CN111445254A - Transaction behavior detection method, device and system - Google Patents
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Abstract
The invention discloses a transaction behavior detection method, a device and a system, and relates to the technical field of computers. One embodiment of the method comprises: encrypting and uploading user transaction information owned by transaction behavior detection mechanisms to a central learning platform, so that the central learning platform collects the user transaction information uploaded by one or more transaction behavior detection mechanisms, and trains the collected user transaction information to obtain a transaction behavior detection model; obtaining the transaction behavior detection model from the central learning platform; and detecting the transaction behavior of the user served by the transaction behavior detection mechanism by using the transaction behavior detection model. The implementation mode not only ensures the safety and the privacy of the user transaction information, but also ensures the reliability of the transaction behavior detection model.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a transaction behavior detection method, a device and a system.
Background
The transaction information of the users served by different service organizations, the information of the users and the like have certain privacy, and as a unique information asset, the service organizations generally do not disclose or share the user transaction information and the like, so that the possibility is provided for the users to carry out various transactions at one or more service organizations simultaneously so as to realize illegal transactions, and the user transaction information of the service organizations cannot be integrated to identify the illegal transaction behaviors of the users in the purpose of data protection.
If the service organization is taken as a bank for example for explanation, on one hand, the same user can utilize a plurality of different banks for opening accounts to perform transaction operations such as transfer, fund transfer and the like, and realize tax stealing and tax leaking by utilizing some loopholes; on the other hand, in order to avoid possible malignant competition among different banks and protect the security of user transaction information, the user transaction information cannot be shared among different banks, so that the transaction information of the user at all the account opening banks cannot be integrated to judge whether the user carries out illegal or abnormal transaction behaviors such as tax evasion, tax omission and the like.
Disclosure of Invention
In view of this, the invention provides a transaction behavior detection method, a device and a system, which can summarize all user transaction information to train to obtain a more reliable and efficient transaction behavior detection model while protecting the security and privacy of the user transaction information owned by each organization.
To achieve the above object, according to a first aspect of the present invention, there is provided a transaction behavior detection method applied to a transaction behavior detection mechanism, including:
encrypting and uploading user transaction information owned by transaction behavior detection mechanisms to a central learning platform, so that the central learning platform collects the user transaction information uploaded by one or more transaction behavior detection mechanisms, and trains the collected user transaction information to obtain a transaction behavior detection model;
obtaining the transaction behavior detection model from the central learning platform;
and detecting the transaction behavior of the user served by the transaction behavior detection mechanism by using the transaction behavior detection model.
Optionally, the method further comprises:
before user transaction information owned by a transaction behavior detection mechanism is encrypted and uploaded to a central learning platform, the user transaction information is processed by using word frequency-reverse document frequency.
Optionally, the processing the user transaction information by using a word frequency-inverse document frequency technique includes:
the user transaction information comprises one or more kinds of transaction information corresponding to one or more users, a category word is extracted from each kind of transaction information, and each category word indicates a transaction performed by the user;
and counting the category words corresponding to each user and the occurrence frequency of the category words in the user transaction information, wherein the processed user transaction information indicates the category words corresponding to one or more users and the occurrence frequency of the category words.
In order to achieve the above object, according to a second aspect of the present invention, there is provided a transaction behavior detection method applied to a central learning platform, including:
receiving user transaction information uploaded by one or more transaction behavior detection mechanisms in an encrypted manner;
aggregating the received user transaction information uploaded by one or more of the transaction behavior detection mechanisms;
training the summarized user transaction information to obtain a transaction behavior detection model;
and issuing the transaction behavior detection model to the transaction behavior detection mechanism so that the transaction behavior detection mechanism can detect the transaction behavior of the served user by using the transaction behavior detection model.
Alternatively,
the user transaction information indicates one or more category words corresponding to the user and the occurrence frequency of the category words, and each category word indicates a transaction performed by the user;
summarizing category words corresponding to the same user and the occurrence frequency of the category words uploaded by one or more transaction behavior detection mechanisms;
and training the category words corresponding to the one or more aggregated users and the occurrence frequency of the category words to obtain the transaction behavior detection model.
