CN114092140B - Data processing method, device, computer equipment and storage medium - Google Patents
Data processing method, device, computer equipment and storage medium Download PDFInfo
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- CN114092140B CN114092140B CN202111357362.2A CN202111357362A CN114092140B CN 114092140 B CN114092140 B CN 114092140B CN 202111357362 A CN202111357362 A CN 202111357362A CN 114092140 B CN114092140 B CN 114092140B
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract
The present disclosure provides a data processing method, apparatus, computer device, and storage medium, where the method includes: acquiring first behavior data generated by a target user group on a first service platform; performing first mask processing on the first line of data to obtain first mask data; sending the first mask data to a second server; and acquiring third behavior data returned by the second server, and carrying out fusion processing on the first behavior data and the third behavior data to obtain a fusion processing result. The fusion processing result obtained by the embodiment of the disclosure is consistent with the result obtained by directly using the first behavior data and the second behavior data to perform matrix product calculation, and meanwhile, the two parties are not aware of the behavior data of the other party, so that the privacy of the behavior data is ensured; and the interactive data in the calculation process are all plaintext data, so that the problem of increased calculation amount caused by encryption and decryption processes and the problem of reduced data transmission efficiency caused by increased ciphertext data amount are avoided.
Description
Technical Field
The disclosure relates to the technical field of computers, and in particular relates to a data processing method, a data processing device, computer equipment and a storage medium.
Background
At present, the data privacy protection technology is related to the project of multiparty participation, and can ensure that common calculation targets are completed under the condition of ensuring that the data privacy is not revealed in the multiparty participation process.
As services rapidly develop to derive more complex service scenarios, the complex service scenarios need to ensure data privacy in the process of performing matrix multiplication on data, however, the current common solution is based on cryptography.
In the matrix product calculation, generally, one party a homomorphic encrypts own private data, then sends the encrypted private data to the other party B for matrix product calculation, and returns a matrix product result to the party a, so that the party a obtains the matrix product result and decrypts the matrix product result.
The encryption and decryption processes can increase the calculated amount and affect the calculation efficiency; and the data volume of the ciphertext formed after encryption is increased relative to the data volume before encryption, so that the data communication duration is prolonged, and the data transmission efficiency is reduced.
Disclosure of Invention
The embodiment of the disclosure at least provides a data processing method, a data processing device, computer equipment and a storage medium.
In a first aspect, an embodiment of the present disclosure provides a data processing method, which is applied to a first service end, including:
acquiring first behavior data, wherein the first behavior data represents behavior data generated by a target user group on a first service platform on the first service platform;
performing first mask processing on the first line of data to obtain first mask data;
the first mask data are sent to a second service end, the first mask data are used for indicating the second service end to conduct second mask processing on the first mask data to obtain second mask data, and second behavior data generated by the target user group on a second service platform are processed based on the second mask data to obtain third behavior data;
acquiring the third behavior data returned by the second server, and performing fusion processing on the first behavior data and the third behavior data to obtain a fusion processing result; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
In an alternative embodiment, performing a first masking process on the first row of data to obtain first mask data includes:
Performing orthogonal matrix decomposition on the first row of data to obtain zero-space data orthogonal to the first row of data;
and selecting a first preset number of element data from the null space data, and combining the first preset number of element data to obtain first mask data.
In an optional implementation manner, the fusing processing of the first behavior data and the third behavior data to obtain a fusion processing result includes:
performing matrix transposition on the first behavior data to obtain fourth behavior data;
and calculating the product of the third behavior data and the fourth behavior data to obtain the fusion processing result.
In an alternative embodiment, after determining the fusion processing result, the method further includes:
and sending the fusion processing result to the second server.
In a second aspect, an embodiment of the present disclosure further provides a data processing method, applied to a second server, including:
receiving first mask data sent by a first server; the first mask data are obtained by performing first mask processing on first line data by the first server; the first behavior data represents behavior data generated on the first service platform by a target user group on the first service platform;
Performing second mask processing on the first mask data to obtain second mask data;
processing second behavior data generated on a second service platform by the target user group based on the second mask data to obtain third behavior data;
the third behavior data is sent to the first server side, and the third behavior data is used for indicating the first server side to perform fusion processing based on the first behavior data and the third behavior data, so that a fusion processing result is obtained; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
In an alternative embodiment, performing a second masking process on the first masking data to obtain second masking data includes:
and selecting a second preset number of element data from the first mask data, and combining the second preset number of element data to obtain second mask data.
In an optional implementation manner, processing the second behavior data generated by the target user group on the second service platform based on the second mask data to obtain third behavior data includes:
performing matrix transposition on the second mask data to obtain third mask data;
And processing second behavior data generated on a second service platform by the target user group based on the second mask data and the third mask data to obtain third behavior data.
