CN117574381A - Physical examination user privacy protection method, device and system - Google Patents
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Abstract
The invention discloses a physical examination user privacy protection method, a physical examination user privacy protection device and a physical examination user privacy protection system, which are characterized in that a corresponding model is established according to participant data, a prediction result is obtained according to requirements, a complete quantitative evaluation system is established for the uncertainty of the model prediction result, a trained model is verified and adjusted under new data based on a trusted third party, the trusted third party returns the adjusted model and parameters to each party after training based on the data attributes of each party through the processed data attributes, and the calculation model is enabled to output the same result under the condition that private data of each participant (including a service party and each party of an Internet service platform) does not leave a domain or the private data of each participant is ensured not to leak.
Description
The application is a divisional application of Chinese patent application with the application number of CN202110893693.1 and the name of 'physical examination user privacy protection method, device and system', wherein the application date is 2021, 8 and 5.
Technical Field
The invention relates to the technical field of computer information security, in particular to a physical examination user privacy protection method, device and system.
Background
With the popularization of the internet and the mobile internet, the trend has been to collect data based on user behavior tracks and then build a model to predict the behavior preference of users, but personal user data security has become the focus of attention of all communities, and for this reason, data security related regulations are being exported in all countries to protect personal privacy of users.
The multiparty safe calculation solves the problem of cooperative calculation for protecting privacy among a group of mutually-untrusted participants, and the multiparty often refers to strongly-associated parties in an application scene.
Uncertainty quantitative assessment is to measure the uncertainty of calculation by a quantitative method, so that information deletion caused by providing only the original calculation result is avoided.
The transfer learning refers to a machine learning method in which a pre-trained model is re-applied in a different task, and mainly aims at the problem that may occur when training data and application data are obviously inconsistent.
Disclosure of Invention
Aiming at the defects, the invention aims to solve the technical problem of how to fully utilize the advantages of big data and modeling to utilize the information of the patient on the premise of protecting the privacy of the user of the patient, thereby accurately judging the illness state of the patient so as to provide an accurate treatment scheme.
In view of the above drawbacks, it is an object of the present invention to provide a method, a system, an electronic device, a computer storage medium, and a program product for protecting privacy of a physical examination user.
According to an aspect of the embodiments of the present disclosure, a physical examination user privacy protection method is provided, which is used for joint modeling training of at least one service side and at least one internet service platform, each side of the service side and the internet service platform respectively performs preliminary modeling according to data collected by itself, a model of each service is built according to historical service information and historical big data, a prediction result is obtained according to requirements, a complete quantitative evaluation system is built for uncertainty of the model prediction result, a trained model is verified and parameterized under new data based on a trusted third party, uncertainty of prediction progress caused by difference of training models of each side is reduced, each side of trusted third party provides processed data attributes, the trusted third party returns a model and parameters after the optimization based on data attributes of each side, each side of trusted third party calculates and returns a prediction result to the trusted third party based on the new model and parameters and respective data, if the result consistency condition is met, iteration is ended until the result consistency condition is met, if the model is not met.
Preferably, the service side and each side of the internet service platform perform preliminary modeling according to the collected data, and large data of different service types, different crowds and different detection schemes are modeled by establishing a model.
Preferably, the uncertainty system to be evaluated includes, but is not limited to, uncertainty in model prediction accuracy, uncertainty in model generalization capability, and uncertainty in data integrity.
Preferably, the data of at least one service party has data of different latitudes, including but not limited to user personal information, query history information, related business data, and preliminary treatment plan data.
Preferably, the trusted third party has a trusted data store, including a separate model computation space and data sent by the parties.
Preferably, the trusted third party sends the model and exchange modeling parameters to at least one server and the internet service platform.
According to another aspect of the embodiments of the present disclosure, there is provided a physical examination user privacy protection method, applied to an internet service platform, including:
after receiving a joint modeling request of at least one service party, carrying out preliminary modeling according to data collected by the service party, establishing a model of each service according to historical service information and historical big data, obtaining a prediction result according to requirements, establishing a complete quantitative evaluation system for uncertainty of the model prediction result, verifying and adjusting the trained model under new data based on a trusted third party, providing processed data attributes for the trusted third party, returning the model and parameters after the trusted third party is trained based on the data attributes of all the parties to an Internet service platform, calculating and returning the prediction result to the trusted third party based on the new model and the parameters and the collected data, if the prediction result meets the result consistency condition, ending, and if the prediction result is not met, carrying out iteration until the result consistency condition is met.
Preferably, each party of the internet service platform carries out preliminary modeling according to the collected data, and models big data of different service types, different crowds and different detection schemes by establishing a model.
