用的身份信息注册账户，并利用注册的账户进行各种违法操作，从而对用户带来不良的后果。
当前，在识别身份信息是否被他人冒用时，通常可以根据账户的使用环境来判定。例如，如果某个账户与其它大量的账户在同一台设备中进行认证，那么该账户对应的身份信息很有可能被人冒用了。然而，如果账户的使用环境发生改变，这种方法就无法有效地识别出被冒用的身份信息。因此，目前亟需一种有效的识别身份信息的方法。
发明内容
本说明书实施例的目的是提供一种身份信息的识别方法及装置，能够提高身份信息的识别效率。
为实现上述目的，本说明书的一些实施例是这样实现的：
一种身份信息的识别方法，包括：
获取用户数据，所述用户数据中包括用户的身份信息和账户信息；
建立所述用户数据中相绑定的账户信息和身份信息之间的关联关系，并建立所述用户数据中存在共性特征的两个账户信息之间的关联关系；
根据建立的所述关联关系，确定所述用户数据中目标身份信息的风险值，并基于确定的所述风险值，判断所述目标身份信息是否存在冒用风险。
一种身份信息的识别装置，包括：
数据获取单元，用于获取用户数据，所述用户数据中包括用户的身份信息和账户信息；
关联关系建立单元，用于建立所述用户数据中相绑定的账户信息和身份信息之间的 关联关系，并建立所述用户数据中存在共性特征的两个账户信息之间的关联关系；
识别单元，用于根据建立的所述关联关系，确定所述用户数据中目标身份信息的风险值，并基于确定的所述风险值，判断所述目标身份信息是否存在冒用风险。
一种身份信息的识别装置，包括存储器和处理器，所述存储器用于存储计算机程序，所述计算机程序被所述处理器执行时，实现上述的身份信息的识别方法。
由以上可见，本说明书一个或多个实施例中，在获取到待分析的用户数据之后，可以建立该用户数据中身份信息与账户信息，以及账户信息与账户信息之间的关联关系。其中，身份信息与账户信息之间的关联性可以体现在：账户信息是基于关联的身份信息注册或者认证的；不同账户信息之间的关联性可以体现在：两个账户信息具备共性特征。进一步地，两个账户信息具备共性特征例如可以包括两个账户信息存在交互行为、两个账户信息绑定同一个对象、两个账户信息在同一台设备中登陆等情况。通常而言，与同一个身份信息关联的多个账户信息中，往往会存在两个或者更多个具备共性特征的账户信息。原因在于，用户在使用自身创建的多个账户时，这些账户中的部分账户可能会在同一台设备中登陆，某些账户之间也可能存在转账、协助认证、绑定同一部手机登情况，从而使得这些账户之间具备共性特征。而一旦某个用户的身份信息被冒用，冒用者基于该用户的身份信息创建的账户，通常不会或者很少会与该用户的其它账户具备共性特征。这样，通过分析用户数据中身份信息与账户信息，以及账户信息与账户信息之间的关联性，从而可以确定出某个目标身份信息的风险值。一旦该风险值过高，便可以认为该目标身份信息存在冒用风险。由此可见，本说明书一个或多个实施例提供的技术方案，通过分析身份信息和账户信息之间的关联性，从而确定出身份信息是否被冒用。即使冒用者改变账户的使用环境，本方法还是能够从该账户与其它账户之间是否存在共性特征来识别该账户是否处于异常状态，从而能够提高身份信息的识别效率。
附图说明
为了更清楚地说明本说明书一个或多个实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本说明书中记载的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本说明书提供的一种身份信息的识别方法流程图；
图2是本说明书提供的正常用户对应的关系图谱；
图3是本说明书提供的包含冒用账户信息的关系图谱；
图4是本说明书提供的身份信息的识别装置的功能模块示意图；
图5是本说明书提供的身份信息的识别装置的结构示意图。
具体实施方式
本说明书实施例提供一种身份信息的识别方法及装置。
为了使本技术领域的人员更好地理解本说明书中的技术方案，下面将结合本说明书实施例中的附图，对本说明书实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本说明书一部分实施例，而不是全部的实施例。基于本说明书中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本说明书保护的范围。