Optionally, the summarized user transaction information is trained using a logistic regression model to obtain a transaction behavior detection model.
To achieve the above object, according to a third aspect of the present invention, there is provided a transaction behavior detection method, comprising:
the transaction behavior detection mechanism encrypts and uploads owned user transaction information to the central learning platform;
the central learning platform receives the user transaction information uploaded by one or more transaction behavior detection mechanisms in an encrypted manner, summarizes the received transaction information uploaded by one or more transaction behavior detection mechanisms, trains the summarized user transaction information to obtain a transaction behavior detection model, and issues the transaction behavior detection model to one or more transaction behavior detection mechanisms;
and the transaction behavior detection mechanism acquires the transaction behavior detection model from the central learning platform and detects the transaction behavior of the user served by the transaction behavior detection mechanism by using the transaction behavior detection model.
To achieve the above object, according to a fourth aspect of the present invention, there is provided a transaction behavior detection device applied to a transaction behavior detection mechanism, including: the system comprises a user transaction information uploading module, a transaction behavior detection model obtaining module and a transaction behavior detection module; wherein the content of the first and second substances,
the user transaction information uploading module is used for encrypting and uploading user transaction information owned by the transaction behavior detection mechanism to a central learning platform, so that the central learning platform collects the user transaction information uploaded by one or more transaction behavior detection mechanisms and trains the collected user transaction information to obtain a transaction behavior detection model;
the transaction behavior detection model acquisition module is used for acquiring the transaction behavior detection model from the central learning platform;
the transaction behavior detection module is used for detecting the transaction behavior of the user served by the transaction behavior detection mechanism by using the transaction behavior detection model.
To achieve the above object, according to a fifth aspect of the present invention, there is provided a transaction behavior detection system comprising: one or more transaction behavior detection agencies, federal learning skin platforms; wherein the content of the first and second substances,
the transaction behavior detection mechanism is used for encrypting and uploading owned user transaction information to the central learning platform;
the central learning platform is used for receiving the user transaction information encrypted and uploaded by one or more transaction behavior detection mechanisms, summarizing the received transaction information uploaded by the one or more transaction behavior detection mechanisms, training the summarized user transaction information to obtain a transaction behavior detection model, and issuing the transaction behavior detection model to the one or more transaction behavior detection mechanisms;
the transaction behavior detection mechanism is further configured to obtain the transaction behavior detection model from the central learning platform, and detect the transaction behavior of the user served by the transaction behavior detection mechanism by using the transaction behavior detection model.
To achieve the above object, according to a sixth aspect of the present invention, there is provided an electronic device for transaction behavior detection, comprising: one or more processors; a storage device to store one or more programs that, when executed by the one or more processors, implement the method of any of the transaction behavior detection methods described above.
To achieve the above object, according to a seventh aspect of the present invention, there is provided a computer readable medium having stored thereon a computer program which, when executed by a processor, implements any one of the transaction behavior detection methods described above.
The invention has the following advantages or beneficial effects: because the federal learning technology is adopted for modeling, namely, the user transaction information uploaded by one or more transaction behavior detection mechanisms is collected through the central learning platform in a gathering way, and the gathered user transaction information is trained to obtain a more reliable and effective transaction behavior detection model; on the other hand, when the transaction behavior detection mechanism uploads the user transaction information, the user transaction information is uploaded in an encryption mode and is only uploaded to the central learning platform, interaction does not exist among different transaction behavior detection mechanisms, and the safety and privacy of the user transaction behavior owned by the transaction behavior detection mechanism are effectively guaranteed.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic view of a main flow of a transaction behavior detection method applied to a transaction behavior detection mechanism according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a main flow of another transaction behavior detection method applied to a transaction behavior detection mechanism according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a main flow of a transaction behavior detection method applied to a central learning platform according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the main modules of a transaction activity detection mechanism applied to a transaction activity detection mechanism, according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the main structure of a transaction behavior detection system according to an embodiment of the invention;
fig. 6 is a schematic diagram of a main flow of a transaction behavior detection method applied to a transaction behavior detection system according to an embodiment of the present invention;
FIG. 7 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 8 is a schematic structural diagram of a computer system suitable for implementing a terminal device or a server according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow of a transaction behavior detection method applied to a transaction behavior detection mechanism according to an embodiment of the present invention, and as shown in fig. 1, the transaction behavior detection method may specifically include the following steps:
step S101, user transaction information owned by a transaction behavior detection mechanism is encrypted and uploaded to a central learning platform, so that the central learning platform collects one or more user transaction information uploaded by the transaction behavior detection mechanism, and the collected user transaction information is trained to obtain a transaction behavior detection model.