In a third aspect, an embodiment of the present disclosure further provides a data processing apparatus, including:
the acquisition module is used for acquiring the first row of data; the first behavior data represents behavior data generated on the first service platform by a target user group on the first service platform;
the first processing module is used for carrying out first mask processing on the first line of data to obtain first mask data;
the first sending module is used for sending the first mask data to a second service end, the first mask data is used for indicating the second service end to conduct second mask processing on the first mask data to obtain second mask data, and processing second behavior data generated by the target user group on a second service platform based on the second mask data to obtain third behavior data;
the second processing module is used for acquiring the third behavior data returned by the second server and carrying out fusion processing on the first behavior data and the third behavior data to obtain a fusion processing result; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
In a fourth aspect, an embodiment of the present disclosure further provides a data processing apparatus, including:
the receiving module is used for receiving first mask data sent by the first service end; the first mask data are obtained by performing first mask processing on first line data by the first server; the first behavior data represents behavior data generated on the first service platform by a target user group on the first service platform;
the third processing module is used for carrying out second mask processing on the first mask data to obtain second mask data;
the fourth processing module is used for processing second behavior data generated on a second service platform by the target user group based on the second mask data to obtain third behavior data;
the third sending module is used for sending the third behavior data to the first server, and the third behavior data is used for indicating the first server to perform fusion processing based on the first behavior data and the third behavior data to obtain a fusion processing result; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
In a fifth aspect, embodiments of the present disclosure further provide a computer device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication via the bus when the computer device is running, the machine-readable instructions when executed by the processor performing the steps of the first aspect, or any of the possible implementations of the first aspect.
In a sixth aspect, embodiments of the present disclosure further provide a computer device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication via the bus when the computer device is running, the machine-readable instructions when executed by the processor performing the steps of the second aspect, or any of the possible implementations of the second aspect.
In a seventh aspect, the presently disclosed embodiments also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the first aspect, or any of the possible implementations of the first aspect.
In an eighth aspect, the presently disclosed embodiments also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the second aspect, or any of the possible implementations of the second aspect.
According to the data processing method provided by the embodiment of the disclosure, first behavior data generated by a target user group on a first service platform on the first service platform is obtained, then first masking processing is carried out on the first behavior data to obtain first masking data, then the first masking data is sent to a second service end, the first masking data is used for indicating the second service end to carry out second masking processing on the first masking data to obtain second masking data, the second behavior data generated by the target user group on the second service platform is processed based on the second masking data to obtain third behavior data, finally third behavior data returned by the second service end is obtained, and fusion processing is carried out on the first behavior data and the third behavior data to obtain a fusion processing result. The fusion processing result obtained by the embodiment of the disclosure is consistent with a result obtained by directly performing matrix product calculation based on the first behavior data and the second behavior data; meanwhile, the first service end cannot know the second behavior data in the second service end, and the second service end cannot know the first behavior data in the first service end, so that the accuracy of a calculation result is ensured, and meanwhile, the privacy of the behavior data of the two parties is also ensured. In addition, the interactive data in the calculation process are all plaintext data, so that compared with the prior art, the problem of calculation amount increase caused by encryption and decryption processes is avoided, and the problem of data transmission efficiency reduction caused by ciphertext data amount increase is avoided.
The foregoing objects, features and advantages of the disclosure will be more readily apparent from the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for the embodiments are briefly described below, which are incorporated in and constitute a part of the specification, these drawings showing embodiments consistent with the present disclosure and together with the description serve to illustrate the technical solutions of the present disclosure. It is to be understood that the following drawings illustrate only certain embodiments of the present disclosure and are therefore not to be considered limiting of its scope, for the person of ordinary skill in the art may admit to other equally relevant drawings without inventive effort.
FIG. 1 illustrates a flow chart of a data processing method provided by an embodiment of the present disclosure;
FIG. 2 illustrates a flow chart of another data processing method provided by an embodiment of the present disclosure;
FIG. 3 shows a schematic diagram of a data processing apparatus provided by an embodiment of the present disclosure;
FIG. 4 shows a schematic diagram of another data processing apparatus provided by an embodiment of the present disclosure;
FIG. 5 illustrates a schematic diagram of a computer device provided by an embodiment of the present disclosure;
fig. 6 shows a schematic diagram of another computer device provided by an embodiment of the present disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, but not all embodiments. The components of the embodiments of the present disclosure, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present disclosure provided in the accompanying drawings is not intended to limit the scope of the disclosure, as claimed, but is merely representative of selected embodiments of the disclosure. All other embodiments, which can be made by those skilled in the art based on the embodiments of this disclosure without making any inventive effort, are intended to be within the scope of this disclosure.
In the matrix product calculation process, typically, party a homomorphic encrypts each element in its own private data, and then sends the encrypted private data to party B. And directly using the encrypted private data and the private data of the party B to perform matrix product calculation to obtain a matrix product result, and returning the matrix product result to the party A. And the party A decrypts the data according to the homomorphic key to obtain a matrix product result.