请参阅图1，本说明书一个实施例提供的身份信息的识别方法可以包括以下步骤。
S1：获取用户数据，所述用户数据中包括用户的身份信息和账户信息。
在本实施例中，所述用户数据可以是待分析的数据，该用户数据可以从各个网站的后台服务器中获取。所述用户数据中可以包括账户信息，该账户信息例如可以是用户注册的账户名，或者是该账户名在网站中的数字ID。用户在注册账户时，可以一并填写自身的身份信息，或者在注册账户之后进行身份信息的认证。这样，在网站的后台服务器中便可以将注册的账户信息以及认证的身份信息进行绑定。所述身份信息可以是身份证信息、护照信息、医保卡信息、驾照信息等能够唯一表征用户身份的信息。
S3：建立所述用户数据中相绑定的账户信息和身份信息之间的关联关系，并建立所述用户数据中存在共性特征的两个账户信息之间的关联关系。
在本实施例中，用户数据中的相绑定的账户信息和身份信息可以表征该账户信息是通过该身份信息进行认证的，因此，相绑定的账户信息和身份信息之间可以具备关联性。此外，不同的账户信息在使用过程中，可能会存在共性特征。该共性特征可以体现在多方面。例如，若两个账户信息存在交互行为，则可以认为这两个账户信息存在共性特征。所述交互行为可以是相互转账、发送聊天信息、共同支付/收取同一笔资金等行为。又例如，若两个账户信息绑定同一个对象，也可以认为这两个账户信息存在共性特征。绑定 同一个对象可以指两个账户信息绑定相同的收货地址、绑定同一个手机号码、绑定同一个电子邮箱等。又例如，若两个账户信息在同一台设备中登陆，也可以认为这两个账户信息存在共性特征。当然，在实际应用中，具备共性特征的两个账户信息还可以包含其它更多的情况，本实施例中上述举例的情形并非穷举，本领域技术人员在理解本实施例技术方案的精髓的情况下增加的具备共性特征的其它情形，也应当属于本实施例的保护范围。
在本实施例中，可以建立所述用户数据中相绑定的账户信息和身份信息之间的关联关系，并建立所述用户数据中存在共性特征的两个账户信息之间的关联关系。在一个实施例中，为了具象化这种关联关系，可以采用关系图谱的形式来表示账户信息和身份信息，以及账户信息与账户信息之间的关联关系。请参阅图2，在关系图谱中，可以将账户信息和身份信息分别用账户节点和身份节点来表示，并且相绑定的账户节点和身份节点之间具备连线，存在共性特征的两个账户节点之间也具备连线。这样，通过节点的方式来表征用户数据中的各个信息，并且在关联的节点之间增加连线之后，便可以建立该用户数据对应的关系图谱。
S5：根据建立的所述关联关系，确定所述用户数据中目标身份信息的风险值，并基于确定的所述风险值，判断所述目标身份信息是否存在冒用风险。
在本实施例中，在用户数据中建立了各个信息之间的关联关系之后，可以对建立的关联关系进行分析，从而得到正常用户与身份冒用者之间的差别，并基于分析得到的差别来识别可能存在冒用风险的身份信息。具体地，以图2所示的关系谱图为例，图2所示的是某个正常用户的身份信息和账户信息的关系图谱。在图2中，用户的身份信息通过身份节点来表示，用户通过自身的身份信息认证了账户信息A和账户信息B，因此，账户信息A的节点和账户信息B的节点均与身份节点建立连线。此外，账户信息A与账户信息C之间产生过转账行为，并且账户信息A与账户信息D在同一台设备上登陆过，此外，账户信息C和账户信息D绑定了相同的收货地址，因此，根据上述的共性特征，可以在账户信息A的节点与账户信息C的节点之间建立连线，在账户信息A的节点与账户信息D的节点之间建立连线，并且在账户信息C的节点和账户信息D的节点之间建立连线。类似地，可以根据共性特征建立其它账户节点之间的连线。从图2可见，尽管用户利用自身的身份信息创建了两个不同的账户，但由于这两个账户都是该用户在使用，因此这两个账户很可能会与另外一个相同的账户均存在共性特征，在关系图谱中则可以表现为两个账户各自关联的账户群之间，也会存在关联关系。
