CN111310234B

CN111310234B - Personal data processing method and device based on zero-knowledge proof and electronic equipment

Info

Publication number: CN111310234B
Application number: CN202010387119.4A
Authority: CN
Inventors: 白玮; 王维强; 金宏; 张震
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2020-05-09
Filing date: 2020-05-09
Publication date: 2020-11-03
Anticipated expiration: 2040-05-09
Also published as: CN112487485A; CN112487485B; CN111310234A

Abstract

The embodiment of the specification discloses a personal data processing method, a device and electronic equipment based on zero knowledge certification, wherein in the personal data processing method based on zero knowledge certification, a first agent representing a service provider sends a preset problem to a second agent representing a target user; the second agent queries a personal database of a target user to obtain answers of preset questions, generates a proof containing the answers through a zero-knowledge proof algorithm and sends the proof to the first agent; and the first agent verifies the correctness of the proof based on the zero-knowledge proof algorithm, acquires the answer of the preset question from the proof after the proof is verified to be correct, and finally determines the preset question and the answer of the preset question as the features for modeling.

Description

Personal data processing method and device based on zero-knowledge proof and electronic equipment

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for processing personal data based on zero-knowledge proof, and an electronic device.

Background

With the development of artificial intelligence and big data technology, it has become very common to perform machine learning modeling based on a large amount of training data to solve related problems. For example, a large amount of user data is accumulated in some large internet companies, and machine learning modeling is performed using the user data, so that a recommendation model that can accurately recommend a commodity of interest to a user can be obtained.

Currently, personal data of a user is centrally stored in a server of a corresponding internet company, and most of current machine learning is a centralized machine learning method. The centralized data storage and application mode ensures that personal data of the user is not controlled by the user, and the privacy of the user can be leaked. The protection of user privacy becomes a very important field for society and individuals, and the protection of user privacy and the prevention of information leakage become the current problem that enterprises and individuals cannot avoid. In addition, in order to protect the privacy of the user, some countries or organizations have specialized laws and regulations, for example, the european union has issued a General Data Protection Regulation (GDPR) that restricts the usage manner of the user Data and gives the user control right to the personal Data, and any enterprise or service provider organization cannot use the Data at will without the personal permission of the user.

However, although the release of these laws and regulations plays a good role in protecting the privacy of the user, the use of data including the privacy of the user is also limited, and the application and development of machine learning are restricted, so that there is a need to provide a personal data processing scheme that can not only protect the privacy of the user, but also meet the requirements of machine learning.

Disclosure of Invention

The embodiment of the specification provides a personal data processing method and device based on zero-knowledge proof and electronic equipment, so that the purpose of machine learning by using personal data of a user is met on the premise of protecting the privacy of the user.

In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:

in a first aspect, a method for processing personal data based on zero-knowledge proof is provided, including:

a first agent sending a preset question to a second agent, wherein the first agent is an agent obtaining authorization of a service provider and being capable of representing the service provider, and the second agent is an agent obtaining authorization of a target user of the service provider and being capable of representing the target user;

the second agent queries the personal database of the target user to obtain an answer of the preset question, generates a proof containing the answer through a zero-knowledge proof algorithm and sends the proof to the first agent;

the first agent verifies the correctness of the proof based on the zero-knowledge proof algorithm, and obtains the answer of the preset question from the proof after the correctness is verified;

the first agent determines the preset question and the answer to the preset question as features for modeling.

In a second aspect, a method for processing personal data based on zero knowledge proof is provided, which includes:

sending a preset question to a second agent, so that the second agent queries a personal database of a target user to obtain an answer to the preset question, and generates a proof containing the answer through a zero-knowledge proof algorithm and returns the proof, wherein the second agent obtains the authorization of the target user and can represent the agent of the target user;

verifying the correctness of the proof based on the zero-knowledge proof algorithm, and acquiring the answer of the preset question from the proof after the correctness is verified;

and determining the preset questions and the answers of the preset questions as features for modeling.