In the above process, the encryption of the private data and the decryption of the matrix product result are required, which increases the amount of calculation and results in a decrease in the calculation efficiency. And the ciphertext data volume formed after encryption is increased relative to the data volume before encryption, so that the data which can be transmitted in a few minutes can be transmitted, the transmission can be completed in a few hours, and the data transmission efficiency is reduced.
Based on this, the embodiment of the disclosure provides a data processing method, first obtain first behavior data generated by a target user group on a first service platform on the first service platform, then perform first mask processing on the first behavior data to obtain first mask data, then send the first mask data to a second service end, where the first mask data is used to instruct the second service end to perform second mask processing on the first mask data to obtain second mask data, process the second behavior data of the target user group on the second service platform based on the second mask data to obtain third behavior data, finally obtain third behavior data returned by the second service end, and perform fusion processing on the first behavior data and the third behavior data to obtain a fusion processing result. The fusion processing result obtained by the embodiment of the disclosure is consistent with a result obtained by directly performing matrix product calculation based on the first behavior data and the second behavior data; meanwhile, the first service end cannot know the second behavior data in the second service end, and the second service end cannot know the first behavior data in the first service end, so that the accuracy of a calculation result is ensured, and meanwhile, the privacy of the behavior data of the two parties is also ensured. In addition, the interactive data in the calculation process are all plaintext data, so that compared with the prior art, the problem of calculation amount increase caused by encryption and decryption processes is avoided, and the problem of data transmission efficiency reduction caused by ciphertext data amount increase is avoided.
The present invention is directed to a method for manufacturing a semiconductor device, and a semiconductor device manufactured by the method.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
For the sake of understanding the present embodiment, first, a detailed description will be given of a data processing method disclosed in an embodiment of the present disclosure, where an execution body of the data processing method provided in the embodiment of the present disclosure is generally a computer device with a certain computing capability.
The data processing method provided by the embodiment of the disclosure is mainly applicable to a scene that two computing participants need to perform privacy protection-oriented matrix product computation. For example A, B both enterprises wish to calculate the average amount of consumption of the A company's loyalty user group in the B company in a privacy-oriented manner. Two computing participants in the scene need to provide respective privacy data (namely whether the target user group is the loyalty user group of the A company and the consumption amount of the target user group in the B company) and then use the privacy data of the two parties to perform matrix product computation to obtain a matrix product result (namely the average consumption amount of the loyalty user group of the A company in the B company), and ensure that the two parties cannot know the privacy data of the other party.
In the embodiment of the disclosure, after defining any one of the two computing participants as a first computing participant, the other computing participant is a second computing participant. The computing server used by the first computing participant is a first server, and the computing server used by the second computing participant is a second server. The first service end and the second service end are used for performing matrix product calculation.
The data processing method provided by the embodiment of the present disclosure is described below by taking the execution body as the first server as an example.
Referring to fig. 1, a flowchart of a data processing method according to an embodiment of the disclosure is shown, where the method includes S101 to S104, where:
s101: acquiring first row data; the first behavior data characterizes behavior data generated on the first service platform by a target user group on the first service platform.
In an embodiment of the present disclosure, the first behavior data may include behavior attribute information, such as a behavior type, behavior content, and the like, of a target user group on the first service platform on which the first behavior occurs. The first behavior occurring between different user groups may be the same or different.
The first behavior of the target user group on the first service platform may include a plurality of types. For example, the first service platform is an e-commerce platform for selling first and second commodities, and the behavior of the target user group occurring on the first service platform may include a behavior of purchasing the first commodity and a behavior of purchasing the second commodity.
In the embodiment of the present disclosure, the first behavior data may be represented in a matrix form, for example, user 1 spends 500 yuan for purchasing a commodity and 30000 yuan for purchasing a commodity; user 2 spends 1500 yuan for the first commodity and 10000 yuan for the second commodity, then the first behavior data represented in matrix form may be:
s102: and performing first mask processing on the first line of data to obtain first mask data.
Here, the first mask data may be obtained by performing a first mask process according to the first line data, but the second server cannot reversely derive the first line data according to the first mask data.
In one embodiment, the first row of data may be subjected to orthogonal matrix decomposition to obtain zero-space data orthogonal to the first row of data; and selecting a first preset number of element data from the null space data, and combining the first preset number of element data to obtain first mask data.
There are many ways to perform orthogonal matrix decomposition on the first row of data. For example, an orthogonal triangular decomposition method, i.e., a QR decomposition method, may be employed, with which the real (complex) non-singular matrix a may be decomposed into the product of the orthogonal matrix Q and the real (complex) non-singular upper triangular matrix R, i.e., a=qr. For another example, singular value (Singular Value Decomposition, SVD) decomposition may be employed, by which the real (complex) non-singular matrix A may be decomposed into a first orthogonal matrix U, a diagonal matrix Σ, and a second orthogonal matrix V T I.e. a=uΣv T 。
When the QR decomposition method is adopted, a column element with a preset column number in the orthogonal matrix Q may be selected as a zero-space matrix orthogonal to the first row of data. When the SVD decomposition method is adopted, a preset number of rows of row elements in the second orthogonal matrix can be used as a zero-space matrix orthogonal to the first row of data.