然而，请参阅图3，假设图2中用户的身份信息被人冒用，冒用者利用该用户的身份信息创建了账户信息H，并且冒用者利用账户信息H与另外两个账户信息I和J分别进行了转账行为。那么由于冒用者与用户是两个不同的主体，这两个不同的主体之间的关系网络很可能没有交集，反映到关系图谱中，冒用者使用的账户信息所在的账户群，与用户使用的账户信息所在的账户群，通常没有关联。
由上可见，在关系图谱中，正常用户创建的账户信息与冒用者基于正常用户的身份信息创建的账户信息之间，存在明显的差异。为了量化这种差异，在本实施例中，可以引入不同账户节点之间的最短路径的概念。具体地，在关系图谱中，可以将连接第一账户节点和第二账户节点的路径中，包含连线数量最少的路径作为所述第一账户节点和所述第二账户节点之间的最短路径。举例来说，图3中连接账户节点C和账户节点B的各条路径中，至少可以包括从C到B的路径、从C到D到A到身份节点到B的路径以及从C到A到身份节点到B的路径等多条路径，在这些路径中，包含连线数据量最少的便是从C到B的路径，仅包含一条连接，因此，接账户节点C和账户节点B之间的最短路径便是从C直接到B的路径。再举例来说，从账户节点A到账户节点I的最短路径，应当是从A到身份节点到H到I的路径，包含了3条连线。由此可见，在某些最短路径中，可以包含身份节点(例如A与I的最短路径)，而在某些最短路径中，可以不包含身份节点(例如C与B的最短路径)。从关系图谱中可以发现，对于用户自身创建的多个账户信息而言，与这些账户信息关联的账户群中，不同账户节点之间的最短路径很有可能不包含该用户的身份节点。例如在图3中，节点C到节点B，节点C到节点F，节点G到节点A，节点E到节点A，这些节点之间的最短路径均不包括身份节点。然而，针对冒用者创建的账户信息关联的账户群中，如果该账户群中的账户节点要与用户关联的账户群中的账户节点生成最短路径，那么在生成的最短路径中，往往都包含用户的身份节点。例如，从账户节点H、I、J分别到图3中账户节点A至账户节点G中任一账户节点之间的最短路径，都会包含身份节点。由此可见，如果用户的身份信息不被冒用，那么该用户的身份信息对应的身份节点并不会包含在大量的最短路径中。然而，如果该用户的身份信息被冒用，那么该用户的身份信息对应的身份节点会包含于较多的最短路径中。
基于上述的差别，本实施例可以计算关系图谱中目标身份节点的风险值。该目标身份节点可以是当前待分析的身份节点。在计算目标身份节点的风险值时，可以在建立的所述关系图谱中，统计表征所述目标身份信息的目标身份节点所处的最短路径的条数，并统计所述关系图谱中最短路径的总条数。然后，可以将所述目标身份节点所处的最短 路径的条数与所述最短路径的总条数之间的比值确定为所述目标身份信息的风险值。由此可见，目标身份节点所处的最短路径的条数越多，对应的风险值则越高。这样，可以基于确定的所述风险值，判断所述目标身份信息是否存在冒用风险。具体地，可以将确定的所述风险值与指定阈值进行比较，若确定的所述风险值大于所述指定阈值，则判定所述目标身份信息存在冒用风险。所述指定阈值可以是针对大量的正常用户的关系图谱进行计算得到的数值。例如，根据正常用户的关系图谱计算得到的身份节点所在的最短路径的条数与总条数的比值通常不高于0.4，那么便可以将0.4或者在0.4的基础上加上一定的冗余值，作为所述指定阈值。根据实际应用场景，该指定阈值的数值可以灵活设置，本说明书对此并不做限定。
在一个实施例中，针对关系图谱中出现的各个身份节点，均可以按照上述的方式进行处理，从而筛选出存在冒用风险的身份信息。针对存在冒用风险的身份信息，可以从所述用户数据中提取与存在冒用风险的身份信息相绑定的各个账户信息。然后可以逐一分析各个账户信息，从而筛选出可能是冒用者创建的可疑账户信息。在筛选可疑账户信息时，可以判断当前账户信息是否同时与其它大量的账户信息在同一个设备中进行认证，如果是，那么当前账户信息便可能是冒用者创建的可疑账户信息。具体地，针对与目标身份信息相绑定的账户信息中的当前账户信息，可以根据该当前账户信息的历史操作记录，确定所述当前账户信息对应的认证设备。然后，可以查询该认证设备所产生的历史认证信息，并从该历史认证信息中统计在所述认证设备中进行认证的账户的数量，若统计的所述数量大于或者等于指定数量阈值，则可以判定所述当前账户信息为可疑账户信息。所述指定数量阈值可以根据实际情况灵活设置，例如，可以统计正常认证设备的平均认证数量，然后将该平均认证数量或者在该平均认证数量的基础上加上一定的冗余值，作为该指定数量阈值。