In a third aspect, a method for processing personal data based on zero knowledge proof is provided, which includes:

receiving a preset question sent by a first agent, wherein the first agent is an agent which obtains authorization of a service provider and can represent the service provider;

inquiring a personal database of a target user to obtain an answer of the preset question, and generating a proof containing the answer through a zero-knowledge proof algorithm;

and sending the certification to the first agent so that the first agent verifies the correctness of the certification based on the zero-knowledge certification algorithm, acquiring answers of the preset questions from the certification after the verification is passed, and determining the preset questions and the answers of the preset questions as features for modeling.

In a fourth aspect, a personal data processing system based on zero knowledge proof is proposed, comprising: a first agent and a second agent, wherein the first agent is an agent that obtains authorization from a service provider and is capable of representing the service provider, and the second agent is an agent that obtains authorization from a target user of the service provider and is capable of representing the target user;

the first agent is used for sending a preset question to the second agent so that the second agent queries a personal database of a target user to obtain an answer to the preset question, generates a proof containing the answer through a zero-knowledge proof algorithm and returns the proof; verifying the correctness of the proof based on the zero-knowledge proof algorithm, and acquiring the answer of the preset question from the proof after the correctness is verified; determining the preset questions and answers of the preset questions as features for modeling;

the second agent is used for receiving the preset problem sent by the first agent; inquiring a personal database of a target user to obtain answers of the preset questions, generating a proof containing the answers through a zero-knowledge proof algorithm, sending the proof to the first agent, enabling the first agent to verify the correctness of the proof based on the zero-knowledge proof algorithm, obtaining the answers of the preset questions from the proof after the proof passes the verification, and determining the preset questions and the answers of the preset questions as features for modeling.

In a fifth aspect, a personal data processing device based on zero knowledge proof is provided, comprising:

the question-asking module is used for sending a preset question to a second agent so that the second agent queries a personal database of a target user to obtain an answer of the preset question, generates a proof containing the answer through a zero-knowledge proof algorithm and returns the proof, wherein the second agent obtains the authorization of the target user and can represent the target user;

the verification module is used for verifying the correctness of the proof based on the zero-knowledge proof algorithm and acquiring the answer of the preset question from the proof after the correctness is verified;

and the characteristic determining module is used for determining the preset questions and the answers of the preset questions as the characteristics for modeling.

In a sixth aspect, a personal data processing device based on zero knowledge proof is provided, comprising:

the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for receiving a preset question sent by a first intelligent agent, and the first intelligent agent is an intelligent agent which obtains the authorization of a service provider and can represent the service provider;

the query module is used for querying a personal database of a target user to obtain answers of the preset questions and generating proofs containing the answers through a zero-knowledge proof algorithm;

and the sending module is used for sending the certification to the first agent so that the first agent verifies the correctness of the certification based on the zero-knowledge certification algorithm, acquires the answer of the preset question from the certification after the verification is passed, and determines the preset question and the answer of the preset question as the features for modeling.

In a seventh aspect, an electronic device is provided, including:

a processor; and

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

In an eighth aspect, a computer-readable storage medium is presented, the computer-readable storage medium storing one or more programs that, when executed by an electronic device that includes a plurality of application programs, cause the electronic device to:

In a ninth aspect, an electronic device is provided, comprising:

a processor; and

In a tenth aspect, a computer-readable storage medium is presented, storing one or more programs that, when executed by an electronic device including a plurality of application programs, cause the electronic device to:

As can be seen from the technical solutions provided in the embodiments of the present specification, the solutions provided in the embodiments of the present specification have at least one of the following technical effects: the user personal data in the scheme is stored in a personal database of a user and is controlled by the user, the second agent authorized by the user can access the personal data of the user, the service provider does not have the right to directly access the personal data of the user, and the service provider obtains the characteristics for modeling in a questioning and zero-knowledge proof mode.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

fig. 1 is a schematic flowchart of a method for processing personal data based on zero-knowledge proof according to an embodiment of the present disclosure.