Preferably, the uncertainty system to be evaluated includes, but is not limited to, uncertainty in model prediction accuracy, uncertainty in model generalization capability, and uncertainty in data integrity.
According to another aspect of embodiments of the present disclosure, there is provided a physical examination user privacy protection method applied to a third party trusted platform, including: after receiving a request of combined modeling training of at least one service side and at least one Internet service platform, building a training model, receiving parameters after the service side and each side of the Internet service platform perform preliminary modeling according to data collected by the service side and each side of the Internet service platform respectively, performing verification and parameter adjustment based on new data sent by each side, receiving the model and the parameters after the data attribute provided by each side is trained based on the data attribute of each side, respectively calculating and returning a prediction result to a trusted third party by each side based on the new model and the parameters and the respective data, if the result consistency condition is met, ending, and if the result consistency condition is not met, performing iteration until the result consistency condition is met.
Preferably, the third party trusted platform has a trusted data storage space comprising a separate model computation space and data sent by each party is stored.
Preferably, the third party trusted platform sends the model and the exchange modeling parameters to at least one server and the internet service platform.
According to another aspect of embodiments of the present specification, there is provided a physical examination user privacy protection system, comprising: at least one service party, at least one internet service platform and a third party trusted platform, wherein,
the service side performs preliminary modeling according to the data collected by the service side, a model of each service is built according to historical service information and historical big data, a prediction result is obtained according to requirements, a complete quantitative evaluation system is built for uncertainty of the model prediction result, the trained model is verified and adjusted under new data based on the trusted third party, processed data attributes are provided for the trusted third party, the trusted third party returns the model and parameters after the internet service platform is adjusted to be optimal after training based on the data attributes of the parties, the internet service platform calculates and returns the prediction result to the trusted third party based on the new model and the parameters and the collected data, if the result consistency condition is met, the process is finished, and if the result consistency condition is not met, the process is iterated until the result consistency condition is met;
After receiving the joint modeling request of the service side, the internet service platform carries out preliminary modeling according to the data collected by the internet service platform, establishes a model of each service according to historical service information and historical big data, obtains a prediction result according to requirements, establishes a complete quantitative evaluation system for uncertainty of the model prediction result, verifies and adjusts parameters of the trained model under new data based on the trusted third party, provides processed data attributes for the trusted third party, and returns the optimized model and parameters to the internet service platform after training based on the data attributes of each party;
after receiving the request of the service side and the internet service platform combined modeling training, the third party trusted platform establishes a training model, receives parameters after the service side and the internet service platform respectively perform preliminary modeling according to data collected by the service side and the internet service platform, performs verification and parameter adjustment based on new data sent by each side, receives the model and the parameters after the data attribute provided by each side after being processed is trained based on the data attribute of each side, calculates and returns a prediction result to the trusted third party based on the new model and the parameters and the respective data, and if the result consistency condition is met, the third party is finished, and if the result consistency condition is not met, the third party iterates until the result consistency condition is met.
Preferably, the uncertainty system to be evaluated includes, but is not limited to, uncertainty in model prediction accuracy, uncertainty in model generalization capability, and uncertainty in data integrity.
According to another aspect of embodiments of the present description, there is provided a computer-readable storage medium having stored thereon a computer program/instruction which, when executed by a processor, performs the steps of:
the service side and the internet service platform respectively conduct preliminary modeling according to data collected by the service side and the internet service platform, a model of each service is built according to historical service information and historical big data, a prediction result is obtained according to requirements, a complete quantitative evaluation system is built for uncertainty of the model prediction result, the trained model is verified and adjusted under new data based on a trusted third party, uncertainty of prediction progress caused by difference of training models of the parties is reduced, the trusted third party provides processed data attributes, the trusted third party returns the adjusted model and parameters to the parties after training based on the data attributes of the parties, the parties respectively calculate and return the prediction result to the trusted third party based on the new model and the parameters and the respective data, if the result consistency condition is met, the iteration is ended, and if the result consistency condition is not met, the iteration is conducted until the result consistency condition is met.
According to another aspect of embodiments of the present description, there is provided a computer program product comprising computer programs/instructions which when executed by a processor implement the steps of:
the service side and the internet service platform respectively conduct preliminary modeling according to data collected by the service side and the internet service platform, a model of each service is built according to historical service information and historical big data, a prediction result is obtained according to requirements, a complete quantitative evaluation system is built for uncertainty of the model prediction result, the trained model is verified and adjusted under new data based on a trusted third party, uncertainty of prediction progress caused by difference of training models of the parties is reduced, the trusted third party provides processed data attributes, the trusted third party returns the adjusted model and parameters to the parties after training based on the data attributes of the parties, the parties respectively calculate and return the prediction result to the trusted third party based on the new model and the parameters and the respective data, if the result consistency condition is met, the iteration is ended, and if the result consistency condition is not met, the iteration is conducted until the result consistency condition is met.