请参阅图4，本说明书一个实施例还提供一种身份信息的识别装置，包括：
数据获取单元，用于获取用户数据，所述用户数据中包括用户的身份信息和账户信息；
关联关系建立单元，用于建立所述用户数据中相绑定的账户信息和身份信息之间的关联关系，并建立所述用户数据中存在共性特征的两个账户信息之间的关联关系；
识别单元，用于根据建立的所述关联关系，确定所述用户数据中目标身份信息的风险值，并基于确定的所述风险值，判断所述目标身份信息是否存在冒用风险。
在一个实施例中，所述关联关系建立单元包括：
关系图谱建立模块，用于建立所述用户数据对应的关系图谱，所述关系图谱中包括用于表征所述用户数据中身份信息的身份节点以及用于表征所述用户数据中账户信息的账户节点，其中，相绑定的账户节点和身份节点之间具备连线，并且存在共性特征的两个账户节点之间具备连线。
在一个实施例中，在所述关系图谱中，将连接第一账户节点和第二账户节点的路径中，包含连线数量最少的路径作为所述第一账户节点和所述第二账户节点之间的最短路径；相应地，所述识别单元包括：
最短路径统计模块，用于在建立的所述关系图谱中，统计表征所述目标身份信息的目标身份节点所处的最短路径的条数，并统计所述关系图谱中最短路径的总条数；
比值计算模块，用于将所述目标身份节点所处的最短路径的条数与所述最短路径的总条数之间的比值确定为所述目标身份信息的风险值。
在一个实施例中，所述识别单元包括：
阈值比较模块，用于将确定的所述风险值与指定阈值进行比较，若确定的所述风险值大于所述指定阈值，判定所述目标身份信息存在冒用风险。
在一个实施例中，所述装置还包括：
可疑账户筛选单元，用于若所述目标身份信息存在冒用风险，从所述用户数据中提取与所述目标身份信息相绑定的账户信息，并从与所述目标身份信息相绑定的账户信息中筛选出可疑账户信息。
在一个实施例中，所述可疑账户筛选单元包括：
认证设备确定模块，用于针对与所述目标身份信息相绑定的账户信息中的当前账户信息，确定所述当前账户信息对应的认证设备；
认证数量统计模块，用于统计在所述认证设备中进行认证的账户的数量，若统计的所述数量大于或者等于指定数量阈值，判定所述当前账户信息为可疑账户信息。
请参阅图5，本说明书一个实施例还提供一种身份信息的识别装置，包括存储器和处理器，所述存储器用于存储计算机程序，所述计算机程序被所述处理器执行时，可以实现上述的身份信息的识别方法。具体地，如图5所示，在硬件层面，该识别装置可以包括处理器、内部总线和存储器。所述存储器可以包括内存以及非易失性存储器。处理 器从非易失性存储器中读取对应的计算机程序到内存中然后运行。本领域普通技术人员可以理解，图5所示的结构仅为示意，其并不对上述识别装置的结构造成限定。例如，所述识别装置还可包括比图5中所示更多或者更少的组件，例如还可以包括其他的处理硬件，如GPU(Graphics Processing Unit，图像处理器)，或者具有与图5所示不同的配置。当然，除了软件实现方式之外，本申请并不排除其他实现方式，比如逻辑器件抑或软硬件结合的方式等等。
本说明书实施例中，所述的处理器可以包括中央处理器(CPU)或图形处理器(GPU)，当然也可以包括其他的具有逻辑处理能力的单片机、逻辑门电路、集成电路等，或其适当组合。本申请实施例所述的存储器可以是用于保存信息的记忆设备。在数字系统中，能保存二进制数据的设备可以是存储器；在集成电路中，一个没有实物形式的具有存储功能的电路也可以为存储器，如RAM、FIFO等；在系统中，具有实物形式的存储设备也可以叫存储器等。实现的时候，该存储器也可以采用云存储器的方式实现，具体实现方式，本说明书不错限定。
需要说明的是，本说明书实施例上述所述的装置，根据相关方法实施例的描述还可以包括其他的实施方式。具体的实现方式可以参照方法实施例的描述，在此不作一一赘述。