The transaction behavior detection mechanism in this example refers to financial mechanisms such as banks that provide services for users, and the like, and the central learning platform refers to mechanisms such as central banks that perform machine learning processing on user transaction information uploaded by each transaction behavior detection mechanism by using the federal learning technology. The federal learning is a new artificial intelligence basic technology which is firstly proposed by google in 2016 and is originally used for solving the problem of local model updating of an android mobile phone terminal user, and the design goal of the federal learning is to carry out efficient machine learning among multiple parties or multiple computing nodes on the premise of guaranteeing information safety during big data exchange, protecting terminal data and personal data privacy and guaranteeing legal compliance. It can be understood that, in order to ensure the security and privacy of the user transaction information, the transaction behavior detection mechanism may upload the user transaction information in an encrypted manner, including but not limited to symmetric encryption and asymmetric encryption, for example, the asymmetric encryption is taken as an example, the central learning platform may issue its own public key to one or more transaction behavior detection mechanisms participating in federal learning, and then the transaction behavior detection mechanism may encrypt the owned user transaction information using the public key of the central learning platform and upload the encrypted user transaction information to the central learning platform, so that only the central learning platform may decrypt the user transaction information using the private key corresponding to the public key, thereby ensuring the security of the user transaction information.
Further, before the user transaction information owned by the transaction behavior detection mechanism is encrypted and uploaded to the central learning platform, the user transaction information is processed by using the word frequency-reverse document frequency.
It can be immediately understood that the user transaction information includes various text information besides the digital information such as the transaction amount, and if the user transaction information is directly used for training, adverse effects on the training model caused by information such as a general field (such as a 'bank') appearing in the text information are inevitable. Based on this, it is considered to process user transaction information, especially text information therein, using a word frequency-inverse document frequency technique. The Term Frequency-inverse document Frequency (TF-IDF) is a commonly used weighting technique for information retrieval and data mining, wherein TF means Term Frequency (Term Frequency) and IDF means inverse text Frequency index (inverse document Frequency). TF-IDF is a statistical method used to assess how important a word is for one of a set of documents or a corpus. The importance of a word increases in proportion to the number of times it appears in a document, but at the same time decreases in inverse proportion to the frequency with which it appears in the corpus.
More specifically, the processing the user transaction information by using a word frequency-inverse document frequency technology comprises: the user transaction information comprises one or more kinds of transaction information corresponding to one or more users, a category word is extracted from each kind of transaction information, and each category word indicates a transaction performed by the user; and counting the category words corresponding to each user and the occurrence frequency of the category words in the user transaction information, wherein the processed user transaction information indicates the category words corresponding to one or more users and the occurrence frequency of the category words.
