CN110995688A - Personal data sharing method and device for internet financial platform and terminal equipment - Google Patents
Personal data sharing method and device for internet financial platform and terminal equipment Download PDFInfo
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Abstract
The embodiment of the invention relates to the technical field of internet finance, in particular to a personal data sharing method, a personal data sharing device and a terminal device for an internet financial platform. Therefore, the request information and the multi-type characteristic information corresponding to each request instruction do not need to be detected, the CPU time slice resources of the terminal equipment are effectively saved, the terminal equipment can fully utilize the limited time slice resources to detect the request information and the multi-type characteristic information of the request instruction corresponding to the risk rate exceeding the set threshold value, the detection efficiency is improved, and the influence on the personal data sharing process of the user due to the fact that the excessive CPU time slice resources are occupied is avoided.
Description
Technical Field
The invention relates to the technical field of internet finance, in particular to a personal data sharing method and device for an internet finance platform and terminal equipment.
Background
With the development of internet technology, internet finance has gradually become a new economic development mode, playing an important role in economic development and operation. Compared with the traditional one-to-one and field processing financial mode, the internet finance enables a user to interface with a plurality of internet financial platforms respectively in the same time period, and in the one-to-many mode, the user needs to share personal data with the plurality of internet financial platforms.
When a user shares personal data, potential safety hazards of stealing the personal data by malicious programs, viruses and trojans may exist, the potential safety hazards may be maliciously implemented by some internet financial platforms, and the potential safety hazards may also be implemented by third parties in the information interaction process between the terminal equipment of the user and the internet financial platforms. Therefore, in order to ensure the security of the personal data of the user, it is necessary to detect malicious programs, viruses and trojans when the terminal device of the user performs information interaction with the internet financial platform, but the existing detection method has low detection efficiency and can seriously affect the personal data sharing process of the user.
Disclosure of Invention
In order to overcome at least the above disadvantages of the prior art, an object of the present invention is to provide a method, an apparatus and a terminal device for sharing personal data of an internet financial platform.
The embodiment of the invention provides a personal data sharing method for an Internet financial platform, which comprises the following steps:
receiving a request instruction, wherein the request instruction is generated by the internet financial platform through first encryption processing on a data information stream, and the data information stream is obtained by the internet financial platform through packaging request information and various feature information corresponding to the internet financial platform;
decrypting the request instruction to obtain the request information and the multiple kinds of characteristic information;
determining the hierarchical relationship of each kind of feature information in the multiple kinds of feature information, and generating a logic topology according to each hierarchical relationship and each kind of feature information; the logical topology comprises a characteristic information combination set and a logical path between each characteristic information combination in the characteristic information combination set; determining an initial characteristic information combination in the characteristic information combination set, and splicing all characteristic information combinations in the characteristic information combination set according to the initial characteristic information combination to form a directed acyclic graph; determining a recursion path according to the directed acyclic graph; according to the recursion path, recursion is carried out to obtain comprehensive safety index data corresponding to the various characteristic information; determining a risk rate according to the comprehensive safety index data;
and when the risk rate exceeds a set threshold value, detecting the request information and the various kinds of characteristic information to obtain a detection result.
In an optional manner, the method further comprises:
if the detection result represents that malicious programs exist in the request information and the various feature information, refusing to respond to the request information;
and sending the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information to a cloud server, so that the cloud server performs associated storage on the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information.
In an optional manner, the method further comprises:
defining the category stored in association and the identification information corresponding to the category as an abnormal identification;
sending an abnormal identifier classification request to the cloud server so that the cloud server classifies the abnormal identifiers stored in association according to the abnormal identifier classification request to obtain a classification result;
receiving the classification result returned by the cloud server;
the enabling the cloud server to classify the abnormal identifiers stored in association with the abnormal identifier classification request to obtain a classification result includes:
enabling the cloud server to obtain convolution kernel parameters of x reference convolution kernels of the neural network, wherein x is a positive integer;
the cloud server obtains y groups of mask tensors of the neural network, wherein y is a positive integer, each group of mask tensors in the y groups of mask tensors is composed of a plurality of mask tensors, the number of bits occupied when elements in the y groups of mask tensors are stored is smaller than the number of bits occupied when elements in convolution kernel parameters in x reference convolution kernels are stored, and each reference convolution kernel in the x reference convolution kernels corresponds to one group of mask tensors in the y groups of mask tensors;
enabling the cloud server to perform Hadamard product operation on each reference convolution kernel in the x reference convolution kernels and a group of corresponding mask tensors of each reference convolution kernel in the y groups of mask tensors to obtain a plurality of sub-convolution kernels;
enabling the cloud server to carry out convolution processing on the abnormal identification according to the plurality of sub-convolution kernels to obtain a plurality of convolution characteristic graphs;
and enabling the cloud server to classify the abnormal identifications according to the plurality of convolution characteristic graphs to obtain the classification result.
In an optional manner, the method further comprises:
when the risk rate does not exceed the set threshold, searching target data matched with the request information in personal data stored in the terminal equipment according to the request information;
encrypting the target data to obtain encrypted data, wherein the target data is provided with a use duration;
sending the encrypted data to an internet financial platform;
receiving feedback information sent by the Internet financial platform; the feedback information is generated when the Internet financial platform decrypts the encrypted data to obtain the target data;
and starting timing by taking the moment of receiving the feedback information as a first moment to obtain accumulated time length, and sending a forced destruction instruction to the internet financial platform when the accumulated time length reaches the service time length so that the internet financial platform carries out forced destruction on the target data.