Here, the first behavior data may be defined as X 1 The corresponding zero-space matrix is
Wherein R is n Is a set of n real numbers, n is a positive integer.
Then, from the zero-space matrix N (X 1 ) Randomly selecting a first preset number i of element data Z i Wherein Z is i ∈R n I is a positive integer.
Next, i element data Z can be directly used i And performing random arrangement to obtain first mask data. The first mask data may be represented in a matrix form. For example, the first mask data obtained after the random arrangement may be: z= [ Z ] 1 Z 2 Z 3 ...Z i ]∈R n×i Or z= [ Z 2 Z 3 Z i ...Z 1 ]∈R n×i Or z= [ Z i Z 2 Z 3 ...Z 1 ]∈R n×i Etc. I element data Z may also be used i And performing random linear arrangement to obtain first mask data. For example, the first mask data obtained after the random alignment may be: z= [ Z ] 1 +Z 3 Z 2 +2Z i Z 3 ...Z i ]∈R n×i Or z= [ Z 2 +4Z 3 Z 3 3Z 1 +Z i ...Z 1 ]∈R n ×i Etc.
Here, due to Z i Is the first row of data X 1 Thus, from the nature of the zero-space matrix, Z can be derived i And the first line of data X 1 Is vertical, i.e. X 1 T Z i The first mask data Z generated as described above is also the first line data X =0 1 Vertical, i.e. X 1 T Z＝0。
S103: and sending the first mask data to a second server, wherein the first mask data is used for indicating the second server to perform second mask processing on the first mask data to obtain second mask data, and processing second behavior data generated by the target user group on a second service platform based on the second mask data to obtain third behavior data.
Since the column space coslp (Z) of the first mask data Z is the first row data X 1 Is a space coslp (X) 1 ) ⊥ Is (are) subspaces, i.e
Therefore, even if the first mask data Z is sent to the second server, the second server cannot obtain all coslp (X 1 ) ⊥ More cannot be known about coslp (X 1 ) Therefore, the second server cannot accurately and reversely push the first behavior data X 1 Thus the first mask data Z canIs capable of ensuring the first row of data X 1 Privacy of (2).
Here, the second behavior data may be defined as X 2 The second behavior data may also be represented in a matrix form. The second behavior data may include behavior attribute information of the second behavior of the target user group on the second service platform.
The first mask data may be used to instruct the second server to randomly select a second preset number of element data from the first mask data Z, and then combine the second preset number of element data to obtain the second mask data
Wherein j is a positive integer, and j is less than or equal to i. Here, the selected second preset number of element data may be a subset randomly selected from the element set of the first mask data Z, the subset including the second preset number of element data.
The first mask data may also be used to instruct the second server to base the second mask data on 
For the second behavior data X 2 And processing to obtain third behavior data. Specifically, the third behavior data obtained may be +.>
Wherein I is n ∈R n×n Is a unitary matrix, and alpha is a random real number.
S104: acquiring the third behavior data returned by the second server, and performing fusion processing on the first behavior data and the third behavior data to obtain a fusion processing result; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
After the third behavior data M returned by the second server is obtained, although the first server knows the first mask data Z, the second mask data included in the third behavior data M cannot be obtained according to the first mask data Z
(second mask data->
Is obtained by randomly selecting a second preset number of element data combinations from the first mask data Z), and the random real number α contained in the third behavior data M cannot be known, so that the first server cannot reversely derive the second behavior data X from the third behavior data M 2 The third behavior data M thus enables the second behavior data X to be guaranteed 2 Privacy of (2).
In determining the fusion processing result, the first row of data X can be processed 1 Performing matrix transposition processing to obtain fourth behavior data X 1 T The method comprises the steps of carrying out a first treatment on the surface of the Then calculate the third behavior data M and the fourth behavior data X 1 T And obtaining a fusion processing result.
Specifically, the fusion processing result may be:
here, due to the second mask data +>
Is obtained by randomly selecting a second preset number of combinations of element data from the first mask data Z, and from the foregoing, there is a first row of data X 1 And a zero space matrix N (X 1 ) Any element Z in (3) i Vertical, i.e. X 1 T Z i =0, so there is a first row of data X 1 And second mask data->
Perpendicular, i.e.)>
Furthermore there is->
Further, it can be derived that
That is, the final fusion processing result P is consistent with the matrix multiplication result obtained by directly performing matrix product calculation using the first behavior data and the second behavior data through the first mask processing of the first service end and the second mask processing of the second service end.
After the fusion processing result is obtained, the fusion processing result can be sent to the second server side, so that the second server side can share the fusion processing result.