由以上可见，本说明书一个或多个实施例中，在获取到待分析的用户数据之后，可以建立该用户数据中身份信息与账户信息，以及账户信息与账户信息之间的关联关系。其中，身份信息与账户信息之间的关联性可以体现在：账户信息是基于关联的身份信息注册或者认证的；不同账户信息之间的关联性可以体现在：两个账户信息具备共性特征。进一步地，两个账户信息具备共性特征例如可以包括两个账户信息存在交互行为、两个账户信息绑定同一个对象、两个账户信息在同一台设备中登陆等情况。通常而言，与同一个身份信息关联的多个账户信息中，往往会存在两个或者更多个具备共性特征的账户信息。原因在于，用户在使用自身创建的多个账户时，这些账户中的部分账户可能会在同一台设备中登陆，某些账户之间也可能存在转账、协助认证、绑定同一部手机登情况，从而使得这些账户之间具备共性特征。而一旦某个用户的身份信息被冒用，冒用者基于该用户的身份信息创建的账户，通常不会或者很少会与该用户的其它账户具备共性特征。这样，通过分析用户数据中身份信息与账户信息，以及账户信息与账户信息之间的关联性，从而可以确定出某个目标身份信息的风险值。一旦该风险值过高，便可以认为该目标身份信息存在冒用风险。由此可见，本说明书一个或多个实施例提供的技术方案，通 过分析身份信息和账户信息之间的关联性，从而确定出身份信息是否被冒用。即使冒用者改变账户的使用环境，本方法还是能够从该账户与其它账户之间是否存在共性特征来识别该账户是否处于异常状态，从而能够提高身份信息的识别效率。
上述对本说明书特定实施例进行了描述。其它实施例在所附权利要求书的范围内。在一些情况下，在权利要求书中记载的动作或步骤可以按照不同于实施例中的顺序来执行并且仍然可以实现期望的结果。另外，在附图中描绘的过程不一定要求示出的特定顺序或者连续顺序才能实现期望的结果。在某些实施方式中，多任务处理和并行处理也是可以的或者可能是有利的。
虽然本申请提供了如实施例或流程图所述的方法操作步骤，但基于常规或者无创造性的劳动可以包括更多或者更少的操作步骤。实施例中列举的步骤顺序仅仅为众多步骤执行顺序中的一种方式，不代表唯一的执行顺序。在实际中的装置或客户端产品执行时，可以按照实施例或者附图所示的方法顺序执行或者并行执行(例如并行处理器或者多线程处理的环境)。
在20世纪90年代，对于一个技术的改进可以很明显地区分是硬件上的改进(例如，对二极管、晶体管、开关等电路结构的改进)还是软件上的改进(对于方法流程的改进)。然而，随着技术的发展，当今的很多方法流程的改进已经可以视为硬件电路结构的直接改进。设计人员几乎都通过将改进的方法流程编程到硬件电路中来得到相应的硬件电路结构。因此，不能说一个方法流程的改进就不能用硬件实体模块来实现。例如，可编程逻辑器件(Programmable Logic Device，PLD)(例如现场可编程门阵列(Field Programmable Gate Array，FPGA))就是这样一种集成电路，其逻辑功能由用户对器件编程来确定。由设计人员自行编程来把一个数字系统“集成”在一片PLD上，而不需要请芯片制造厂商来设计和制作专用的集成电路芯片。而且，如今，取代手工地制作集成电路芯片，这种编程也多半改用“逻辑编译器(logic compiler)”软件来实现，它与程序开发撰写时所用的软件编译器相类似，而要编译之前的原始代码也得用特定的编程语言来撰写，此称之为硬件描述语言(Hardware Description Language，HDL)，而HDL也并非仅有一种，而是有许多种，如ABEL(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby Hardware Description Language)等，目前最普遍使用的是VHDL(Very-High-Speed Integrated Circuit Hardware Description Language) 与Verilog。本领域技术人员也应该清楚，只需要将方法流程用上述几种硬件描述语言稍作逻辑编程并编程到集成电路中，就可以很容易得到实现该逻辑方法流程的硬件电路。
上述实施例阐明的装置、模块或单元，具体可以由计算机芯片或实体实现，或者由具有某种功能的产品来实现。一种典型的实现设备为计算机。具体的，计算机例如可以为个人计算机、膝上型计算机、蜂窝电话、相机电话、智能电话、个人数字助理、媒体播放器、导航设备、电子邮件设备、游戏控制台、平板计算机、可穿戴设备或者这些设备中的任何设备的组合。