In an alternative mode, the internet financial platform performs the first encryption process by:
encrypting the preorder information flow of the data information flow to obtain preorder information flow ciphertext;
performing first interleaving encryption processing on the preamble information ciphertext and the remaining information streams except the preamble information stream in the data information stream to obtain an interleaving encryption ciphertext;
performing second interleaving encryption processing on the preorder information stream ciphertext and the interleaving encryption ciphertext to obtain a request instruction;
the decrypting the request instruction to obtain the request information and the multiple kinds of characteristic information includes:
performing a first reverse interleaving process on the request instruction to obtain the preorder information stream ciphertext and the interleaving and ciphertext;
performing second inverse interleaving processing on the interleaved ciphertext to obtain the residual information stream, and performing decryption processing on the preamble information encrypted text to obtain the preamble information stream;
and determining the request information and the various feature information according to the preamble information flow and the residual information flow.
The embodiment of the invention provides a personal data sharing device for an internet financial platform, which is applied to terminal equipment in communication connection with the internet financial platform, and comprises the following components:
a request instruction receiving module, configured to receive a request instruction, where the request instruction is generated by the internet financial platform performing a first encryption process on a data information stream, and the data information stream is obtained by the internet financial platform encapsulating request information and multiple feature information corresponding to the internet financial platform;
the decryption module is used for decrypting the request instruction to obtain the request information and the various feature information;
the risk rate determining module is used for determining the hierarchical relationship of each kind of characteristic information in the various kinds of characteristic information and generating a logic topology according to each hierarchical relationship and each kind of characteristic information; the logical topology comprises a characteristic information combination set and a logical path between each characteristic information combination in the characteristic information combination set; determining an initial characteristic information combination in the characteristic information combination set, and splicing all characteristic information combinations in the characteristic information combination set according to the initial characteristic information combination to form a directed acyclic graph; determining a recursion path according to the directed acyclic graph; according to the recursion path, recursion is carried out to obtain comprehensive safety index data corresponding to the various characteristic information; determining a risk rate according to the comprehensive safety index data;
and the detection result obtaining module is used for detecting the request information and the various kinds of characteristic information when the risk ratio exceeds a set threshold value to obtain a detection result.
In an optional manner, the apparatus further comprises: an association storage module to:
if the detection result represents that malicious programs exist in the request information and the various feature information, refusing to respond to the request information;
and sending the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information to a cloud server, so that the cloud server performs associated storage on the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information.
In an optional manner, the apparatus further comprises: a classification module to:
defining the category stored in association and the identification information corresponding to the category as an abnormal identification;
sending an abnormal identifier classification request to the cloud server so that the cloud server classifies the abnormal identifiers stored in association according to the abnormal identifier classification request to obtain a classification result;
receiving the classification result returned by the cloud server;
the enabling the cloud server to classify the abnormal identifiers stored in association with the abnormal identifier classification request to obtain a classification result includes:
enabling the cloud server to obtain convolution kernel parameters of x reference convolution kernels of the neural network, wherein x is a positive integer;
the cloud server obtains y groups of mask tensors of the neural network, wherein y is a positive integer, each group of mask tensors in the y groups of mask tensors is composed of a plurality of mask tensors, the number of bits occupied when elements in the y groups of mask tensors are stored is smaller than the number of bits occupied when elements in convolution kernel parameters in x reference convolution kernels are stored, and each reference convolution kernel in the x reference convolution kernels corresponds to one group of mask tensors in the y groups of mask tensors;
enabling the cloud server to perform Hadamard product operation on each reference convolution kernel in the x reference convolution kernels and a group of corresponding mask tensors of each reference convolution kernel in the y groups of mask tensors to obtain a plurality of sub-convolution kernels;
enabling the cloud server to carry out convolution processing on the abnormal identification according to the plurality of sub-convolution kernels to obtain a plurality of convolution characteristic graphs;
and enabling the cloud server to classify the abnormal identifications according to the plurality of convolution characteristic graphs to obtain the classification result.
The embodiment of the invention provides terminal equipment, which comprises a processor, a memory and a bus, wherein the memory and the bus are connected with the processor; wherein, the processor and the memory complete mutual communication through the bus; the processor is configured to invoke the program instructions in the memory to perform the above-described personal data sharing method for an internet financial platform.
An embodiment of the present invention provides a storage medium having a program stored thereon, the program implementing the above-described personal data sharing method for an internet financial platform when executed by a processor.
The personal data sharing method, the personal data sharing device and the terminal equipment for the internet financial platform provided by the embodiment of the invention can decrypt a received request instruction to obtain request information and various kinds of characteristic information corresponding to the internet financial platform, then determine a risk rate according to the request information and the various kinds of characteristic information, and detect the request information and the various kinds of characteristic information when the risk rate exceeds a set threshold value to obtain a detection result. Under the condition that the number of the internet financial platforms is large, the number of the request instructions received by the terminal equipment is increased, and by the method, the request information and the various types of characteristic information corresponding to each request instruction do not need to be detected, so that the CPU time slice resources of the terminal equipment are effectively saved, the terminal equipment can fully utilize the limited time slice resources to detect the request information and the various types of characteristic information of the request instructions corresponding to the risk rate exceeding the set threshold value, the detection efficiency is improved, and the personal data sharing process of the user is prevented from being influenced by the occupation of the excessive CPU time slice resources.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a block diagram illustrating a personal data sharing system for an internet financial platform according to an embodiment of the present invention.