Fig. 2 is a second flowchart of a method for processing personal data based on zero-knowledge proof according to an embodiment of the present disclosure.

Fig. 3 is a third schematic flowchart of a method for processing personal data based on zero-knowledge proof according to an embodiment of the present disclosure.

Fig. 4 is an interaction diagram of a method for processing personal data based on zero-knowledge proof according to an embodiment of the present specification.

Fig. 5 is a fourth flowchart of a method for processing personal data based on zero-knowledge proof according to an embodiment of the present disclosure.

Fig. 6 is a fifth flowchart of a method for processing personal data based on zero-knowledge proof according to an embodiment of the present disclosure.

FIG. 7 is a block diagram of a personal data processing system based on zero-knowledge proof according to an embodiment of the present disclosure.

Fig. 8 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification.

Fig. 9 is a schematic structural diagram of a personal data processing device based on zero-knowledge proof according to an embodiment of the present specification.

Fig. 10 is a second schematic structural diagram of a personal data processing device based on zero-knowledge proof according to an embodiment of the present disclosure.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, 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 application.

In order to meet the purpose of machine learning by using personal data of a user on the premise of protecting the privacy of the user, the embodiment of the specification provides a personal data processing method, device and system based on zero-knowledge proof. The method and the apparatus provided by the embodiments of the present disclosure may be executed by an electronic device, such as a terminal device or a server device. In other words, the method may be performed by software or hardware installed in the terminal device or the server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The terminal devices include but are not limited to: any one of smart terminal devices such as a smart phone, a Personal Computer (PC), a notebook computer, a tablet computer, an electronic reader, a web tv, and a wearable device.

The following first describes a method for processing personal data based on zero-knowledge proof provided in the embodiments of the present specification.

Fig. 1 is a schematic flow chart of an implementation of a zero-knowledge proof-based personal data processing method according to an embodiment of the present specification. As shown in fig. 1, the method may include the following steps.

Step 102, a first agent on behalf of a service provider sends a preset question to a second agent on behalf of a target user.

An Agent is a software system with intelligence, and an Agent generally has autonomy (autonomously controls its own behavior and has its own goal or intention), responsiveness (being able to adjust its own behavior according to the perception of the environment), adaptability (correcting its own goal and plan according to the change of the environment), and sociality (being able to cooperate and communicate with each other between agents).

The first agent is an agent authorized by the service provider and capable of representing the service provider, and specifically may be an agent capable of collecting user characteristics on behalf of the service provider, building a target model by using the user characteristics for machine learning, and predicting by using the target model, wherein the service provider may be an enterprise, a company or other organization providing products or services for the user, such as an e-commerce company, and any network application company (such as a reading APP company, a game APP company, and a music APP company) providing products or services for the user by charging, and the like. The second agent is an agent authorized by and capable of representing a target user of the service provider, and may specifically be an agent authorized by the target user to access a personal database of the target user on behalf of the target user, wherein the target user may serve any user of the service provider, and optionally the target user may be one of some users more favorable for modeling, such as a user whose registration time exceeds a preset time.

Before step 102, the first agent needs to accept the authority granted by the service provider to obtain the user features on behalf of the service provider, so that the first agent can collect, use, etc. the features of the target user on behalf of the service provider; the second agent also needs to accept the rights granted by the target user to control the personal database of the target user in order for the second agent to be able to access the personal database on behalf of the target user.

And 104, the second agent queries the personal database of the target user to obtain an answer to a preset question, generates a proof containing the answer through a zero-knowledge proof algorithm and sends the proof to the first agent.

The personal database of the target user is controlled by the target user, some personal data of the target user are stored in the personal database of the target user, when the target user is a user of an e-commerce company, the personal data stored in the personal database of the target user can comprise online shopping records and the like of the target user, and when the target user is a user of a certain online music APP company, the personal data stored in the personal database of the target user can comprise music listening records and the like of the target user. The target user may have authorized means for the second agent to exercise control over the personal database on behalf of the target user, including access, queries, etc.