为了描述的方便，描述以上装置时以功能分为各种单元分别描述。当然，在实施本说明书时可以把各单元的功能在同一个或多个软件和/或硬件中实现。
本领域内的技术人员应明白，本发明的实施例可提供为方法、装置、或计算机程序产品。因此，本发明可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(装置)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
在一个典型的配置中，计算设备包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
内存可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)。内存是计算机可读介质的示例。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储、石墨烯存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
还需要说明的是，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、商品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、商品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、商品或者设备中还存在另外的相同要素。
本领域技术人员应明白，本说明书的实施例可提供为方法、装置或计算机程序产品。因此，本说明书可采用完全硬件实施例、完全软件实施例或结合软件和硬件方面的实施例的形式。而且，本说明书可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本说明书可以在由计算机执行的计算机可执行指令的一般上下文中描述，例如程序模块。一般地，程序模块包括执行特定任务或实现特定抽象数据类型的例程、程序、对象、组件、数据结构等等。也可以在分布式计算环境中实践本说明书，在这些分布式计算环境中，由通过通信网络而被连接的远程处理设备来执行任务。在分布式计算环境中，程序模块可以位于包括存储设备在内的本地和远程计算机存储介质中。
本说明书中的各个实施例均采用递进的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。尤其，对于装置和服务器实施例而言，由于其基本相似于方法实施例，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
以上所述仅为本说明书的实施例而已，并不用于限制本说明书。对于本领域技术人员来说，本说明书可以有各种更改和变化。凡在本说明书的精神和原理之内所作的任何修改、等同替换、改进等，均应包含在权利要求范围之内。

Claims (14)

  1. 一种身份信息的识别方法，包括：
    获取用户数据，所述用户数据中包括用户的身份信息和账户信息；
    建立所述用户数据中相绑定的账户信息和身份信息之间的关联关系，并建立所述用户数据中存在共性特征的两个账户信息之间的关联关系；
    根据建立的所述关联关系，确定所述用户数据中目标身份信息的风险值，并基于确定的所述风险值，判断所述目标身份信息是否存在冒用风险。
  2. 根据权利要求1所述的方法，建立所述用户数据中相绑定的账户信息和身份信息之间的关联关系，并建立所述用户数据中存在共性特征的两个账户信息之间的关联关系包括：
    建立所述用户数据对应的关系图谱，所述关系图谱中包括用于表征所述用户数据中身份信息的身份节点以及用于表征所述用户数据中账户信息的账户节点，其中，相绑定的账户节点和身份节点之间具备连线，并且存在共性特征的两个账户节点之间具备连线。
  3. 根据权利要求1或2所述的方法，两个账户信息具备共性特征包括以下至少一种：
    两个账户信息存在交互行为；
    两个账户信息绑定同一个对象；
    两个账户信息在同一台设备中登陆。