Fig. 2 is a flowchart of a personal data sharing method for an internet financial platform according to an embodiment of the present invention.
Fig. 3 is a functional unit block diagram of a personal data sharing device for an internet financial platform according to an embodiment of the present invention.
Fig. 4 is a block diagram of an apparatus according to an embodiment of the present invention.
Icon:
100-personal data sharing system for internet financial platform;
101-a terminal device; 1011-a processor; 1012-memory; 1013-a bus;
102-internet financial platform;
103-cloud server;
20-personal data sharing means for internet financial platforms; 21-request instruction receiving module; 22-a decryption module; 23-a risk determination module; 24-a detection result obtaining module; 25-an associative memory module; 26-Classification Module.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment of the invention provides a personal data sharing method, a personal data sharing device and terminal equipment for an internet financial platform, which are used for solving the technical problems that the existing detection method is low in detection efficiency and seriously affects the personal data sharing process of a user.
In order to solve the technical problems, an embodiment of the invention provides a personal data sharing method, a personal data sharing device and a terminal device for an internet financial platform, and the general idea is as follows:
receiving a request instruction, wherein the request instruction is generated by the internet financial platform through first encryption processing on a data information stream, and the data information stream is obtained by the internet financial platform through packaging request information and various feature information corresponding to the internet financial platform; decrypting the request instruction to obtain the request information and the multiple kinds of characteristic information; determining the hierarchical relationship of each kind of feature information in the multiple kinds of feature information, and generating a logic topology according to each hierarchical relationship and each kind of feature information; the logical topology comprises a characteristic information combination set and a logical path between each characteristic information combination in the characteristic information combination set; determining an initial characteristic information combination in the characteristic information combination set, and splicing all characteristic information combinations in the characteristic information combination set according to the initial characteristic information combination to form a directed acyclic graph; determining a recursion path according to the directed acyclic graph; according to the recursion path, recursion is carried out to obtain comprehensive safety index data corresponding to the various characteristic information; determining a risk rate according to the comprehensive safety index data; and when the risk rate exceeds a set threshold value, detecting the request information and the various kinds of characteristic information to obtain a detection result.
Therefore, the request information and the multi-type characteristic information corresponding to each request instruction do not need to be detected, the CPU time slice resources of the terminal equipment are effectively saved, the terminal equipment can fully utilize the limited time slice resources to detect the request information and the multi-type characteristic information of the request instruction corresponding to the risk rate exceeding the set threshold value, the detection efficiency is improved, and the influence on the personal data sharing process of the user due to the fact that the excessive CPU time slice resources are occupied is avoided.
In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.
As shown in fig. 1, the personal data sharing system 100 for the internet financial platform includes a terminal device 101, an internet financial platform 102, and a cloud server 103, where the terminal device 101 is respectively connected to the internet financial platform 102 and the cloud server 103 in a communication manner, in this embodiment, the number of the internet financial platforms 102 may be multiple, and for convenience of subsequent description, in this embodiment, one internet financial platform 102 is taken as an example for description.
Referring to fig. 2, a flowchart of a personal data sharing method for an internet financial platform according to an embodiment of the present invention is provided, where the method is applied to the terminal device 101 in fig. 1, and the method may include the following steps:
and S21, receiving a request instruction.
And S22, decrypting the request command to obtain the request information and the various feature information.
S23, determining the hierarchical relationship of each kind of characteristic information in the multiple kinds of characteristic information, and generating a logic topology according to each hierarchical relationship and each kind of characteristic information; the logical topology comprises a characteristic information combination set and a logical path between each characteristic information combination in the characteristic information combination set; determining an initial characteristic information combination in the characteristic information combination set, and splicing all characteristic information combinations in the characteristic information combination set according to the initial characteristic information combination to form a directed acyclic graph; determining a recursion path according to the directed acyclic graph; according to the recursion path, carrying out recursion to obtain comprehensive safety index data corresponding to various characteristic information; and determining the risk rate according to the comprehensive safety index data.
And S24, when the risk ratio exceeds the set threshold value, detecting the request information and the multiple kinds of characteristic information to obtain a detection result.
Under the condition that the number of the internet financial platforms is large, the number of the request instructions received by the terminal device is increased, and through S21-S24, if the risk rate is determined to exceed the set threshold, the request information and the multi-type characteristic information corresponding to the request instructions are detected, so that the request information and the multi-type characteristic information corresponding to each request instruction do not need to be detected, CPU time slice resources of the terminal device are effectively saved, the terminal device can fully utilize the limited time slice resources to detect the request information and the multi-type characteristic information of the request instructions corresponding to the risk rate exceeding the set threshold, the detection efficiency is improved, and further the phenomenon that the personal data sharing process of a user is influenced due to the fact that the excessive CPU time slice resources are occupied is avoided.
In S24, the setting threshold may be set according to actual conditions, and is not limited herein.
In S21, the request command is generated by the internet financial platform performing a first encryption process on the data information stream, and the data information stream is obtained by the internet financial platform encapsulating the request information and the various feature information corresponding to the internet financial platform.
Specifically, the internet financial platform performs the first encryption processing by:
s211, encrypting the preorder information flow of the data information flow to obtain preorder information flow ciphertext.