According to another aspect of embodiments of the present specification, there is provided an electronic device including:
A processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
after receiving a joint modeling request of at least one service party, carrying out preliminary modeling according to data collected by the service party, establishing a model of each service according to historical service information and historical big data, obtaining a prediction result according to requirements, establishing a complete quantitative evaluation system for uncertainty of the model prediction result, verifying and adjusting the trained model under new data based on a trusted third party, providing processed data attributes for the trusted third party, returning the model and parameters after the trusted third party is trained based on the data attributes of all the parties to an Internet service platform, calculating and returning the prediction result to the trusted third party based on the new model and the parameters and the collected data, if the prediction result meets the result consistency condition, ending, and if the prediction result is not met, carrying out iteration until the result consistency condition is met.
According to another aspect of embodiments of the present description, there is provided a computer-readable storage medium having stored thereon a computer program/instruction which, when executed by a processor, performs the steps of:
After receiving a joint modeling request of at least one service party, carrying out preliminary modeling according to data collected by the service party, establishing a model of each service according to historical service information and historical big data, obtaining a prediction result according to requirements, establishing a complete quantitative evaluation system for uncertainty of the model prediction result, verifying and adjusting the trained model under new data based on a trusted third party, providing processed data attributes for the trusted third party, returning the model and parameters after the trusted third party is trained based on the data attributes of all the parties to an Internet service platform, calculating and returning the prediction result to the trusted third party based on the new model and the parameters and the collected data, if the prediction result meets the result consistency condition, ending, and if the prediction result is not met, carrying out iteration until the result consistency condition is met.
According to another aspect of embodiments of the present description, there is provided a computer program product comprising computer programs/instructions which when executed by a processor implement the steps of:
after receiving a joint modeling request of at least one service party, carrying out preliminary modeling according to data collected by the service party, establishing a model of each service according to historical service information and historical big data, obtaining a prediction result according to requirements, establishing a complete quantitative evaluation system for uncertainty of the model prediction result, verifying and adjusting the trained model under new data based on a trusted third party, providing processed data attributes for the trusted third party, returning the model and parameters after the trusted third party is trained based on the data attributes of all the parties to an Internet service platform, calculating and returning the prediction result to the trusted third party based on the new model and the parameters and the collected data, if the prediction result meets the result consistency condition, ending, and if the prediction result is not met, carrying out iteration until the result consistency condition is met.
According to another aspect of embodiments of the present specification, there is provided an electronic device including:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
after receiving a request of combined modeling training of at least one service side and at least one Internet service platform, building a training model, receiving parameters after the service side and each side of the Internet service platform perform preliminary modeling according to data collected by the service side and each side of the Internet service platform respectively, performing verification and parameter adjustment based on new data sent by each side, receiving the model and the parameters after the data attribute provided by each side is trained based on the data attribute of each side, respectively calculating and returning a prediction result to a trusted third party by each side based on the new model and the parameters and the respective data, if the result consistency condition is met, ending, and if the result consistency condition is not met, performing iteration until the result consistency condition is met.
According to another aspect of embodiments of the present description, there is provided a computer-readable storage medium having stored thereon a computer program/instruction which, when executed by a processor, performs the steps of:
After receiving a request of combined modeling training of at least one service side and at least one Internet service platform, building a training model, receiving parameters after the service side and each side of the Internet service platform perform preliminary modeling according to data collected by the service side and each side of the Internet service platform respectively, performing verification and parameter adjustment based on new data sent by each side, receiving the model and the parameters after the data attribute provided by each side is trained based on the data attribute of each side, respectively calculating and returning a prediction result to a trusted third party by each side based on the new model and the parameters and the respective data, if the result consistency condition is met, ending, and if the result consistency condition is not met, performing iteration until the result consistency condition is met.
According to another aspect of embodiments of the present description, there is provided a computer program product comprising computer programs/instructions which when executed by a processor implement the steps of:
after receiving a request of combined modeling training of at least one service side and at least one Internet service platform, building a training model, receiving parameters after the service side and each side of the Internet service platform perform preliminary modeling according to data collected by the service side and each side of the Internet service platform respectively, performing verification and parameter adjustment based on new data sent by each side, receiving the model and the parameters after the data attribute provided by each side is trained based on the data attribute of each side, respectively calculating and returning a prediction result to a trusted third party by each side based on the new model and the parameters and the respective data, if the result consistency condition is met, ending, and if the result consistency condition is not met, performing iteration until the result consistency condition is met.
