CN114757696A - Data pricing system and method based on private calculation data exploration - Google Patents
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Abstract
The invention discloses a data pricing system and a method based on privacy calculation data exploration, which relate to the technical field of data transaction and comprise a data initialization module, an asset matching module, an asset value evaluation module, an asset pricing module and an asset consumption module; the data pricing system receives data input from the outside into the data initialization module, the output end of the data initialization module is connected with the input end of the asset matching module in a signal mode, the output end of the asset matching module is connected with the input end of the asset value evaluation module in a signal mode, the output end of the asset value evaluation module is connected with the input end of the asset pricing module in a signal mode, and the asset pricing module is in bidirectional signal connection with the asset consumption module. Under the addition of the privacy computing technology, the data can effectively realize the separation of the use right and the ownership, the asset pricing can be carried out based on the practical value of the data, meanwhile, the feedback condition of the data assets in the real consumption scene can be collected, and the data value and the pricing can be effectively corrected.
Description
Technical Field
The invention relates to the technical field of data transaction, in particular to a data pricing system and a data pricing method based on privacy calculation data exploration.
Background
Data trading has gradually become an activity in the marketplace. The premise of data transaction is that data pricing is carried out, and factors influencing the data pricing are more, including data varieties, time span, data depth, data integrity, data sample size, data instantaneity and the like. Meanwhile, the data value has great difference for different scenes of different customers; how to construct an effective and practical data value evaluation and pricing mechanism is urgent.
In the prior art, most data pricing only stays at a macroscopic level, for example: through a series of evaluation pricing such as accuracy and integrity of metadata of the big data, through reasonably establishing a hierarchical structure model and utilizing an AHP analysis method, the weight of each pricing strategy is obtained, and thus, the data assets are evaluated and priced; the data pricing modes basically evaluate the data before the data consumption, but the value of the data is often greatly different in different consumption scenes, which is a common problem in the existing data pricing.
In order to solve the above problems, we propose a data pricing system and method based on private computing data exploration.
Disclosure of Invention
In view of the deficiencies of the prior art, the present invention provides a data pricing system and method based on private computing data exploration to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: a data pricing system based on privacy calculation data exploration realizes data asset value assessment and pricing functions under the background of privacy security calculation, and comprises a data initialization module, an asset matching module, an asset value assessment module, an asset pricing module and an asset consumption module; the data pricing system receives data input from the outside into a data initialization module, the output end of the data initialization module is connected with the input end of an asset matching module in a signal mode, the output end of the asset matching module is connected with the input end of an asset value evaluation module in a signal mode, the output end of the asset value evaluation module is connected with the input end of an asset pricing module in a signal mode, and the asset pricing module is in bidirectional signal connection with an asset consumption module;
the data initialization module is used for initializing data needing pricing and endowing the data to be priced with a unique identifier in the whole data pricing system;
the asset matching module is used for obtaining a joint sample set based on the privacy of the data main key fields agreed by both trading parties;
the asset value evaluation module is used for evaluating the value indexes within the validity, the correlation and the contribution degree of the target field and the input fields of the two parties;
the asset pricing module is used for giving the valuation of each data asset;
and the asset consumption module is used for providing consumption data assets in a privacy calculation consumption scene.
Further optimizing the technical scheme, the data initialization module carries out desensitization processing on sensitive field names in the data, wherein fields including gender and age are replaced by x1 and x2 respectively, and updating frequency, effectiveness, coverage rate and a main key forming mode of the data are explained.
Further optimizing the technical scheme, the asset matching module is used for uploading own data by a data demand party, configuring intersection fields of the data of the two parties, designating the fields of a sample table after intersection and simultaneously supporting sampling strategies including random sampling and layered sampling; the asset matching module is used for the data provider to set a certain field in the data as a target column.
The technical scheme is further optimized, and the asset value evaluation module is used for supporting data preprocessing and characteristic engineering operation and supporting rapid calculation of relevant value evaluation indexes of data assets of a data provider.
The technical scheme is further optimized, value evaluation indexes supported by the asset value evaluation module comprise Shapley feature contribution degree alpha, feature multiple collinearity VIF value chi, feature IV importance degree delta, feature GINI importance degree epsilon, feature information entropy importance degree phi and feature correlation coefficient gamma, and the value evaluation indexes are used for providing multi-angle and comprehensive quantification of the effect and the efficacy of each feature in the scene.
Further optimizing the technical scheme, establishing a value evaluation function based on the value evaluation index, wherein the value evaluation function is shown as the following formula:
value＝ω1flabel(α,δ,ε,φ)+ω2goth er feature(χ,γ)+ω3zlabel(γ)+Constant
wherein f islabel、goth er featureAnd wlabelFor data asset value evaluation triplets, Constant is a value Constant.