S212, the first interleaving encryption processing is carried out on the preamble information ciphertext and the rest information flow except the preamble information flow in the data information flow, and an interleaving encryption ciphertext is obtained.
S213, the preorder information flow ciphertext and the interweaving encrypted ciphertext are subjected to second interweaving encryption processing to obtain a request instruction.
In the field of internet finance, timeliness is very important, and by means of the encryption method, the computation complexity of encryption can be effectively reduced, time delay is reduced, and the interaction efficiency of an internet finance platform and terminal equipment is improved.
In S22, the request instruction is decrypted to obtain request information and various feature information, which specifically include the following:
s221, the first inverse interleaving processing is carried out on the request command to obtain a preorder information flow ciphertext and an interleaving and ciphertext.
S222, carrying out second inverse interleaving processing on the interleaved ciphertext to obtain a residual information stream, and carrying out decryption processing on the preamble information encrypted text to obtain a preamble information stream.
S223, according to the preamble information flow and the residual information flow, determining the request information and the various feature information.
Through S221-S223, the operation complexity of the terminal device in the process of decrypting the request instruction can be effectively reduced, so that the terminal device can be ensured to efficiently acquire the request information and various feature information on the premise of ensuring the transmission security of the request instruction, and the efficiency of determining the comprehensive safety index data is further improved.
Through S23, the recursion path can be gradually determined based on the various characteristic information, and then the comprehensive safety index data corresponding to the various characteristic information is obtained by recursion, so that the comprehensive safety index data can be accurately determined based on the various characteristic information, and the comprehensive safety index data is obtained based on the recursion path, so that the complexity of operation is effectively reduced, and the efficiency of determining the comprehensive safety index data can be improved.
On the basis, the personal data sharing method for the internet financial platform shown in fig. 2 further includes:
and S251, if the detection result represents that the request information and the various feature information contain the malicious program, refusing to respond to the request information.
And S252, the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information are sent to the cloud server, so that the cloud server performs associated storage on the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information.
Through S251-S252, the identification information of the Internet financial platform with the malicious program in the request instruction and the category of the malicious program can be stored in an associated manner, the later-stage association analysis of the identification information of the Internet financial platform and the category of the malicious program is facilitated, and therefore it is determined which category of malicious program is easy to invade different Internet financial platforms, a data basis is further provided for subsequent wind control and security protection strategies of the Internet financial platform, the success rate of malicious program invasion in the interaction process of the Internet financial platform and the terminal device is reduced, and the personal information sharing safety in the interaction process of the terminal device and the Internet financial platform is ensured.
Further, with the construction of the wind control and security protection strategy of the internet financial platform, after the terminal device receives the request instruction corresponding to the internet financial platform, it can be determined that the risk rate of the internet financial platform does not exceed a set threshold value, so that the request information and the various types of characteristic information in the request instruction do not need to be detected, with the increase of the internet financial platforms constructing the wind control and security protection strategy, the request information and the various types of characteristic information detected by the terminal device can be reduced, so that the detection efficiency is further improved, more CPU time slice resources of the terminal device can be used for the personal data sharing process, and the timeliness of the personal data sharing process is improved.
On the basis of S251-S252, the personal data sharing method for the Internet financial platform further comprises the following steps:
and S261, defining the category stored in the association mode and the identification information corresponding to the category as an abnormal identification.
S262, sending an abnormal identifier classification request to the cloud server, so that the cloud server classifies the abnormal identifiers stored in association with each other according to the abnormal identifier classification request, and obtains a classification result.
And S263, receiving the classification result returned by the cloud server.
In S262, the cloud server classifies the abnormal identifier stored in association with the abnormal identifier classification request to obtain a classification result, which specifically includes the following contents:
s2621, the cloud server obtains convolution kernel parameters of x reference convolution kernels of the neural network, where x is a positive integer.
S2622, enabling the cloud server to obtain y sets of mask tensors of the neural network, where y is a positive integer, each set of mask tensors in the y sets of mask tensors is composed of a plurality of mask tensors, a number of bits occupied by elements in the y sets of mask tensors when stored is smaller than a number of bits occupied by elements in convolution kernel parameters in x reference convolution kernels when stored, and each reference convolution kernel in the x reference convolution kernels corresponds to one set of mask tensors in the y sets of mask tensors;
s2623, the cloud server performs hadamard product operations on each reference convolution kernel in the x reference convolution kernels and a group of mask tensors corresponding to each reference convolution kernel in the y group of mask tensors, so as to obtain a plurality of sub-convolution kernels.
And S2624, enabling the cloud server to perform convolution processing on the abnormal identifications respectively according to the plurality of sub-convolution kernels to obtain a plurality of convolution characteristic graphs.
And S2625, enabling the cloud server to classify the abnormal identifications according to the plurality of convolution feature maps to obtain a classification result.
Through S261-S263 and S2621-S2624, the storage overhead of a classification neural network deployed in a cloud server can be effectively reduced, so that the cloud server can rapidly and efficiently classify a plurality of abnormal identifications, the terminal equipment can rapidly and efficiently acquire classification results, and analysis and mining are performed based on the classification results.
On the basis, the personal data sharing method for the Internet financial platform further comprises the following steps:
and S271, when the risk rate does not exceed the set threshold, searching target data matched with the request information in the personal data stored in the terminal equipment according to the request information.