If the user A has an account A1 in bank A, an account B1 in bank B, a public account C1 in bank C and personal accounts C2 and C3 in bank C, the user A can conduct various transaction behaviors including but not limited to transfer accounts, deposit accounts, withdrawal accounts, investments, financing accounts, purchase commodities, loans and the like in the five accounts A1, B1, C1, C2 and C3; and each transaction behavior is the user transaction information of the customer A, and the bank A, the bank B and the bank C only have the transaction information recorded by the customer A under the account of the customer A. If only the term frequency-reverse document frequency technology is used for extracting the category words corresponding to the customer A from the user transaction information of the bank A for illustration, the extracted category words may have one or more, such as "transfer to the account B1", "pay to the account D", "loan to the bank A", and the like, that is, each transaction behavior such as fund change or loan corresponds to one category word, each category word represents one transaction, and the category words corresponding to the same transaction are the same, so that more concise and effective category words can be used to replace the transaction information; meanwhile, it can be understood that, if the frequency of occurrence of the category word corresponding to the user is higher, it indicates that the user performs the transaction behavior corresponding to the category word more frequently. On the basis, the category words corresponding to the user and the occurrence frequency of the category words are used for improving the original user transaction information and uploading the original user transaction information to the central learning platform, so that the original user transaction information can be prevented from being directly uploaded, and the safety and privacy of the user transaction information are further improved.
And step S102, acquiring the transaction behavior detection model from the central learning platform. That is, the federally learned transaction behavior detection mechanism can download a transaction behavior detection model obtained by joint training of data uploaded by a plurality of transaction behavior detection mechanisms from the central learning platform to the central learning platform.
And step S103, detecting the transaction behavior of the user served by the transaction behavior detection mechanism by using the transaction behavior detection model. That is, the transaction characteristics such as the category word and the occurrence frequency of the category word of one user are input, and the transaction behavior detection model is used for detecting whether the transaction behavior is abnormal or legal.
Based on the embodiment, the federal learning technology is adopted for modeling, namely, the user transaction information uploaded by one or more transaction behavior detection mechanisms is collected through the central learning platform, and the collected user transaction information is trained to obtain a more reliable and effective transaction behavior detection model; on the other hand, when the transaction behavior detection mechanism uploads the user transaction information, the user transaction information is uploaded in an encryption mode and is only uploaded to the central learning platform, interaction does not exist among different transaction behavior detection mechanisms, and the safety and privacy of the user transaction behavior owned by the transaction behavior detection mechanism are effectively guaranteed.
Referring to fig. 2, on the basis of the above embodiment, another transaction behavior detection method is provided in the embodiment of the present invention, and the method may specifically include the following steps:
step S201, the transaction information of the user owned by the transaction behavior detection mechanism is processed by using the word frequency-reverse document frequency. That is, a word frequency-reverse document frequency technology is adopted to extract a category word representing the transaction information from each transaction information of the user, the occurrence frequency of the category word is counted, that is, the frequency of the transaction behavior of the user is counted, and the occurrence frequency of the category word and the category word corresponding to the user is used for replacing the original user transaction information and uploading the original user transaction information to the central learning platform, so that the safety of the user transaction information is ensured, and the effectiveness of the training data is also ensured.
Step S202, the processed user transaction information is encrypted and uploaded to a central learning platform, so that the central learning platform collects the user transaction information uploaded by one or more transaction behavior detection mechanisms, and the collected user transaction information is trained to obtain a transaction behavior detection model.
Step S203, obtaining the transaction behavior detection model from the central learning platform.
And step S204, detecting the transaction behavior of the user served by the transaction behavior detection mechanism by using the transaction behavior detection model.
Referring to fig. 3, on the basis of the above embodiment, an embodiment of the present invention provides a transaction behavior detection method applied to a central learning platform, where the method specifically includes the following steps:
step S301, receiving user transaction information uploaded by one or more transaction behavior detection mechanisms in an encrypted manner. Still taking the transaction behavior detection mechanism as a bank, the user a has an account a1 in bank a, has an account B1 in bank B, has a public account C1 in bank C, and has an explanation of personal accounts C2 and C3 as examples, so that the user a can perform various transaction behaviors in five accounts of a1, B1, C1, C2 and C3, while the bank a only has transaction information performed by the user a using the account a1, the bank B only has transaction information performed by the user a using the account B1, the bank C only has transaction information performed by the user a using the accounts C1, C2 and C3, and all transaction information of the user a in five accounts of a1, B1, C1, C2 and C3 is all transaction information of the user a. Therefore, in order to ensure the reliability of the trained transaction behavior detection model, the central learning platform needs to receive and summarize the user transaction information uploaded by the bank a, the bank B and the bank C at the same time to ensure the integrity of the same user transaction information.