The preset question may be a question preset by the first agent and related to modeling, and the answer of the preset question cannot reveal sensitive information or personal privacy of the target user. For example, the preset question may be "whether the monthly salary of the target user exceeds ten thousand", and accordingly, the answer may be "yes" or "no", but the preset question may not be "what the monthly salary of the target user is", and if the preset question is, the answer obtained by the second agent by querying the personal data of the target user may be 15000 (the monthly salary of the target user is true), and the answer is transmitted to the first agent by way of zero knowledge proof, so that sensitive information that the target user is really monthly income may be leaked.

Therefore, in order to protect sensitive information or privacy information in personal data of a target user from being leaked, after querying a personal database of the target user to obtain an answer to the preset question, before generating a proof containing the answer based on a zero-knowledge proof algorithm, the second agent may further determine whether the answer to the preset question reveals the privacy of the target user, and specifically may set some rules in advance for determination; if the user privacy is leaked, refusing to provide the answer of the preset question to the first agent, wherein the specific refusing mode can be that prompt information like 'the question can cause the user privacy leakage and please change a question' is fed back to the first agent; if not, continuing to execute the step of generating the proof containing the answer based on the zero-knowledge proof algorithm.

Zero-Knowledge Proof (Zero-Knowledge Proof) is a probability-based verification method, comprising two parts: a prover (prover) claiming a certain proposition to be genuine and a verifier (verifier) confirming that the proposition is indeed genuine. The zero knowledge proof system provides a proof protocol that satisfies the following three characteristics: completeness-as long as the statement is correct, the server can convince the verifier; reliability-if the statement is wrong, the server has no way to convince the verifier; zero knowledge-the attestation protocol merely shows whether the statement is correct, without revealing any other additional information, that is, a zero knowledge attestation means that the prover can convince the verifier that some assertion is correct without providing the verifier with any useful information. As the name implies, the zero knowledge proof can fully prove that the self is a legal owner with certain rights and does not leak related information, namely the knowledge is zero to the outside.

As an example, the zero Knowledge proof algorithm in the embodiment of the present specification includes a zero Knowledge concise no-interactive Knowledge authentication (zkSNARK). The next step will be described in detail the process of the second agent generating proof based on zkSNARK, and the correctness of the first intelligent proof of experience proof.

And step 106, the first agent verifies the correctness of the certification based on the zero-knowledge certification algorithm, if the certification is correct, the step 108 is executed, otherwise, the step 102 is executed again or the question is asked again to the second agent.

1) Before generating proof that a certain topic is true (as the answer is true as described above) based on zkSNARK, the second agent is configured as follows:

random selection

Generate, generate

：

Defining an equation relating to a secret to be certified:

wherein the content of the first and second substances,

is a value that is selected at random,

a representation domain;

is a polynomial relating to the problem to be proved, in which,

and

is a polynomial associated with the problem to be proved,nexpression polynomial

The order of (a) is selected,

(statuses) stating that the prover knows the secret information;

representative secret information (witnesses);

and

are integers related to statements (statements) and secret information (witnesses).

2) The proof process for the second agent to generate a question of true (as the answer above is true) based on zkSNARK is:

randomly selecting two parameters

And

calculating

Wherein the content of the first and second substances,

in order to generate the proof of authenticity,

for the contents to be proved (i.e. the answer to the above-mentioned predetermined question is true)

And

is a randomly chosen parameter to increase uncertainty.

3) The process by which the first intelligence verifies the correctness of the received proof based on zkSNARK:

verifying whether the following equation is true, if true, indicating that the proof is correct, otherwise, not correct:

and 108, the first agent obtains the answer of the preset question from the certificate, and determines the preset question and the answer of the preset question as the features for modeling.