In the above embodiment, the first service end and the second service end not only obtain accurate fusion processing results, but also the first service end fails to know the second behavior data of the second service end, and the second service end also fails to know the first behavior data of the first service end, thereby realizing privacy protection oriented matrix product calculation.
The data processing method provided by the embodiment of the present disclosure is described below by taking the execution body as the second server as an example. Referring to fig. 2, a flowchart of a data processing method according to an embodiment of the disclosure is shown, where the method includes S201 to S204, where:
s201: receiving first mask data sent by a first server; the first mask data are obtained by performing first mask processing on first line data by the first server; the first behavior data characterizes behavior data generated on the first service platform by a target user group on the first service platform.
Here, the process of obtaining the first mask data Z may refer to the process of S101, which is not described herein.
S202: and performing second mask processing on the first mask data to obtain second mask data.
Here, the second preset number of element data may be selected from the first mask data, and the second preset number of element data may be combined to obtain the second mask data.
The selected second preset number of element data may be a subset randomly selected from the element set of the first mask data Z, the subset including the second preset number of element data. Then combining the second preset number of element data to obtain second mask data 
Wherein j is a positive integer, and j is less than or equal to i. In a specific implementation, the second preset number of element data may be arbitrarily combined.
For example, the element set of the first mask data Z is { Z 1 Z 2 Z 3 Z 4 Z 5 Z 6 Z can be chosen here 1 Z 3 Z 6 And then { Z } 1 Z 3 Z 6 Re-combining to obtain second mask data
Etc.
S203: and processing second behavior data generated on a second service platform by the target user group based on the second mask data to obtain third behavior data.
In one embodiment, the second mask data may be subjected to matrix transposition to obtain third mask data; and then processing the second behavior data generated by the target user group on the second service platform based on the second mask data and the third mask data to obtain third behavior data. Specifically, for the second mask data
After the matrix transposition process, the third mask data +.>
Then, based on the second mask data +.>
And a third maskCode data->
For the second behavior data X 2 And processing to obtain third behavior data. The third behavior data may be defined herein as M.
To add the second behavior data X 2 Optionally introducing random real number alpha, and calculating to obtain third behavior data as 
Wherein I is n ∈R n×n Is an identity matrix.
S204: the third behavior data is sent to the first server side, and the third behavior data is used for indicating the first server side to perform fusion processing based on the first behavior data and the third behavior data, so that a fusion processing result is obtained; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
After the third behavior data is sent to the first service end, the first service end still cannot obtain the second mask data Z-according to the first mask data Z (because the second mask data
Is obtained by randomly selecting a second preset number of element data combinations from the first mask data Z, and the random real number alpha cannot be known, so that the first server cannot reversely deduce the second behavior data X from the third behavior data M 2 The third behavior data M can thus guarantee the second behavior data X 2 Privacy of (2).
The first server side is used for carrying out fusion processing based on the first behavior data and the third behavior data, and determining a fusion processing result. The process of obtaining the fusion processing data may refer to the process of S104, which is not described herein. And finally, the second server side can also receive the fusion processing result obtained by the first server side, so that the sharing of the fusion processing result is realized.
In the above embodiment, the first service end and the second service end not only obtain accurate fusion processing results, but also the first service end fails to know the second behavior data of the second service end, and the second service end also fails to know the first behavior data of the first service end, thereby realizing privacy protection oriented matrix product calculation.
The data processing method provided by the embodiment of the present disclosure will be described in detail below.
First, the first behavior data of the target user group on the first service platform can be defined as X A ∈R 5×2 The second behavior data of the target user group on the second service platform is X B ∈R 5×3 . First behavior data X A The method comprises the following steps:
| 0 | 1 | |
| 0 | 1.00000 | 0.80000 |
| 1 | -3.00000 | 2.00000 |
| 2 | 11.00000 | 1.50000 |
| 3 | 5.00000 | 3.00000 |
| 4 | 5.00000 | 1.00000 |
second behavior data X B The method comprises the following steps:
| 0 | 1 | 2 | |
| 0 | 0.50000 | 2.00000 | 3.00000 |
| 1 | 1.00000 | 1.50000 | 0.10000 |
| 2 | -2.00000 | 1.00000 | -1.00000 |
| 3 | 0.50000 | 1.80000 | 1.9000 |
| 4 | 1.00000 | 1.00000 | -1.00000 |
the first service end is used for first line data X A QR decomposition is performed to obtain X A =qr, where q∈r 5×5 R epsilon R 5×2 Wherein Q is an orthogonal matrix, and R is a non-singular upper triangular matrix. Specifically, the orthogonal matrix Q is:
| 0 | 1 | 2 | 3 | 4 | |
| 0 | -0.07433 | -0.18510 | -0.75817 | -0.50133 | -0.36613 |
| 1 | 0.22299 | -0.74348 | 0.41696 | -0.46222 | 0.10007 |
| 2 | -0.81762 | 0.11872 | 0.38517 | -0.29905 | -0.28215 |
| 3 | -0.37165 | -0.63030 | -0.17978 | 0.64661 | -0.11901 |
| 4 | -0.37165 | -0.03996 | -0.26579 | -0.16578 | 0.87302 |
the non-singular upper triangular matrix R is:
| 0 | 1 | |
| 0 | -13.45362 | -2.3651 |
| 1 | 0.00000 | -3.38782 |
| 2 | 0.00000 | 0.00000 |
| 3 | 0.00000 | 0.00000 |
| 4 | 0.00000 | 0.00000 |
column elements in columns 3, 4, 5 (denoted as Q3, Q4, Q5) of the orthogonal matrix Q may constitute the first row of data X, depending on the nature of the QR decomposition method A Is a set of bases of the zero-space matrix of (b). Then, based on the column elements in columns 3, 4, 5, the first mask data z= [ q3,2q4,3q5, q3+q4, q4+q5, q3+q5 can be obtained ]∈R 5×6 The method specifically comprises the following steps:
the first server may then send the first mask data Z to the second server.