Step S302, aggregating the received user transaction information uploaded by one or more transaction behavior detection mechanisms. That is, after receiving user transaction information uploaded by multiple transaction behavior detection mechanisms such as a receiving bank a, a bank B, a bank C, and the like, the central learning platform summarizes all user transaction information, especially user transaction information generated by the same user in different transaction bank.
It can be understood that, when the user transaction information received by the central learning platform indicates one or more category words corresponding to the user and the occurrence frequency of the category words, and each category word indicates a transaction performed by the user, the category words corresponding to the same user and the occurrence frequency of the category words uploaded by one or more transaction behavior detection mechanisms are summarized, and the summarized category words corresponding to the one or more users and the occurrence frequency of the category words are trained to obtain the transaction behavior detection model.
Step S303, training the summarized user transaction information to obtain a transaction behavior detection model. The central learning platform of this embodiment trains the summarized user transaction information using a logistic regression model to obtain a transaction behavior detection model.
Step S304, the transaction behavior detection model is issued to the transaction behavior detection mechanism, so that the transaction behavior detection mechanism can detect the transaction behavior of the served user by using the transaction behavior detection model.
Referring to fig. 4, on the basis of the above embodiment, an embodiment of the present invention provides a transaction behavior detection apparatus 400, applied to a transaction behavior detection mechanism, including: the system comprises a user transaction information uploading module 401, a transaction behavior detection model obtaining module 402 and a transaction behavior detection module 403; wherein the content of the first and second substances,
the user transaction information uploading module 401 is configured to encrypt and upload user transaction information owned by a transaction behavior detection mechanism to a central learning platform, so that the central learning platform collects user transaction information uploaded by one or more transaction behavior detection mechanisms, and trains the collected user transaction information to obtain a transaction behavior detection model;
the transaction behavior detection model obtaining module 402 is configured to obtain the transaction behavior detection model from the central learning platform;
the transaction behavior detection module 403 is configured to detect a transaction behavior of a user served by the transaction behavior detection mechanism using the transaction behavior detection model.
Referring to fig. 5, on the basis of the above embodiments, an embodiment of the present invention provides a transaction behavior detection system 500, which includes one or more transaction behavior detection mechanisms and a federal learning skin platform; wherein the content of the first and second substances,
the transaction behavior detection mechanism is used for encrypting and uploading owned user transaction information to the central learning platform;
the central learning platform is used for receiving the user transaction information encrypted and uploaded by one or more transaction behavior detection mechanisms, summarizing the received transaction information uploaded by the one or more transaction behavior detection mechanisms, training the summarized user transaction information to obtain a transaction behavior detection model, and issuing the transaction behavior detection model to the one or more transaction behavior detection mechanisms;
the transaction behavior detection mechanism is further configured to obtain the transaction behavior detection model from the central learning platform, and detect the transaction behavior of the user served by the transaction behavior detection mechanism by using the transaction behavior detection model.
Referring to fig. 6, on the basis of the foregoing embodiment, an embodiment of the present invention provides a transaction behavior detection method, which is applied to a transaction behavior detection system, and specifically, the method may specifically include the following steps:
step S601, the transaction behavior detection mechanism encrypts and uploads the owned user transaction information to the central learning platform.
Step S602, the central learning platform receives the user transaction information encrypted and uploaded by one or more transaction behavior detection mechanisms.
Step S603, the central learning platform collects the received transaction information uploaded by one or more of the transaction behavior detection mechanisms.
Step S604, the central learning platform trains the summarized user transaction information to obtain a transaction behavior detection model.
Step S605, the transaction behavior detection model is issued to one or more transaction behavior detection mechanisms.
Step S606, the transaction behavior detection mechanism obtains the transaction behavior detection model from the central learning platform.
Step S607, the transaction behavior detection mechanism uses the transaction behavior detection model to detect the transaction behavior of the user served by the transaction behavior detection mechanism.