And S272, encrypting the target data to obtain encrypted data, wherein the target data is provided with a use duration.
And S273, sending the encrypted data to the Internet financial platform.
S274, receiving feedback information sent by the Internet financial platform; the feedback information is generated when the internet financial platform decrypts the encrypted data to obtain the target data.
And S275, timing is started by taking the moment of receiving the feedback information as a first moment, the accumulated time length is obtained, and when the accumulated time length reaches the service time length, a forced destruction instruction is sent to the Internet financial platform, so that the Internet financial platform carries out forced destruction on the target data.
Through S271-S275, potential safety hazards such as data leakage caused by long-time sharing of shared personal data can be avoided, and due to the fact that the target data is set to be long in use time, the efficiency of the internet financial platform can be improved when the internet financial platform carries out service processing according to the target data, and therefore the interaction efficiency between the internet financial platform and the terminal device is improved.
The embodiment of the invention provides a personal data sharing device 20 for an internet financial platform. Fig. 3 is a functional unit block diagram of a personal data sharing device 20 for an internet financial platform according to an embodiment of the present invention, where the personal data sharing device 20 for the internet financial platform includes:
a request instruction receiving module 21, configured to receive a request instruction, where the request instruction is generated by the internet financial platform performing a first encryption process on a data information stream, and the data information stream is obtained by the internet financial platform encapsulating request information and multiple feature information corresponding to the internet financial platform.
And the decryption module 22 is configured to decrypt the request instruction to obtain the request information and the multiple kinds of feature information.
A risk determining module 23, configured to determine a hierarchical relationship of each of the plurality of types of feature information, and generate a logical topology according to each hierarchical relationship and each type of feature information; the logical topology comprises a characteristic information combination set and a logical path between each characteristic information combination in the characteristic information combination set; determining an initial characteristic information combination in the characteristic information combination set, and splicing all characteristic information combinations in the characteristic information combination set according to the initial characteristic information combination to form a directed acyclic graph; determining a recursion path according to the directed acyclic graph; according to the recursion path, recursion is carried out to obtain comprehensive safety index data corresponding to the various characteristic information; and determining the risk rate according to the comprehensive safety index data.
And a detection result obtaining module 24, configured to, when the risk ratio exceeds a set threshold, detect the request information and the multiple kinds of feature information, and obtain a detection result.
In an optional manner, the apparatus further comprises: an association storage module 25, configured to:
if the detection result represents that malicious programs exist in the request information and the various feature information, refusing to respond to the request information;
and sending the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information to a cloud server, so that the cloud server performs associated storage on the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information.
In an optional manner, the apparatus further comprises: a classification module 26 for:
defining the category stored in association and the identification information corresponding to the category as an abnormal identification;
sending an abnormal identifier classification request to the cloud server so that the cloud server classifies the abnormal identifiers stored in association according to the abnormal identifier classification request to obtain a classification result;
receiving the classification result returned by the cloud server;
the enabling the cloud server to classify the abnormal identifiers stored in association with the abnormal identifier classification request to obtain a classification result includes:
enabling the cloud server to obtain convolution kernel parameters of x reference convolution kernels of the neural network, wherein x is a positive integer;
the cloud server obtains y groups of mask tensors of the neural network, wherein y is a positive integer, each group of mask tensors in the y groups of mask tensors is composed of a plurality of mask tensors, the number of bits occupied when elements in the y groups of mask tensors are stored is smaller than the number of bits occupied when elements in convolution kernel parameters in x reference convolution kernels are stored, and each reference convolution kernel in the x reference convolution kernels corresponds to one group of mask tensors in the y groups of mask tensors;
enabling the cloud server to perform Hadamard product operation on each reference convolution kernel in the x reference convolution kernels and a group of corresponding mask tensors of each reference convolution kernel in the y groups of mask tensors to obtain a plurality of sub-convolution kernels;
enabling the cloud server to carry out convolution processing on the abnormal identification according to the plurality of sub-convolution kernels to obtain a plurality of convolution characteristic graphs;
and enabling the cloud server to classify the abnormal identifications according to the plurality of convolution characteristic graphs to obtain the classification result.
In an alternative form, the decryption module 22 is configured to:
performing a first reverse interleaving process on the request instruction to obtain the preorder information stream ciphertext and the interleaving and ciphertext;
performing second inverse interleaving processing on the interleaved ciphertext to obtain the residual information stream, and performing decryption processing on the preamble information encrypted text to obtain the preamble information stream;
and determining the request information and the various feature information according to the preamble information flow and the residual information flow.
The personal data sharing device 20 for the internet financial platform comprises a processor and a memory, wherein the request instruction receiving module 21, the decryption module 22, the risk rate determining module 23, the detection result obtaining module 24, the association storage module 25, the classification module 26 and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, the detection efficiency is improved by adjusting kernel parameters, and the influence on the personal data sharing process of a user is avoided.
An embodiment of the present invention provides a storage medium having a program stored thereon, the program implementing the personal data sharing method for an internet financial platform when being executed by a processor.
The embodiment of the invention provides a processor, which is used for running a program, wherein the program executes the personal data sharing method for the Internet financial platform during running.