According to the invention, through applying the multiparty safe calculation base under privacy protection and combining with the idea of transfer learning, a plurality of service sides which store patient information and an Internet service platform are subjected to joint modeling and collaborative inference on the premise of not sharing respective data, a big data model of related service is predicted, the condition development situation under various data is summarized, and an uncertainty quantitative evaluation system is established to reasonably measure the uncertainty of a prediction result, so that the related result and the service development trend are reasonably predicted, and a scientific reference basis is provided for a decision maker.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present invention, the drawings that are needed to be used in the embodiments of the present invention will be briefly described, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic structural diagram of an embodiment of a physical examination user privacy protection method of the present invention;
FIG. 2 is a schematic structural diagram of another embodiment of the physical examination user privacy protection method of the present invention;
FIG. 3 is a schematic structural diagram of another embodiment of the physical examination user privacy protection method of the present invention;
FIG. 4 is a flowchart illustrating an embodiment of a physical examination user privacy preserving method according to the present invention;
FIG. 5 is a flow chart illustrating another embodiment of a physical examination user privacy preserving method of the present invention;
fig. 6 is a flowchart of another embodiment of the physical examination user privacy protection method of the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely configured to illustrate the invention and are not configured to limit the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the invention by showing examples of the invention.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, 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 … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
As shown in fig. 1, in the physical examination user privacy protection method provided in one embodiment of the present disclosure, the physical examination user privacy protection method is used for joint modeling training of at least one service party 11 and at least one internet service platform 12, each party of the service party 11 and the internet service platform 12 respectively performs preliminary modeling according to data collected by itself, a model of each service is built according to historical service information and historical big data, a prediction result is obtained according to requirements, a complete quantitative evaluation system is built according to uncertainty of the model prediction result, a trained model is verified and parameterized under new data based on a trusted third party (or a third party trusted platform) 13, uncertainty of prediction progress caused by differences of training models of each party is reduced, each trusted third party 13 provides processed data attributes, each party returns a model and parameters after the optimization based on each party data attribute, each party calculates and returns a prediction result to the trusted third party 13 based on the new model and the parameters, if the result consistency condition is met, and if the result consistency condition is not met, iteration is finished until the result consistency condition is met.
In some embodiments, the service side and each side of the internet service platform perform preliminary modeling according to the collected data, and large data of different service types, different crowds and different detection schemes are modeled by building a model.
It is worth noting that the uncertainty system to be evaluated includes, but is not limited to, uncertainty in model prediction accuracy, uncertainty in model generalization capability, and uncertainty in data integrity.
In some embodiments, the data of at least one service party has data of different latitudes, including but not limited to user personal information, query history information, related business data, and preliminary treatment plan data.
In some embodiments, the trusted third party has a trusted data store, including storing data sent by the parties and an independent model computation space.
In some embodiments, the trusted third party sends the model and exchange modeling parameters to at least one of the server and the internet service platform.
In a specific example, the service party may be an external related cooperative institution, such as a linkage physical examination center, where the physical examination center is a service institution with medical qualification, some is a subordinate unit of a hospital, some is a professional physical examination linkage institution, and common examples include an icongbin, a great health in the united states, a arrowhead, and the like.
In a specific example, the internet service platform may be a comprehensive internet health medicine and medical platform, such as an ali health platform and a jindong health platform, or a special medical platform for medical health inquiry, such as a spring rain doctor, a clove garden, etc., or a special medical platform, such as a special mobile medical platform for good mood mobile medical treatment, which is focused on the central nervous field.
As shown in fig. 2, taking an external relevant cooperative mechanism as an example, the physical examination user privacy protection method provided by one embodiment of the present disclosure is used for joint modeling training of at least one physical examination mechanism and at least one internet service platform, each of the physical examination mechanism and the internet service platform respectively performs preliminary modeling according to data collected by itself, a model of each service is built according to historical service information and historical big data, a complete quantitative evaluation system is built according to uncertainty of the model prediction result, a trained model is verified and parameterized under new data based on a trusted third party, uncertainty of prediction progress caused by difference of training models of each party is reduced, each trusted third party provides processed data attributes, each trusted third party returns a model and parameters after being optimized to each party after training based on data attributes of each party, each party calculates and returns the prediction result to the trusted third party based on the new model and parameters, if the result consistency condition is met, iteration is ended, and if the result consistency condition is not met, the iteration is performed until the result consistency condition is met.