Further optimizing the technical scheme, the data asset value evaluation triple further comprises the following specific contents:
flabel: the method comprises the following steps that Shapley feature contribution degree alpha, feature IV importance degree delta, feature GINI importance degree epsilon and feature information entropy importance degree phi are included, the indexes directly reflect the relation of target column label, and the larger the index value is, the more important the features are;
goth er feature: the method comprises a feature multiple collinearity VIF value χ, wherein two indexes of a feature correlation coefficient γ reflect the relationship between each feature and other features, and the index value is larger, so that the feature uniqueness is smaller, and the feature uniqueness is easier to replace;
wlabel: the feature correlation coefficient γ also reflects the importance of the relationship with the target column label, and this part of index information needs to be processed separately.
Further optimizing the technical scheme, each characteristic scene in the asset pricing module has corresponding market cost, such as: marketing guest obtaining cost, wind control fraud average cost and consumption loan risk cost, and combining scene cost and quantitative indexes of value evaluation, the valuation of each data asset can be given; the transaction trading value of the past data assets of the category is supported to be provided, and some fine adjustment can be made on the basis of valuation; supporting a gradient pricing strategy, supporting a pricing strategy according to calculation times and supporting a monthly payment pricing strategy according to the package year.
Further optimizing the technical scheme, the asset consumption module provides a place for data consumption for the platform, and provides data modeling capacity and data joint prediction capacity; the platform provides a complete process of asset shelving, asset verification and asset consumption, can audit the consistency of a scene in a verification stage and a scene in a consumption stage, ensures that the benefit of a data provider is guaranteed, can collect the feedback condition of the data asset in a real consumption scene, and effectively corrects the data value and pricing.
A data pricing method based on privacy calculation data exploration is sequentially operated based on the data pricing system based on privacy calculation data exploration, reasonable pricing is carried out through privacy deal solving, value evaluation, data pricing, data transaction and data consumption, and data pricing is effectively corrected according to the feedback condition of data assets in a real consumption scene.
Compared with the prior art, the invention provides a data pricing system and method based on private calculation data exploration, which have the following beneficial effects:
according to the data pricing system and method based on the privacy calculation data exploration, under the condition that the privacy calculation technology is added, the data can effectively achieve separation of the use right and the ownership, asset pricing can be conducted based on the practical value of the data, meanwhile, the feedback condition of the data assets in the real consumption scene can be collected, and the data value and pricing can be effectively corrected.
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FIG. 1 is a schematic structural diagram of a data pricing system based on privacy computing data exploration according to the present invention;
fig. 2 is a schematic flow chart of a data pricing method based on privacy computation data exploration according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b):
referring to fig. 1, a data pricing system based on privacy computation data exploration realizes data asset value assessment and pricing functions in the context of privacy security computation, and includes a data initialization module, an asset matching module, an asset value assessment module, an asset pricing module, and an asset consumption module; the data pricing system receives data input from the outside into the data initialization module, the output end of the data initialization module is connected with the input end of the asset matching module in a signal mode, the output end of the asset matching module is connected with the input end of the asset value evaluation module in a signal mode, the output end of the asset value evaluation module is connected with the input end of the asset pricing module in a signal mode, and the asset pricing module is in bidirectional signal connection with the asset consumption module. Under the addition of the privacy calculation technology, the data can effectively realize the separation of the use right and the ownership, the asset pricing can be carried out based on the practical value of the data, meanwhile, the feedback condition of the data assets in the real consumption scene can be collected, and the data value and the pricing can be effectively corrected.
The data initialization module is used for initializing data needing pricing and endowing the data to be priced with a unique identifier in the whole data pricing system.
Further, the data initialization module desensitizes sensitive field names in the data, wherein the fields including gender and age are replaced by x1 and x2 respectively, and the update frequency, effectiveness, coverage rate and main key composition mode of the data are described. The usual update frequency: real-time (below 500 milliseconds), near real-time (within 3 seconds), hourly, daily, weekly, monthly, yearly, etc.; the common primary key is generally taken from IMEI, IDFA, mobile phone number, identity card number and the like, and is encrypted by algorithms such as MD5, SHA256 and the like.
The asset matching module is used for obtaining a combined sample set based on data main key field privacy agreement between two trading parties, wherein a data demand party provides a target field, and the target field represents a specific scene of data consumption.
Further, the asset matching module is used for uploading own data by a data demand party, configuring an intersection field of the data of the two parties, designating a field of a sample table after intersection, and simultaneously supporting sampling strategies including random sampling and layered sampling; the asset matching module is used for a data provider to set a certain field in data as a target column.