An embodiment of the present invention provides a terminal device, as shown in fig. 4, a terminal device 101 includes at least one processor 1011, and at least one memory 1012 and a bus connected to the processor 1011; the processor 1011 and the memory 1012 complete communication with each other via the bus 1013; the processor 1011 is configured to invoke program instructions in the memory 1012 to perform the personal data sharing method described above for the internet financial platform. The terminal device 101 herein may be a server, a PC, a PAD, a handset, etc.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device:
receiving a request instruction, wherein the request instruction is generated by the internet financial platform through first encryption processing on a data information stream, and the data information stream is obtained by the internet financial platform through packaging request information and various feature information corresponding to the internet financial platform;
decrypting the request instruction to obtain the request information and the multiple kinds of characteristic information;
determining the hierarchical relationship of each kind of feature information in the multiple kinds of feature information, and generating a logic topology according to each hierarchical relationship and each kind of feature information; the logical topology comprises a characteristic information combination set and a logical path between each characteristic information combination in the characteristic information combination set; determining an initial characteristic information combination in the characteristic information combination set, and splicing all characteristic information combinations in the characteristic information combination set according to the initial characteristic information combination to form a directed acyclic graph; determining a recursion path according to the directed acyclic graph; according to the recursion path, recursion is carried out to obtain comprehensive safety index data corresponding to the various characteristic information; determining a risk rate according to the comprehensive safety index data;
and when the risk rate exceeds a set threshold value, detecting the request information and the various kinds of characteristic information to obtain a detection result.
In an optional manner, the method further comprises:
if the detection result represents that malicious programs exist in the request information and the various characteristic information, refusing to respond to the request information;
and sending the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information to a cloud server, so that the cloud server performs associated storage on the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information.
In an optional manner, the method further comprises:
defining the category stored in association and the identification information corresponding to the category as an abnormal identification;
sending an abnormal identifier classification request to the cloud server so that the cloud server classifies the abnormal identifiers stored in association according to the abnormal identifier classification request to obtain a classification result;
receiving the classification result returned by the cloud server;
the enabling the cloud server to classify the abnormal identifiers stored in association with the abnormal identifier classification request to obtain a classification result includes:
enabling the cloud server to obtain convolution kernel parameters of x reference convolution kernels of the neural network, wherein x is a positive integer;
the cloud server obtains y groups of mask tensors of the neural network, wherein y is a positive integer, each group of mask tensors in the y groups of mask tensors is composed of a plurality of mask tensors, the number of bits occupied when elements in the y groups of mask tensors are stored is smaller than the number of bits occupied when elements in convolution kernel parameters in x reference convolution kernels are stored, and each reference convolution kernel in the x reference convolution kernels corresponds to one group of mask tensors in the y groups of mask tensors;
enabling the cloud server to perform Hadamard product operation on each reference convolution kernel in the x reference convolution kernels and a group of corresponding mask tensors of each reference convolution kernel in the y groups of mask tensors to obtain a plurality of sub-convolution kernels;
enabling the cloud server to carry out convolution processing on the abnormal identification according to the plurality of sub-convolution kernels to obtain a plurality of convolution characteristic graphs;
and enabling the cloud server to classify the abnormal identifications according to the plurality of convolution characteristic graphs to obtain the classification result.
In an optional manner, the method further comprises:
when the risk rate does not exceed the set threshold, searching target data matched with the request information in personal data stored in the terminal equipment according to the request information;
encrypting the target data to obtain encrypted data, wherein the target data is provided with a use duration;
sending the encrypted data to an internet financial platform;
receiving feedback information sent by the Internet financial platform; the feedback information is generated when the Internet financial platform decrypts the encrypted data to obtain the target data;
and starting timing by taking the moment of receiving the feedback information as a first moment to obtain accumulated time length, and sending a forced destruction instruction to the internet financial platform when the accumulated time length reaches the service time length so that the internet financial platform carries out forced destruction on the target data.
In an alternative mode, the internet financial platform performs the first encryption process by:
encrypting the preorder information flow of the data information flow to obtain preorder information flow ciphertext;
performing first interleaving encryption processing on the preamble information ciphertext and the remaining information streams except the preamble information stream in the data information stream to obtain an interleaving encryption ciphertext;
performing second interleaving encryption processing on the preorder information stream ciphertext and the interleaving encryption ciphertext to obtain a request instruction;
the decrypting the request instruction to obtain the request information and the multiple kinds of characteristic information includes:
performing a first reverse interleaving process on the request instruction to obtain the preorder information stream ciphertext and the interleaving and ciphertext;
performing second inverse interleaving processing on the interleaved ciphertext to obtain the residual information stream, and performing decryption processing on the preamble information encrypted text to obtain the preamble information stream;
and determining the request information and the various feature information according to the preamble information flow and the residual information flow.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The 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), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A personal data sharing method for an internet financial platform, applied to a terminal device communicatively connected to the internet financial platform, the method comprising:
receiving a request instruction, wherein the request instruction is generated by the internet financial platform through first encryption processing on a data information stream, and the data information stream is obtained by the internet financial platform through packaging request information and various feature information corresponding to the internet financial platform;
decrypting the request instruction to obtain the request information and the multiple kinds of characteristic information;
determining the hierarchical relationship of each kind of feature information in the multiple kinds of feature information, and generating a logic topology according to each hierarchical relationship and each kind of feature information; the logical topology comprises a characteristic information combination set and a logical path between each characteristic information combination in the characteristic information combination set; determining an initial characteristic information combination in the characteristic information combination set, and splicing all characteristic information combinations in the characteristic information combination set according to the initial characteristic information combination to form a directed acyclic graph; determining a recursion path according to the directed acyclic graph; according to the recursion path, recursion is carried out to obtain comprehensive safety index data corresponding to the various characteristic information; determining a risk rate according to the comprehensive safety index data;
and when the risk rate exceeds a set threshold value, detecting the request information and the various kinds of characteristic information to obtain a detection result.