As shown in fig. 3, in a specific example, the physical examination institution, the service party, and the internet service platform may be used as parties, where the service party may be used as a trusted platform for a third party or as a party. As a participant, the physical examination user privacy protection method provided by one embodiment of the specification is used for joint modeling training of at least one physical examination mechanism, at least one service side and at least one internet service platform, each side of the physical examination mechanism, the service side and the internet service platform respectively carries out preliminary modeling according to data collected by the physical examination mechanism, the service side and the internet service platform, a model of each service is built according to historical service information and historical big data, a complete quantitative evaluation system is built according to uncertainty of the model prediction result, the trained model is verified and parameterized under new data based on a trusted third party, uncertainty of prediction progress caused by difference of training models of each side is reduced, each trusted third party provides processed data attributes, the trusted third party returns the model and parameters after the tempering to each side after training based on the data attributes of each side, each side calculates and returns the prediction result to the trusted third party respectively based on the new model and the parameters, if the result consistency condition is met, iteration is ended, and if the result consistency condition is not met, the iteration is carried out until the result consistency condition is met.
In embodiments of the present invention, the third party trusted platform may be a national medical regulatory authority, such as a medical insurance center, a medical supervision authority, or the like.
In a specific example, in order to make the data privacy protection more complete, the data and modeling behaviors of each party are supervised, the service party, the internet service platform and the third party trusted platform are respectively used as nodes of a alliance chain, each party is used as a node to participate based on the blockchain (alliance chain), the data verification, the data attribute, the modeling parameter and the like are hashed and stored and are used as a chain, a corresponding digital certificate is generated as a unique identifier, and each party behavior in the whole behavior chain can be positioned and confirmed by a trusted means when a medical accident dispute occurs in the later stage, so that basic capability is provided for definitely responsibilities.
As shown in fig. 4, an embodiment of a physical examination user privacy protection method is provided from the perspective of an internet service platform side, which includes the following steps:
s201, after receiving a joint modeling request of at least one server, performing preliminary modeling according to data collected by the server;
s202, establishing a model of each service according to historical service information and historical big data, and obtaining a prediction result according to requirements;
S203, establishing a complete quantitative evaluation system for uncertainty of model prediction results; verifying and adjusting parameters of the trained model under new data based on a trusted third party, and providing processed data attributes for the trusted third party;
s204, the trusted third party trains based on the data attribute of each party and returns a model and parameters after callback to the Internet service platform;
s205, the internet service platform calculates and returns a prediction result to the trusted third party based on the new model and the parameters and the collected data;
s206, if the result consistency condition is met, ending, and if the result consistency condition is not met, iterating until the result consistency condition is met.
In some embodiments, each party of the internet service platform performs preliminary modeling according to the collected data, and models big data of different service types, different crowds and different detection schemes by establishing a model.
In particular, the uncertainty system to be evaluated includes, but is not limited to, uncertainty in model prediction accuracy, uncertainty in model generalization capability, and uncertainty in data integrity.
As shown in fig. 5, an embodiment of a physical examination user privacy protection method is provided from the perspective of a third party trusted platform, which includes the following steps:
S301, after receiving a request of combined modeling training of at least one server and at least one Internet service platform, building a training model;
s302, receiving parameters after preliminary modeling of each of the service side and the Internet service platform according to data collected by each of the service side and the Internet service platform;
s303, verifying and adjusting parameters based on new data sent by each party;
s304, receiving the data attribute provided by each party after processing, training based on the data attribute of each party, and returning the model and parameters after tuning to each party;
s305, each party calculates and returns a prediction result to the trusted third party based on the new model, the parameters and the respective data;
and S306, if the result consistency condition is met, ending, and if the result consistency condition is not met, iterating until the result consistency condition is met.
In some embodiments, the third party trusted platform has a trusted data store, including storing data sent by parties and an independent model computation space.
In some embodiments, the third party trusted platform sends the model and exchange modeling parameters to at least one of the service party and the internet service platform.
As shown in fig. 6, an embodiment of a physical examination user privacy protection method is provided from the perspective of a physical examination organization, which includes the following steps:
S401, after receiving a joint modeling request of at least one server and/or at least one Internet service platform, performing preliminary modeling according to data collected by the server;
s402, establishing a model of each service according to the historical service information and the historical big data, and obtaining a prediction result according to the requirements;
s403, establishing a complete quantitative evaluation system for uncertainty of model prediction results; verifying and adjusting parameters of the trained model under new data based on a trusted third party, and providing processed data attributes for the trusted third party;
s404, the trusted third party trains based on the data attribute of each party and returns the model and parameters after the callback to the physical examination mechanism;
s405, the physical examination mechanism calculates and returns a prediction result to the trusted third party based on the new model and the parameters and the collected data;
s406, if the result consistency condition is met, ending, and if the result consistency condition is not met, iterating until the result consistency condition is met.