The second server may generate a random real number α=15.5, and randomly select a column element { Z ] of Z from the first mask data Z 1 ,...,Z 6 Subset { Z } 1 ,Z 3 ,Z 6 }. Then according to { Z 1 ,Z 3 ,Z 6 Second mask data may be constructed
The second server may be configured to store the second mask data
Random real number alpha and second behavior data X B Calculation of
Wherein I is n ∈R 5×5 The unit matrix is specifically as follows:
| 0 | 1 | 2 | 3 | 4 | |
| 0 | -46.20328 | 19.02125 | -8.08958 | -13.39800 | 52.04848 |
| 1 | 19.02125 | -7.23514 | 0.62400 | 5.21770 | -15.33683 |
| 2 | -8.08958 | 0.62400 | -12.56981 | -3.13375 | 34.97965 |
| 3 | -13.39800 | 5.21770 | -3.13376 | -2.86043 | 16.56491 |
| 4 | 52.04848 | -15.33583 | 34.97965 | 16.56491 | -112.13134 |
then, calculate the third behavior data
The method comprises the following steps:
| 0 | 1 | 2 | |
| 0 | 67.44826 | -44.03218 | -206.12... |
| 1 | -11.69948 | 21.86983 | 80.96571 |
| 2 | 55.13161 | 1.52592 | -52.57032 |
| 3 | 19.92091 | -10.68706 | -58.53819 |
| 4 | -163.11978 | 33.75837 | 263.236... |
then, the second server may send the third behavior data M to the first server, and the first server calculates the third behavior data M and the first behavior data X A And obtaining a matrix product result:
the method comprises the following steps:
| 0 | 1 | 2 | |
| 0 | -17.00000 | 22.50000 | -3.80000 |
| 1 | 1.90000 | 12.50000 | 5.80000 |
the second server may send the matrix multiplier result to the first server.
It will be appreciated by those skilled in the art that in the above-described method of the specific embodiments, the written order of steps is not meant to imply a strict order of execution but rather should be construed according to the function and possibly inherent logic of the steps.
Based on the same inventive concept, the embodiments of the present disclosure further provide a data processing device corresponding to the data processing method, and since the principle of solving the problem by the device in the embodiments of the present disclosure is similar to that of the data processing method in the embodiments of the present disclosure, the implementation of the device may refer to the implementation of the method, and the repetition is omitted.
Corresponding to the data processing method in fig. 1, the embodiment of the disclosure further provides a data processing device. Referring to fig. 3, a schematic architecture diagram of a data processing apparatus according to an embodiment of the disclosure is provided, where the apparatus includes: an acquisition module 301, a first processing module 302, a first sending module 303, and a second processing module 304; wherein, the liquid crystal display device comprises a liquid crystal display device,
an acquisition module 301, configured to acquire a first row of data; the first behavior data represents behavior data generated on the first service platform by a target user group on the first service platform;
a first processing module 302, configured to perform a first mask processing on the first line of data to obtain first mask data;
a first sending module 303, configured to send the first mask data to a second server, where the first mask data is used to instruct the second server to perform a second mask process on the first mask data to obtain second mask data, and process, based on the second mask data, second behavior data generated by the target user group in a second service platform to obtain third behavior data;
the second processing module 304 is configured to obtain the third behavior data returned by the second server, and perform fusion processing on the first behavior data and the third behavior data to obtain a fusion processing result; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
In an alternative embodiment, the first processing module 302 is specifically configured to:
performing orthogonal matrix decomposition on the first row of data to obtain zero-space data orthogonal to the first row of data;
and selecting a first preset number of element data from the null space data, and combining the first preset number of element data to obtain first mask data.