2. The method of claim 1, further comprising:
if the detection result represents that malicious programs exist in the request information and the various characteristic information, refusing to respond to the request information;
and sending the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information to a cloud server, so that the cloud server performs associated storage on the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information.
3. The method of claim 2, further comprising:
defining the category stored in association and the identification information corresponding to the category as an abnormal identification;
sending an abnormal identifier classification request to the cloud server so that the cloud server classifies the abnormal identifiers stored in association according to the abnormal identifier classification request to obtain a classification result;
receiving the classification result returned by the cloud server;
the enabling the cloud server to classify the abnormal identifiers stored in association with the abnormal identifier classification request to obtain a classification result includes:
enabling the cloud server to obtain convolution kernel parameters of x reference convolution kernels of the neural network, wherein x is a positive integer;
the cloud server obtains y groups of mask tensors of the neural network, wherein y is a positive integer, each group of mask tensors in the y groups of mask tensors is composed of a plurality of mask tensors, the number of bits occupied when elements in the y groups of mask tensors are stored is smaller than the number of bits occupied when elements in convolution kernel parameters in x reference convolution kernels are stored, and each reference convolution kernel in the x reference convolution kernels corresponds to one group of mask tensors in the y groups of mask tensors;
enabling the cloud server to perform Hadamard product operation on each reference convolution kernel in the x reference convolution kernels and a group of corresponding mask tensors of each reference convolution kernel in the y groups of mask tensors to obtain a plurality of sub-convolution kernels;
enabling the cloud server to carry out convolution processing on the abnormal identification according to the plurality of sub-convolution kernels to obtain a plurality of convolution characteristic graphs;
and enabling the cloud server to classify the abnormal identifications according to the plurality of convolution characteristic graphs to obtain the classification result.
4. The method of claim 1, further comprising:
when the risk rate does not exceed the set threshold, searching target data matched with the request information in personal data stored in the terminal equipment according to the request information;
encrypting the target data to obtain encrypted data, wherein the target data is provided with a use duration;
sending the encrypted data to an internet financial platform;
receiving feedback information sent by the Internet financial platform; the feedback information is generated when the Internet financial platform decrypts the encrypted data to obtain the target data;
and starting timing by taking the moment of receiving the feedback information as a first moment to obtain accumulated time length, and sending a forced destruction instruction to the internet financial platform when the accumulated time length reaches the service time length so that the internet financial platform carries out forced destruction on the target data.
5. The method of claim 1, wherein the internet financial platform performs the first encryption process by:
encrypting the preorder information flow of the data information flow to obtain preorder information flow ciphertext;
performing first interleaving encryption processing on the preamble information ciphertext and the remaining information streams except the preamble information stream in the data information stream to obtain an interleaving encryption ciphertext;
performing second interleaving encryption processing on the preorder information stream ciphertext and the interleaving encryption ciphertext to obtain a request instruction;
the decrypting the request instruction to obtain the request information and the multiple kinds of characteristic information includes:
performing a first reverse interleaving process on the request instruction to obtain the preorder information stream ciphertext and the interleaving and ciphertext;
performing second inverse interleaving processing on the interleaved ciphertext to obtain the residual information stream, and performing decryption processing on the preamble information encrypted text to obtain the preamble information stream;
and determining the request information and the various feature information according to the preamble information flow and the residual information flow.
6. A personal data sharing apparatus for an internet financial platform, applied to a terminal device communicatively connected to the internet financial platform, the apparatus comprising:
a request instruction receiving module, configured to receive a request instruction, where the request instruction is generated by the internet financial platform performing a first encryption process on a data information stream, and the data information stream is obtained by the internet financial platform encapsulating request information and multiple feature information corresponding to the internet financial platform;
the decryption module is used for decrypting the request instruction to obtain the request information and the various feature information;
the risk rate determining module is used for determining the hierarchical relationship of each kind of characteristic information in the various kinds of characteristic information and generating a logic topology according to each hierarchical relationship and each kind of characteristic information; the logical topology comprises a characteristic information combination set and a logical path between each characteristic information combination in the characteristic information combination set; determining an initial characteristic information combination in the characteristic information combination set, and splicing all characteristic information combinations in the characteristic information combination set according to the initial characteristic information combination to form a directed acyclic graph; determining a recursion path according to the directed acyclic graph; according to the recursion path, recursion is carried out to obtain comprehensive safety index data corresponding to the various characteristic information; determining a risk rate according to the comprehensive safety index data;
and the detection result obtaining module is used for detecting the request information and the various kinds of characteristic information when the risk ratio exceeds a set threshold value to obtain a detection result.
7. The apparatus of claim 6, further comprising: an association storage module to:
if the detection result represents that malicious programs exist in the request information and the various feature information, refusing to respond to the request information;
and sending the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information to a cloud server, so that the cloud server performs associated storage on the category corresponding to the malicious program and the identification information of the Internet financial platform carried in the request information.