In some embodiments, the physical examination mechanism performs preliminary modeling according to the data collected by the physical examination mechanism, and models big data of different business types, different crowds and different detection schemes by establishing a model.
It is worth noting that the uncertainty system to be evaluated includes, but is not limited to, uncertainty in model prediction accuracy, uncertainty in model generalization capability, and uncertainty in data integrity.
According to another embodiment, there is provided a physical examination user privacy protection system, including: at least one service party, at least one internet service platform and a third party trusted platform, wherein,
the service side performs preliminary modeling according to the data collected by the service side, a model of each service is built according to historical service information and historical big data, a prediction result is obtained according to requirements, a complete quantitative evaluation system is built for uncertainty of the model prediction result, the trained model is verified and adjusted under new data based on the trusted third party, processed data attributes are provided for the trusted third party, the trusted third party returns the model and parameters after the internet service platform is called back after training based on the data attributes of the parties, the internet service platform calculates and returns the prediction result to the trusted third party based on the new model and the parameters and the collected data, if the result consistency condition is met, the process is finished, and if the result consistency condition is not met, the process is performed until the result consistency condition is met;
after receiving the joint modeling request of the service party, the internet service platform carries out preliminary modeling according to data collected by the internet service platform, establishes a model of each service according to historical service information and historical big data, obtains a prediction result according to requirements, establishes a complete quantitative evaluation system for uncertainty of the model prediction result, verifies and adjusts parameters of the trained model under new data based on the trusted third party, provides processed data attributes for the trusted third party, and returns a callback-optimized model and parameters to the internet service platform after the trusted third party carries out training based on the data attributes of all the parties;
After receiving the request of the service side and the internet service platform combined modeling training, the third party trusted platform establishes a training model, receives parameters after the service side and the internet service platform respectively perform preliminary modeling according to data collected by the service side and the internet service platform, performs verification and parameter adjustment based on new data sent by each side, receives the model and the parameters after the data attribute provided by each side after processing is trained based on the data attribute of each side, calculates and returns a prediction result to the trusted third party based on the new model and the parameters and the respective data, and if the result consistency condition is met, ends, and if the result consistency condition is not met, performs iteration until the result consistency condition is met.
In some embodiments, the uncertainty system that needs to be evaluated includes, but is not limited to, uncertainty in model prediction accuracy, uncertainty in model generalization capability, and uncertainty in data integrity.
According to an embodiment of another aspect, there is also provided a computer readable storage medium having stored thereon a computer program/instruction which when executed by a processor performs the steps of:
the method comprises the steps that each party of a service party and an internet service platform respectively carries out preliminary modeling according to data collected by the service party and the internet service platform, a model of each service is built according to historical service information and historical big data, a prediction result is obtained according to requirements, a complete quantitative evaluation system is built for uncertainty of the model prediction result, the trained model is verified and adjusted under new data based on a trusted third party, uncertainty of prediction progress caused by difference of training models of each party is reduced, each trusted third party provides processed data attributes, the trusted third party returns the adjusted model and parameters to each party after training based on the data attributes of each party, each party calculates and returns the prediction result to the trusted third party respectively based on the new model and the parameters and the respective data, if the result consistency condition is met, iteration is ended, if the result consistency condition is not met, the iteration is carried out until the result consistency condition is met.
According to an embodiment of a further aspect, there is also provided a computer program product comprising computer programs/instructions which when executed by a processor implement the steps of:
the method comprises the steps that each party of a service party and an internet service platform respectively carries out preliminary modeling according to data collected by the service party and the internet service platform, a model of each service is built according to historical service information and historical big data, a prediction result is obtained according to requirements, a complete quantitative evaluation system is built for uncertainty of the model prediction result, the trained model is verified and adjusted under new data based on a trusted third party, uncertainty of prediction progress caused by difference of training models of each party is reduced, each trusted third party provides processed data attributes, the trusted third party returns the adjusted model and parameters to each party after training based on the data attributes of each party, each party calculates and returns the prediction result to the trusted third party respectively based on the new model and the parameters and the respective data, if the result consistency condition is met, iteration is ended, if the result consistency condition is not met, the iteration is carried out until the result consistency condition is met.