Fig. 7 illustrates an exemplary system architecture 700 to which a transaction behavior detection method or a transaction behavior detection apparatus of an embodiment of the invention may be applied.
As shown in fig. 7, the system architecture 700 may include terminal devices 701, 702, 703, a network 704, and a server 705. The network 704 serves to provide a medium for communication links between the terminal devices 701, 702, 703 and the server 705. Network 704 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 701, 702, 703 to interact with a server 705 over a network 704, to receive or send messages or the like. Various communication client applications, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, and the like, may be installed on the terminal devices 701, 702, and 703.
The terminal devices 701, 702, 703 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 705 may be a server that provides various services, such as a background management server that supports shopping websites browsed by users using the terminal devices 701, 702, and 703. The background management server can analyze and process the received data such as the product information inquiry request and feed back the processing result to the terminal equipment.
It should be noted that the transaction behavior detection method provided by the embodiment of the present invention is generally executed by the server 705, and accordingly, the transaction behavior calibration device is generally disposed in the server 705.
It should be understood that the number of terminal devices, networks, and servers in fig. 7 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 8, shown is a block diagram of a computer system 800 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 8, the computer system 800 includes a Central Processing Unit (CPU)801 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data necessary for the operation of the system 800 are also stored. The CPU 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
To the I/O interface 805, AN input section 806 including a keyboard, a mouse, and the like, AN output section 807 including a network interface card such as a Cathode Ray Tube (CRT), a liquid crystal display (L CD), and the like, a speaker, and the like, a storage section 808 including a hard disk, and the like, and a communication section 809 including a network interface card such as a L AN card, a modem, and the like are connected, the communication section 809 performs communication processing via a network such as the internet, a drive 810 is also connected to the I/O interface 805 as necessary, a removable medium 811 such as a magnetic disk, AN optical disk, a magneto-optical disk, a semiconductor memory, and the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted into the storage section 808 as.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program executes the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 801.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor comprises a user transaction information uploading module, a transaction behavior detection model obtaining module and a transaction behavior detection module. The names of these modules do not form a limitation on the module itself in some cases, for example, the transaction behavior detection model acquisition module may also be described as a "module for acquiring the transaction behavior detection model from the central learning platform".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: encrypting and uploading user transaction information owned by transaction behavior detection mechanisms to a central learning platform, so that the central learning platform collects the user transaction information uploaded by one or more transaction behavior detection mechanisms, and trains the collected user transaction information to obtain a transaction behavior detection model; obtaining the transaction behavior detection model from the central learning platform; and detecting the transaction behavior of the user served by the transaction behavior detection mechanism by using the transaction behavior detection model.
According to the technical scheme of the embodiment of the invention, the federal learning technology is adopted for modeling, namely, the user transaction information uploaded by one or more transaction behavior detection mechanisms is collected through the central learning platform in a gathering way, and the gathered user transaction information is trained to obtain a more reliable and effective transaction behavior detection model; on the other hand, when the transaction behavior detection mechanism uploads the user transaction information, the user transaction information is uploaded in an encryption mode and is only uploaded to the central learning platform, interaction does not exist among different transaction behavior detection mechanisms, and the safety and privacy of the user transaction behavior owned by the transaction behavior detection mechanism are effectively guaranteed.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (11)
1. A transaction behavior detection method is applied to a transaction behavior detection mechanism and comprises the following steps:
encrypting and uploading user transaction information owned by transaction behavior detection mechanisms to a central learning platform, so that the central learning platform collects the user transaction information uploaded by one or more transaction behavior detection mechanisms, and trains the collected user transaction information to obtain a transaction behavior detection model;
obtaining the transaction behavior detection model from the central learning platform;
and detecting the transaction behavior of the user served by the transaction behavior detection mechanism by using the transaction behavior detection model.
2. The transaction behavior detection method according to claim 1, further comprising:
before user transaction information owned by a transaction behavior detection mechanism is encrypted and uploaded to a central learning platform, the user transaction information is processed by using word frequency-reverse document frequency.