8. The apparatus of claim 7, further comprising: a classification module to:
defining the category stored in association and the identification information corresponding to the category as an abnormal identification;
sending an abnormal identifier classification request to the cloud server so that the cloud server classifies the abnormal identifiers stored in association according to the abnormal identifier classification request to obtain a classification result;
receiving the classification result returned by the cloud server;
the enabling the cloud server to classify the abnormal identifiers stored in association with the abnormal identifier classification request to obtain a classification result includes:
enabling the cloud server to obtain convolution kernel parameters of x reference convolution kernels of the neural network, wherein x is a positive integer;
the cloud server obtains y groups of mask tensors of the neural network, wherein y is a positive integer, each group of mask tensors in the y groups of mask tensors is composed of a plurality of mask tensors, the number of bits occupied when elements in the y groups of mask tensors are stored is smaller than the number of bits occupied when elements in convolution kernel parameters in x reference convolution kernels are stored, and each reference convolution kernel in the x reference convolution kernels corresponds to one group of mask tensors in the y groups of mask tensors;
enabling the cloud server to perform Hadamard product operation on each reference convolution kernel in the x reference convolution kernels and a group of corresponding mask tensors of each reference convolution kernel in the y groups of mask tensors to obtain a plurality of sub-convolution kernels;
enabling the cloud server to carry out convolution processing on the abnormal identification according to the plurality of sub-convolution kernels to obtain a plurality of convolution characteristic graphs;
and enabling the cloud server to classify the abnormal identifications according to the plurality of convolution characteristic graphs to obtain the classification result.
9. A terminal device comprising a processor, and a memory and a bus connected to the processor; wherein, the processor and the memory complete mutual communication through the bus; the processor is configured to invoke program instructions in the memory to perform the personal data sharing method for an internet financial platform of any of the above claims 1-5.
10. A storage medium having stored thereon a program which, when executed by a processor, implements the personal data sharing method for an internet financial platform according to any one of claims 1 to 5.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110047044A1 (en) * | 2001-05-30 | 2011-02-24 | William Wright | Method and Apparatus for Evaluating Fraud Risk in an Electronic Commerce Transaction |
| CN103870642A (en) * | 2014-03-05 | 2014-06-18 | 湖州师范学院 | Complex supply network robust performance analyzing method based on topological structure |
| CN107172028A (en) * | 2017-05-09 | 2017-09-15 | 泰豪科技股份有限公司 | A kind of fieldbus data sharing method and device |
| CN107315834A (en) * | 2017-07-12 | 2017-11-03 | 广东奡风科技股份有限公司 | A kind of ETL work flow analysis methods based on breadth-first search |
| CN107844575A (en) * | 2017-11-06 | 2018-03-27 | 国家计算机网络与信息安全管理中心 | A kind of personal data shared system and method for internet financial platform |
| CN108512854A (en) * | 2018-04-09 | 2018-09-07 | 平安科技（深圳）有限公司 | Institutional information method for safety monitoring, device, computer equipment and storage medium |
| CN108615158A (en) * | 2018-03-22 | 2018-10-02 | 平安科技（深圳）有限公司 | Risk checking method, device, mobile terminal and storage medium |
| CN109684109A (en) * | 2018-12-27 | 2019-04-26 | 北京天融信网络安全技术有限公司 | A kind of data-sharing systems and data sharing method |
| CN110147925A (en) * | 2019-04-10 | 2019-08-20 | 阿里巴巴集团控股有限公司 | A kind of Application of risk decision method, device, equipment and system |
| CN110188795A (en) * | 2019-04-24 | 2019-08-30 | 华为技术有限公司 | Image classification method, data processing method and device |
-
2019
- 2019-11-27 CN CN201911180010.7A patent/CN110995688B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110047044A1 (en) * | 2001-05-30 | 2011-02-24 | William Wright | Method and Apparatus for Evaluating Fraud Risk in an Electronic Commerce Transaction |
| CN103870642A (en) * | 2014-03-05 | 2014-06-18 | 湖州师范学院 | Complex supply network robust performance analyzing method based on topological structure |
| CN107172028A (en) * | 2017-05-09 | 2017-09-15 | 泰豪科技股份有限公司 | A kind of fieldbus data sharing method and device |
| CN107315834A (en) * | 2017-07-12 | 2017-11-03 | 广东奡风科技股份有限公司 | A kind of ETL work flow analysis methods based on breadth-first search |
| CN107844575A (en) * | 2017-11-06 | 2018-03-27 | 国家计算机网络与信息安全管理中心 | A kind of personal data shared system and method for internet financial platform |
| CN108615158A (en) * | 2018-03-22 | 2018-10-02 | 平安科技（深圳）有限公司 | Risk checking method, device, mobile terminal and storage medium |
| CN108512854A (en) * | 2018-04-09 | 2018-09-07 | 平安科技（深圳）有限公司 | Institutional information method for safety monitoring, device, computer equipment and storage medium |
| CN109684109A (en) * | 2018-12-27 | 2019-04-26 | 北京天融信网络安全技术有限公司 | A kind of data-sharing systems and data sharing method |
| CN110147925A (en) * | 2019-04-10 | 2019-08-20 | 阿里巴巴集团控股有限公司 | A kind of Application of risk decision method, device, equipment and system |
| CN110188795A (en) * | 2019-04-24 | 2019-08-30 | 华为技术有限公司 | Image classification method, data processing method and device |
Non-Patent Citations (1)
| Title |
|---|
| 曾园: "银行操作风险贝叶斯网络量化控制研究", 《金融监管研究》 * |
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