According to another aspect of embodiments of the present specification, there is also provided an electronic apparatus including:
A processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
after receiving a joint modeling request of at least one service party, carrying out preliminary modeling according to data collected by the service party, establishing a model of each service according to historical service information and historical big data, obtaining a prediction result according to requirements, establishing a complete quantitative evaluation system for uncertainty of the model prediction result, verifying and adjusting the trained model under new data based on a trusted third party, providing processed data attributes for the trusted third party, returning the model and parameters after the trusted third party is trained based on the data attributes of all the parties to an Internet service platform, calculating and returning the prediction result to the trusted third party based on the new model and the parameters and the collected data, if the prediction result meets the result consistency condition, ending, and if the prediction result is not met, carrying out iteration until the result consistency condition is met.
According to another aspect of embodiments of the present description, there is also provided a computer-readable storage medium having stored thereon a computer program/instruction which, when executed by a processor, performs the steps of:
After receiving a joint modeling request of at least one service party, carrying out preliminary modeling according to data collected by the service party, establishing a model of each service according to historical service information and historical big data, obtaining a prediction result according to requirements, establishing a complete quantitative evaluation system for uncertainty of the model prediction result, verifying and adjusting the trained model under new data based on a trusted third party, providing processed data attributes for the trusted third party, returning the model and parameters after the trusted third party is trained based on the data attributes of all the parties to an Internet service platform, calculating and returning the prediction result to the trusted third party based on the new model and the parameters and the collected data, if the prediction result meets the result consistency condition, ending, and if the prediction result is not met, carrying out iteration until the result consistency condition is met.
According to another aspect of embodiments of the present description, there is provided a computer program product comprising computer programs/instructions which when executed by a processor implement the steps of:
after receiving a joint modeling request of at least one service party, carrying out preliminary modeling according to data collected by the service party, establishing a model of each service according to historical service information and historical big data, obtaining a prediction result according to requirements, establishing a complete quantitative evaluation system for uncertainty of the model prediction result, verifying and adjusting the trained model under new data based on a trusted third party, providing processed data attributes for the trusted third party, returning the model and parameters after the trusted third party is trained based on the data attributes of all the parties to an Internet service platform, calculating and returning the prediction result to the trusted third party based on the new model and the parameters and the collected data, if the prediction result meets the result consistency condition, ending, and if the prediction result is not met, carrying out iteration until the result consistency condition is met.
According to another aspect of embodiments of the present specification, there is provided an electronic device including:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
after receiving a request of combined modeling training of at least one service side and at least one Internet service platform, building a training model, receiving parameters after the service side and each side of the Internet service platform perform preliminary modeling according to data collected by the service side and each side of the Internet service platform respectively, performing verification and parameter adjustment based on new data sent by each side, receiving the model and the parameters after the data attribute provided by each side is trained based on the data attribute of each side, respectively calculating and returning a prediction result to a trusted third party by each side based on the new model and the parameters and the respective data, if the result consistency condition is met, ending, and if the result consistency condition is not met, performing iteration until the result consistency condition is met.
According to another aspect of embodiments of the present description, there is provided a computer-readable storage medium having stored thereon a computer program/instruction which, when executed by a processor, performs the steps of:
After receiving a request of combined modeling training of at least one service side and at least one Internet service platform, building a training model, receiving parameters after the service side and each side of the Internet service platform perform preliminary modeling according to data collected by the service side and each side of the Internet service platform respectively, performing verification and parameter adjustment based on new data sent by each side, receiving the model and the parameters after the data attribute provided by each side is trained based on the data attribute of each side, respectively calculating and returning a prediction result to a trusted third party by each side based on the new model and the parameters and the respective data, if the result consistency condition is met, ending, and if the result consistency condition is not met, performing iteration until the result consistency condition is met.
According to another aspect of embodiments of the present description, there is provided a computer program product comprising computer programs/instructions which when executed by a processor implement the steps of:
after receiving a request of combined modeling training of at least one service side and at least one Internet service platform, building a training model, receiving parameters after the service side and each side of the Internet service platform perform preliminary modeling according to data collected by the service side and each side of the Internet service platform respectively, performing verification and parameter adjustment based on new data sent by each side, receiving the model and the parameters after the data attribute provided by each side is trained based on the data attribute of each side, respectively calculating and returning a prediction result to a trusted third party by each side based on the new model and the parameters and the respective data, if the result consistency condition is met, ending, and if the result consistency condition is not met, performing iteration until the result consistency condition is met.
The multi-scheme secure computation framework provided by the embodiment of the invention can ensure that the computation model outputs the same result under the condition that private data of each participant (including a service side and each party of an Internet service platform) cannot go out of a domain or under the condition that private data of each participant is not revealed.
According to the invention, through applying the multiparty safe calculation base under privacy protection and combining with the idea of transfer learning, a plurality of service sides which store patient information and an Internet service platform are subjected to joint modeling and collaborative inference on the premise of not sharing respective data, a big data model of related service is predicted, the condition development situation under various data is summarized, and an uncertainty quantitative evaluation system is established to reasonably measure the uncertainty of a prediction result, so that the service result and the service development trend are reasonably predicted, and a scientific reference basis is provided for a decision maker.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present application.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
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). Memory is an example of computer-readable media.