In an alternative embodiment, the second processing module 304 is specifically configured to:
performing matrix transposition on the first behavior data to obtain fourth behavior data;
and calculating the product of the third behavior data and the fourth behavior data to obtain the fusion processing result.
In an alternative embodiment, the apparatus further comprises:
and the second sending module is used for sending the fusion processing result to the second server.
Corresponding to the data processing method in fig. 2, the embodiment of the disclosure further provides a data processing device. Referring to fig. 4, there is shown a schematic architecture diagram of another data processing apparatus according to an embodiment of the disclosure, where the apparatus includes: a receiving module 401, a third processing module 402, a fourth processing module 403, and a third transmitting module 404; wherein, the liquid crystal display device comprises a liquid crystal display device,
A receiving module 401, configured to receive first mask data sent by a first service end; the first mask data are obtained by performing first mask processing on first line data by the first server; the first behavior data represents behavior data generated on the first service platform by a target user group on the first service platform;
a third processing module 402, configured to perform a second masking process on the first mask data to obtain second mask data;
a fourth processing module 403, configured to process, based on the second mask data, second behavior data generated by the target user group on a second service platform to obtain third behavior data;
a third sending module 404, configured to send the third behavior data to the first service end, where the third behavior data is used to instruct the first service end to perform fusion processing based on the first behavior data and the third behavior data to obtain a fusion processing result; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
In an alternative embodiment, the third processing module 402 is specifically configured to: and selecting a second preset number of element data from the first mask data, and combining the second preset number of element data to obtain second mask data.
In an alternative embodiment, the fourth processing module 403 is specifically configured to: performing matrix transposition on the second mask data to obtain third mask data;
and processing second behavior data generated on a second service platform by the target user group based on the second mask data and the third mask data to obtain third behavior data.
The process flow of each module in the apparatus and the interaction flow between the modules may be described with reference to the related descriptions in the above method embodiments, which are not described in detail herein.
Based on the same technical concept, the embodiment of the disclosure also provides a computer device corresponding to the data processing method in fig. 1. As shown in fig. 5, a schematic structural diagram of a computer device 500 according to an embodiment of the disclosure includes a processor 501, a memory 502, and a bus 503. The memory 502 is configured to store execution instructions, including a memory 5021 and an external memory 5022; the memory 5021 is also referred to as an internal memory, and is used for temporarily storing operation data in the processor 501 and data exchanged with an external memory 5022 such as a hard disk, the processor 501 exchanges data with the external memory 5022 through the memory 5021, and when the computer device 500 is running, the processor 501 and the memory 502 communicate through the bus 503, so that the processor 501 executes the following instructions:
Acquiring first behavior data, wherein the first behavior data represents behavior data generated by a target user group on a first service platform on the first service platform;
performing first mask processing on the first line of data to obtain first mask data;
the first mask data are sent to a second service end, the first mask data are used for indicating the second service end to conduct second mask processing on the first mask data to obtain second mask data, and second behavior data generated by the target user group on a second service platform are processed based on the second mask data to obtain third behavior data;
acquiring the third behavior data returned by the second server, and performing fusion processing on the first behavior data and the third behavior data to obtain a fusion processing result; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
Corresponding to the data processing method in fig. 2, the embodiment of the disclosure further provides a computer device. As shown in fig. 6, a schematic structural diagram of a computer device 600 according to an embodiment of the present disclosure includes a processor 601, a memory 602, and a bus 603. The memory 602 is used for storing execution instructions, including a memory 6021 and an external memory 6022; the memory 6021 is also referred to as an internal memory, and is used for temporarily storing operation data in the processor 601 and data exchanged with the external memory 6022 such as a hard disk, the processor 601 exchanges data with the external memory 6022 through the memory 6021, and when the computer device 600 operates, the processor 601 and the memory 602 communicate through the bus 603, so that the processor 601 executes the following instructions:
Receiving first mask data sent by a first server; the first mask data are obtained by performing first mask processing on first line data by the first server; the first behavior data represents behavior data generated on the first service platform by a target user group on the first service platform;
performing second mask processing on the first mask data to obtain second mask data;
processing second behavior data generated on a second service platform by the target user group based on the second mask data to obtain third behavior data;
the third behavior data is sent to the first server side, and the third behavior data is used for indicating the first server side to perform fusion processing based on the first behavior data and the third behavior data, so that a fusion processing result is obtained; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
The disclosed embodiments also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the data processing method described in the method embodiments above. Wherein the storage medium may be a volatile or nonvolatile computer readable storage medium.
Embodiments of the present disclosure further provide a computer program product, where the computer program product carries program code, where instructions included in the program code may be used to perform steps of a data processing method described in the foregoing method embodiments, and specifically reference may be made to the foregoing method embodiments, which are not described herein.