The asset value evaluation module is used for evaluating the value indexes within the validity, the relevance and the contribution degree of a target field and input fields of two parties based on exploration operation, evaluating the relevant value indexes such as the relevance and multiple collinearity among the input fields, and synthesizing the relevant value indexes to give a comprehensive value evaluation index value. The value evaluation can be carried out on the basis of an AI model, the model is trained by algorithms such as common logistic regression or tree models, one part of the model is trained and the other part of the model is trained together, and the promotion degree of the model after the data assets are added is checked.
Furthermore, the asset value assessment module is used for supporting data preprocessing and characteristic engineering operations and rapidly calculating the relevant value assessment indexes of the data assets of the data provider. The value evaluation indexes supported by the asset value evaluation module comprise Shapley feature contribution degree alpha, feature multiple collinearity VIF value chi, feature IV importance degree delta, feature GINI importance degree epsilon, feature information entropy importance degree phi and feature correlation coefficient gamma, and are used for providing the effect and the efficacy of multi-angle and comprehensive quantification of each feature in the scene. Establishing a value evaluation function based on the value evaluation index, wherein the value evaluation function is shown as the following formula:
value＝ω1flabel(α,δ,ε,φ)+ω2goth er feature(χ,γ)+ω3zlabel(γ)+Constant
wherein f islabel、goth er featureAnd wlabelFor data asset value evaluation triplets, Constant is a value Constant.
Further, the data asset value evaluation triple further comprises the following specific contents:
flabel: including Shapley feature contribution degree alpha, feature IV importance degree delta, feature GINI importance degree epsilon, and featureThe information entropy importance degree phi is characterized, the indexes directly reflect the relationship of target column label, and the larger the index value is, the more important the characteristics are;
goth er feature: the method comprises the steps that a characteristic multiple collinearity VIF value chi is included, two indexes of a characteristic correlation coefficient gamma reflect the relation between each characteristic and other characteristics, and the index value is larger, so that the characteristic uniqueness is smaller, and the characteristic is easier to replace;
wlabel: the feature correlation coefficient γ also reflects the importance of the relationship with the target column label, and this part of index information is processed separately.
And the asset pricing module is used for giving the valuation of each data asset.
Further, each feature scenario in the asset pricing module has a corresponding market cost, such as: the marketing customer acquisition cost, the average cost of the wind control fraud and the cost of the loan consumption risk can be combined with the scene cost and the quantitative index of the value evaluation to give the valuation of each data asset; the transaction trading value of the original category data assets is supported to be provided, and some fine adjustment can be made on the basis of valuation; supporting a gradient pricing strategy, supporting a pricing strategy according to calculation times and supporting a monthly payment pricing strategy according to the package year.
After the data are completely transacted, the asset consumption module provides the consumption data assets in the privacy calculation consumption scene through the platform, and the consistency of the verification scene and the consumption scene is ensured.
Further, the asset consumption module provides a place for data consumption for the platform, and provides data modeling capability and data joint prediction capability; the platform provides a complete process of asset shelving, asset verification and asset consumption, can audit the consistency of a scene in a verification stage and a scene in a consumption stage, ensures that the benefit of a data provider is guaranteed, can collect the feedback condition of the data asset in a real consumption scene, and effectively corrects the data value and pricing.
Referring to fig. 2, a data pricing method based on privacy computation data exploration sequentially operates based on the data pricing system based on privacy computation data exploration, wherein a party a in fig. 2 may be a data demand party, and a party B may be a data provider, and performs reasonable pricing through privacy deal, value evaluation, data pricing, data transaction and data consumption, and effectively corrects data pricing according to feedback conditions of data assets in real consumption scenes.