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 storage media for a computer 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, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
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 one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A physical examination user privacy protection method is used for joint modeling training of at least one linkage physical examination mechanism with medical qualification and at least one Internet service platform, each side of the physical examination mechanism and the Internet service platform respectively carries out preliminary modeling according to data collected by the physical examination mechanism and the Internet service platform, a model of each service is built according to historical service information and historical big data, a prediction result is obtained according to requirements, a complete quantitative evaluation system is built for uncertainty of the model prediction result, a trained model is verified and parameterized under new data based on a trusted third party, uncertainty of prediction progress caused by difference of the training model of each side is reduced, the trusted third party provides processed data attributes in each direction, the trusted third party returns the optimized model and parameters to each side after training based on the data attributes of each side, each side calculates and returns the prediction result to the trusted third party respectively based on the new model and the parameters, if the prediction result accords with a result consistency condition, and if the prediction result accords with the new model and the prediction result consistency condition does not accord with the new model and iterates until the result consistency condition is met.
2. The method for protecting privacy of physical examination users according to claim 1, wherein each of the physical examination institutions and the internet service platform performs preliminary modeling according to the collected data, and models big data of different service types, different crowds and different physical examination schemes by establishing models.
3. The physical examination user privacy preserving method according to claim 1 or 2, wherein the uncertainty system to be evaluated comprises uncertainty in model prediction accuracy, uncertainty in model generalization capability and uncertainty in data integrity.
4. The method for protecting privacy of physical examination users according to claim 1, wherein the physical examination institutions have data with different latitudes, including personal information of users, inquiry history information, related business data and preliminary treatment scheme data.
5. The physical examination user privacy protection method of claim 1, wherein the trusted third party has a trusted data storage space comprising data sent by each party and an independent model computation space.
6. The physical examination user privacy preserving method of claim 5, wherein the trusted third party sends model and exchange modeling parameters to physical examination institutions and internet service platforms.
7. A physical examination user privacy protection method is applied to a trusted third party and comprises the following steps: after receiving a request of joint modeling training of at least one linkage physical examination mechanism and at least one Internet service platform, building a training model, receiving parameters after the physical examination mechanism and the Internet service platform respectively perform preliminary modeling according to data collected by the physical examination mechanism and the Internet service platform, performing verification and parameter adjustment based on new data sent by the parties, receiving the model and the parameters after the parties provide processed data attributes to perform training based on the data attributes of the parties, respectively calculating and returning a prediction result to a trusted third party by the parties based on the new model and the parameters and the respective data, if the result consistency condition is met, ending, and if the result consistency condition is not met, performing iteration until the result consistency condition is met.
8. The method of claim 7, the trusted third party having a trusted data store comprising storing data sent by the parties and an independent model computation space.
9. The method of claim 7, the trusted third party sends model and exchange modeling parameters to at least one of a checking authority and an internet service platform.
10. A physical examination user privacy preserving system comprising: at least one linkage physical examination organization, at least one Internet service platform and a trusted third party, wherein,
the physical examination mechanism carries out preliminary modeling according to the data collected by the physical examination mechanism, establishes a model of each service according to the historical service information and the historical big data, obtains a prediction result according to the requirement, establishes a complete quantitative evaluation system for the uncertainty of the model prediction result, verifies and adjusts parameters of the trained model under new data based on the trusted third party, and provides processed data attributes for the trusted third party;
after receiving the joint modeling request of the physical examination mechanism, the Internet service platform carries out preliminary modeling according to data collected by the Internet service platform, establishes a model of each service according to historical service information and historical big data, obtains a prediction result according to requirements, establishes a complete quantitative evaluation system for uncertainty of the model prediction result, verifies and adjusts parameters of the trained model under new data based on the trusted third party, and provides processed data attributes for the trusted third party;
after receiving the request of the physical examination mechanism and the Internet service platform combined modeling training, the trusted third party establishes a training model, receives parameters after the physical examination mechanism and the Internet service platform respectively perform preliminary modeling according to data collected by the physical examination mechanism and the Internet service platform, performs verification and parameter adjustment based on new data sent by each party, receives the model and the parameters after the data attribute provided by each party after being processed is trained based on the data attribute of each party, calculates and returns a prediction result to the trusted third party based on the new model and the parameters and the respective data, and if the result consistency condition is met, ends, and if the result consistency condition is not met, performs iteration until the result consistency condition is met.
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