  4. 根据权利要求2所述的方法，在所述关系图谱中，将连接第一账户节点和第二账户节点的路径中，包含连线数量最少的路径作为所述第一账户节点和所述第二账户节点之间的最短路径；相应地，确定所述用户数据中目标身份信息的风险值包括：
    在建立的所述关系图谱中，统计表征所述目标身份信息的目标身份节点所处的最短路径的条数，并统计所述关系图谱中最短路径的总条数；
    将所述目标身份节点所处的最短路径的条数与所述最短路径的总条数之间的比值确定为所述目标身份信息的风险值。
  5. 根据权利要求1或4所述的方法，判断所述目标身份信息是否存在冒用风险包括：
    将确定的所述风险值与指定阈值进行比较，若确定的所述风险值大于所述指定阈值，判定所述目标身份信息存在冒用风险。
  6. 根据权利要求1所述的方法，在判断所述目标身份信息是否存在冒用风险之后，所述方法还包括：
    若所述目标身份信息存在冒用风险，从所述用户数据中提取与所述目标身份信息相绑定的账户信息，并从与所述目标身份信息相绑定的账户信息中筛选出可疑账户信息。
  7. 根据权利要求6所述的方法，从与所述目标身份信息相绑定的账户信息中筛选出可疑账户信息包括：
    针对与所述目标身份信息相绑定的账户信息中的当前账户信息，确定所述当前账户信息对应的认证设备；
    统计在所述认证设备中进行认证的账户的数量，若统计的所述数量大于或者等于指定数量阈值，判定所述当前账户信息为可疑账户信息。
  8. 一种身份信息的识别装置，包括：
    数据获取单元，用于获取用户数据，所述用户数据中包括用户的身份信息和账户信息；
    关联关系建立单元，用于建立所述用户数据中相绑定的账户信息和身份信息之间的关联关系，并建立所述用户数据中存在共性特征的两个账户信息之间的关联关系；
    识别单元，用于根据建立的所述关联关系，确定所述用户数据中目标身份信息的风险值，并基于确定的所述风险值，判断所述目标身份信息是否存在冒用风险。
  9. 根据权利要求8所述的装置，所述关联关系建立单元包括：
    关系图谱建立模块，用于建立所述用户数据对应的关系图谱，所述关系图谱中包括用于表征所述用户数据中身份信息的身份节点以及用于表征所述用户数据中账户信息的账户节点，其中，相绑定的账户节点和身份节点之间具备连线，并且存在共性特征的两个账户节点之间具备连线。
  10. 根据权利要求9所述的装置，在所述关系图谱中，将连接第一账户节点和第二账户节点的路径中，包含连线数量最少的路径作为所述第一账户节点和所述第二账户节点之间的最短路径；相应地，所述识别单元包括：
    最短路径统计模块，用于在建立的所述关系图谱中，统计表征所述目标身份信息的目标身份节点所处的最短路径的条数，并统计所述关系图谱中最短路径的总条数；
    比值计算模块，用于将所述目标身份节点所处的最短路径的条数与所述最短路径的总条数之间的比值确定为所述目标身份信息的风险值。
  11. 根据权利要求8或10所述的装置，所述识别单元包括：
    阈值比较模块，用于将确定的所述风险值与指定阈值进行比较，若确定的所述风险值大于所述指定阈值，判定所述目标身份信息存在冒用风险。
  12. 根据权利要求8所述的装置，所述装置还包括：
    可疑账户筛选单元，用于若所述目标身份信息存在冒用风险，从所述用户数据中提取与所述目标身份信息相绑定的账户信息，并从与所述目标身份信息相绑定的账户信息 中筛选出可疑账户信息。
  13. 根据权利要求12所述的装置，所述可疑账户筛选单元包括：
    认证设备确定模块，用于针对与所述目标身份信息相绑定的账户信息中的当前账户信息，确定所述当前账户信息对应的认证设备；
    认证数量统计模块，用于统计在所述认证设备中进行认证的账户的数量，若统计的所述数量大于或者等于指定数量阈值，判定所述当前账户信息为可疑账户信息。
  14. 一种身份信息的识别装置，包括存储器和处理器，所述存储器用于存储计算机程序，所述计算机程序被所述处理器执行时，实现如权利要求1至7中任一权利要求所述的方法。
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