The invention has the beneficial effects that:
according to the data pricing system and method based on the privacy calculation data exploration, under the condition that the privacy calculation technology is added, the data can effectively achieve separation of the use right and the ownership, asset pricing can be conducted based on the practical value of the data, meanwhile, the feedback condition of the data assets in the real consumption scene can be collected, and the data value and pricing can be effectively corrected.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
The related modules involved in the system are all hardware system modules or are functional modules combining computer software programs or protocols with hardware in the prior art, and the computer software programs or the protocols involved in the functional modules are all known to the technology of persons skilled in the art, and are not improvements of the system; the improvement of the system is the interaction relation or the connection relation among all the modules, namely the integral structure of the system is improved so as to solve the corresponding technical problems to be solved by the system.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A data pricing system based on privacy calculation data exploration is characterized in that data asset value assessment and pricing functions are achieved under the privacy security calculation background, and the data asset value assessment and pricing system comprises a data initialization module, an asset matching module, an asset value assessment module, an asset pricing module and an asset consumption module; the data pricing system receives data input from the outside into a data initialization module, the output end of the data initialization module is connected with the input end of an asset matching module in a signal mode, the output end of the asset matching module is connected with the input end of an asset value evaluation module in a signal mode, the output end of the asset value evaluation module is connected with the input end of an asset pricing module in a signal mode, and the asset pricing module is in bidirectional signal connection with an asset consumption module;
the data initialization module is used for initializing data needing pricing and endowing the data to be priced with a unique identifier in the whole data pricing system;
the asset matching module is used for obtaining a joint sample set based on the privacy of the data main key fields agreed by both trading parties;
the asset value evaluation module is used for evaluating the value indexes within the validity, the correlation and the contribution degree of the target field and the input fields of the two parties;
the asset pricing module is used for giving the valuation of each data asset;
and the asset consumption module is used for providing consumption data assets in a privacy calculation consumption scene.
2. The data pricing system based on data exploration with privacy computation of claim 1, wherein the data initialization module desensitizes sensitive field names in data, fields including gender and age, replaces the sensitive field names with x1 and x2 respectively, and explains updating frequency, effectiveness, coverage rate and main key composition of the data.
3. The data pricing system based on privacy computation data exploration according to claim 1, wherein the asset matching module is used for uploading own data by a data demand party, configuring an intersection field of data of both parties, specifying a field of an intersection sample table, and supporting sampling strategies including random sampling and hierarchical sampling; the asset matching module is used for the data provider to set a certain field in the data as a target column.
4. The privacy-computation-data-exploration-based data pricing system according to claim 1, wherein the asset value assessment module is used for supporting data preprocessing and feature engineering operations and for supporting rapid computation of relevant value assessment indicators of data assets of a data provider.
5. The data pricing system based on private computing data exploration according to claim 4, wherein the asset value assessment module supports value assessment indexes including Shapley feature contribution degree α, feature multiple collinearity VIF value χ, feature IV importance degree δ, feature GINI importance degree ε, feature information entropy importance degree φ and feature correlation coefficient γ, and is used for providing multi-angle and comprehensive quantification of the action efficacy of each feature in a scene.
6. The privacy-computation-data-exploration-based data pricing system of claim 5, wherein a value evaluation function is established based on the value evaluation index, the value evaluation function being represented by the following equation:
wherein f islabel、gother featureAnd wlabelTriple is evaluated for data asset value, Constant is a merit Constant.
7. The privacy-computation-data-exploration-based data pricing system of claim 6, wherein the data asset value assessment triplets further comprise the following details:
flabel: the method comprises the following steps that Shapley feature contribution degree alpha, feature IV importance degree delta, feature GINI importance degree epsilon and feature information entropy importance degree phi are included, the indexes directly reflect the relation of a target column label, and the larger the index value is, the more important the features are;
gother feature: the method comprises a feature multiple collinearity VIF value χ, wherein two indexes of a feature correlation coefficient γ reflect the relationship between each feature and other features, and the index value is larger, so that the feature uniqueness is smaller, and the feature uniqueness is easier to replace;
wlabel: the feature correlation coefficient γ also reflects the importance of the relationship with the target column label, and this part of index information is processed separately.
8. A privacy-computing-data-exploration-based data pricing system according to claim 1, characterized in that each feature scenario in the asset pricing module has a corresponding market cost, such as: the marketing customer acquisition cost, the average cost of the wind control fraud and the cost of the loan consumption risk can be combined with the scene cost and the quantitative index of the value evaluation to give the valuation of each data asset; the transaction trading value of the original category data assets is supported to be provided, and some fine adjustment can be made on the basis of valuation; supporting a gradient pricing strategy, supporting a pricing strategy according to calculation times and supporting a monthly payment pricing strategy according to the package year.
9. The data pricing system based on private computing data exploration, according to claim 1, characterized in that the asset consumption module provides a place for data consumption, a data modeling capability, a data joint prediction capability for a platform; the platform provides a complete process of asset shelving, asset verification and asset consumption, can audit the consistency of a scene in a verification stage and a scene in a consumption stage, ensures that the benefit of a data provider is guaranteed, can collect the feedback condition of the data asset in a real consumption scene, and effectively corrects the data value and pricing.
10. A data pricing method based on privacy computation data exploration, characterized in that, a data pricing system based on privacy computation data exploration according to any one of claims 1-9 is sequentially operated, reasonable pricing is carried out through privacy deal, value evaluation, data pricing, data transaction and data consumption, and data pricing is effectively corrected according to feedback conditions of data assets in real consumption scenes.
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