3. The transaction behavior detection method of claim 2, wherein the processing the user transaction information using a word frequency-inverse document frequency technique comprises:
the user transaction information comprises one or more kinds of transaction information corresponding to one or more users, a category word is extracted from each kind of transaction information, and each category word indicates a transaction performed by the user;
and counting the category words corresponding to each user and the occurrence frequency of the category words in the user transaction information, wherein the processed user transaction information indicates the category words corresponding to one or more users and the occurrence frequency of the category words.
4. A transaction behavior detection method is applied to a central learning platform and comprises the following steps:
receiving user transaction information uploaded by one or more transaction behavior detection mechanisms in an encrypted manner;
aggregating the received user transaction information uploaded by one or more of the transaction behavior detection mechanisms;
training the summarized user transaction information to obtain a transaction behavior detection model;
and issuing the transaction behavior detection model to the transaction behavior detection mechanism so that the transaction behavior detection mechanism can detect the transaction behavior of the served user by using the transaction behavior detection model.
5. The transaction behavior detection method according to claim 4,
the user transaction information indicates one or more category words corresponding to the user and the occurrence frequency of the category words, and each category word indicates a transaction performed by the user;
summarizing category words corresponding to the same user and the occurrence frequency of the category words uploaded by one or more transaction behavior detection mechanisms;
and training the category words corresponding to the one or more aggregated users and the occurrence frequency of the category words to obtain the transaction behavior detection model.
6. The transaction behavior detection method according to claim 4,
and training the summarized user transaction information by using a logistic regression model to obtain a transaction behavior detection model.
7. A transaction behavior detection method, comprising:
the transaction behavior detection mechanism encrypts and uploads owned user transaction information to the central learning platform;
the central learning platform receives the user transaction information uploaded by one or more transaction behavior detection mechanisms in an encrypted manner, summarizes the received transaction information uploaded by one or more transaction behavior detection mechanisms, trains the summarized user transaction information to obtain a transaction behavior detection model, and issues the transaction behavior detection model to one or more transaction behavior detection mechanisms;
and the transaction behavior detection mechanism acquires the transaction behavior detection model from the central learning platform and detects the transaction behavior of the user served by the transaction behavior detection mechanism by using the transaction behavior detection model.
8. A transaction behavior detection device is applied to a transaction behavior detection mechanism and comprises: the system comprises a user transaction information uploading module, a transaction behavior detection model obtaining module and a transaction behavior detection module; wherein the content of the first and second substances,
the user transaction information uploading module is used for encrypting and uploading user transaction information owned by the transaction behavior detection mechanism to a central learning platform, so that the central learning platform collects the user transaction information uploaded by one or more transaction behavior detection mechanisms and trains the collected user transaction information to obtain a transaction behavior detection model;
the transaction behavior detection model acquisition module is used for acquiring the transaction behavior detection model from the central learning platform;
the transaction behavior detection module is used for detecting the transaction behavior of the user served by the transaction behavior detection mechanism by using the transaction behavior detection model.
9. A transaction behavior detection system, comprising: one or more transaction behavior detection agencies, federal learning skin platforms; wherein the content of the first and second substances,
the transaction behavior detection mechanism is used for encrypting and uploading owned user transaction information to the central learning platform;
the central learning platform is used for receiving the user transaction information encrypted and uploaded by one or more transaction behavior detection mechanisms, summarizing the received transaction information uploaded by the one or more transaction behavior detection mechanisms, training the summarized user transaction information to obtain a transaction behavior detection model, and issuing the transaction behavior detection model to the one or more transaction behavior detection mechanisms;
the transaction behavior detection mechanism is further configured to obtain the transaction behavior detection model from the central learning platform, and detect the transaction behavior of the user served by the transaction behavior detection mechanism by using the transaction behavior detection model.
10. An electronic device for transaction behavior detection, comprising:
one or more processors;
a storage device for storing one or more programs,
the one or more programs, when executed by the one or more processors, implement the method of any of claims 1-3.
11. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-3.
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