Wherein the above-mentioned computer program product may be realized in particular by means of hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied as a computer storage medium, and in another alternative embodiment, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK), or the like.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding process in the foregoing method embodiment for the specific working process of the apparatus described above, which is not described herein again. In the several embodiments provided in the present disclosure, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on such understanding, the technical solution of the present disclosure may be embodied in essence or a part contributing to the prior art or a part of the technical solution, or in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present disclosure. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present disclosure, and are not intended to limit the scope of the disclosure, but the present disclosure is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, it is not limited to the disclosure: any person skilled in the art, within the technical scope of the disclosure of the present disclosure, may modify or easily conceive changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features thereof; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the disclosure, and are intended to be included within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (7)
1. A data processing method, applied to a first service end, comprising:
acquiring first behavior data, wherein the first behavior data represents behavior data generated by a target user group on a first service platform on the first service platform;
Performing orthogonal matrix decomposition on the first row of data to obtain zero-space data orthogonal to the first row of data, selecting a first preset number of element data from the zero-space data, and combining the first preset number of element data to obtain first mask data;
the first mask data are sent to a second service end, the first mask data are used for indicating the second service end to select second preset number of element data from the first mask data, the second preset number of element data are combined to obtain second mask data, matrix transposition is conducted on the second mask data to obtain third mask data, and the second behavior data generated by the target user group on a second service platform are processed to obtain third behavior data based on the second mask data and the third mask data;
obtaining the third behavior data returned by the second server, performing matrix transposition on the first behavior data to obtain fourth behavior data, and calculating the product of the third behavior data and the fourth behavior data to obtain a fusion processing result; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
2. The method of claim 1, wherein after determining the fusion process result, the method further comprises:
and sending the fusion processing result to the second server.
3. The data processing method is characterized by being applied to a second service end and comprising the following steps:
receiving first mask data sent by a first server; the first mask data is obtained by performing orthogonal matrix decomposition on first row data by the first server to obtain zero-space data orthogonal to the first row data, selecting a first preset number of element data from the zero-space data, and combining the first preset number of element data; the first behavior data represents behavior data generated on the first service platform by a target user group on the first service platform;
selecting a second preset number of element data from the first mask data, and combining the second preset number of element data to obtain second mask data;
performing matrix transposition on the second mask data to obtain third mask data, and processing second behavior data generated by the target user group on a second service platform based on the second mask data and the third mask data to obtain third behavior data;
The third behavior data are sent to the first server side and are used for indicating the first server side to perform matrix transposition on the first behavior data to obtain fourth behavior data, and the product of the third behavior data and the fourth behavior data is calculated to obtain a fusion processing result; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
4. A data processing apparatus, comprising:
the acquisition module is used for acquiring the first row of data; the first behavior data represents behavior data generated on the first service platform by a target user group on the first service platform;
the first processing module is used for carrying out orthogonal matrix decomposition on the first row of data to obtain zero-space data orthogonal to the first row of data, selecting a first preset number of element data from the zero-space data, and combining the first preset number of element data to obtain first mask data;
the first sending module is used for sending the first mask data to a second server, the first mask data is used for indicating the second server to select a second preset number of element data from the first mask data, combining the second preset number of element data to obtain second mask data, performing matrix transposition on the second mask data to obtain third mask data, and processing second behavior data generated by the target user group on a second service platform to obtain third behavior data based on the second mask data and the third mask data;
The second processing module is used for acquiring the third behavior data returned by the second server, performing matrix transposition on the first behavior data to obtain fourth behavior data, and calculating the product of the third behavior data and the fourth behavior data to obtain a fusion processing result; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
5. A data processing apparatus, comprising:
the receiving module is used for receiving first mask data sent by the first service end; the first mask data is obtained by performing orthogonal matrix decomposition on first row data by the first server to obtain zero-space data orthogonal to the first row data, selecting a first preset number of element data from the zero-space data, and combining the first preset number of element data; the first behavior data represents behavior data generated on the first service platform by a target user group on the first service platform;
the third processing module is used for selecting second preset number of element data from the first mask data and combining the second preset number of element data to obtain second mask data;
The fourth processing module is used for performing matrix transposition processing on the second mask data to obtain third mask data, and processing second behavior data generated by the target user group on a second service platform based on the second mask data and the third mask data to obtain third behavior data;
the third sending module is used for sending the third behavior data to the first server, the third behavior data is used for indicating the first server to perform matrix transposition on the first behavior data to obtain fourth behavior data, and the product of the third behavior data and the fourth behavior data is calculated to obtain a fusion processing result; and the fusion processing result is used for representing the correlation between the first behavior data and the second behavior data.
6. A computer device, comprising: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory communicating over the bus when the computer device is running, the machine readable instructions when executed by the processor performing the steps of the data processing method according to any one of claims 1 to 2 or the steps of the data processing method according to claim 3.
7. A computer-readable storage medium, characterized in that it has stored thereon a computer program which, when run by a processor, performs the steps of the data processing method according to any of claims 1 to 2 or performs the steps of the data processing method according to claim 3.
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