For example, a preset question "whether the user's monthly salary is over ten thousand" and an answer "yes" are taken as one feature for modeling.

In the method for processing personal data based on zero-knowledge proof provided by the embodiment of the specification, the personal data of a user is stored in a personal database of the user, and is controlled by the user, a second agent authorized by the user can access the personal data of the user, a service provider does not have the right to directly access the personal data of the user, and the service provider obtains features for modeling in a way of asking questions and zero-knowledge proof, so that on one hand, sensitive data in the personal data of the user can be prevented from being leaked, on the other hand, related feature data can be provided for the service provider, and the service provider is helped to achieve the purpose of machine learning, that is, the scheme can protect the personal privacy of the user and provide the feature data for modeling for the service provider.

Optionally, on the basis of fig. 1, a method for processing personal data based on zero-knowledge proof provided by an embodiment of the present specification may further include:

repeatedly executing the step of appointing for a plurality of times on the target user, taking a plurality of preset questions of the target user and answers corresponding to the preset questions as a plurality of characteristics for modeling, and adding the characteristics into a characteristic pool; wherein the step of specifying comprises steps 102 to 108.

Further, the above-mentioned specifying steps may be repeatedly performed for more users, respectively, to take a plurality of preset questions of the more users and answers corresponding to the plurality of preset questions as a plurality of features for modeling, respectively, and add the features into a feature pool; wherein one user corresponds to one second agent.

That is to say, as shown in fig. 2, a method for processing personal data based on zero-knowledge proof according to an embodiment of the present specification may further include:

and step 110, judging whether the steps 102 to 108 are repeatedly executed for N users, if so, executing the step 112, otherwise, returning to execute the step 102 again.

Generally, when step 102 is repeatedly executed for the same user, the preset questions sent by the first agent to the second agent representing the user are different, so that a series of questions and a series of answers corresponding to the series of questions can be obtained for one user as the features of the user for modeling.

And step 112, adding the determined multiple characteristics for modeling of the N target users into a characteristic pool by the first agent.

Thus, a series of questions of the N users and corresponding answers of the series of questions can be obtained as the characteristics for modeling.

Optionally, as shown in fig. 2, a method for processing personal data based on zero-knowledge proof provided in an embodiment of this specification may further include:

step 114, the first agent trains the target model based on the features in the feature pool.

The goal model may be used to predict preset parameters for a specified user of the service provider, which may be used by the service provider to determine whether to provide preset services to the specified user.

Taking a service provider as an e-commerce company as an example, a user is designated as a new user, the preset parameter may be the preference degree of the user for a certain type of goods, and when the preference degree of the user for the certain type of goods is predicted to be higher, the type of goods may be recommended to the user.

In one example, a first agent performs unsupervised training based on features in a pool of features to obtain a target model.

In another example, the first agent may determine a tag of a user corresponding to a feature in the feature pool, and specifically may determine the tag of the user corresponding to the feature in the feature pool based on user data accumulated by the service provider itself (e.g., whether the user purchases a certain item); and then, based on the labels of the users corresponding to the features in the feature pool and the features in the feature pool, carrying out supervised training on the target model.

The personal data processing method based on zero-knowledge proof provided by the embodiments of the present specification may provide modeling feature data for a service provider on the premise of protecting personal privacy of a user, and may further enable a first agent representing the service provider to train a target model based on features provided by second agents representing a plurality of users, so as to help the service provider to implement machine learning modeling to solve a problem of whether to provide a preset service to a specified user.

Optionally, as shown in fig. 3, a method for processing personal data based on zero-knowledge proof provided in an embodiment of the present specification may further include:

and step 116, repeatedly executing the step for the designated user to obtain a plurality of preset questions of the designated user and answers corresponding to the preset questions.

The designating step includes the above-mentioned steps 102 to 108. The designated user may serve either a new user or an old user of the provider.

And step 118, inputting a plurality of preset questions of the specified user and answers corresponding to the preset questions as features into the target model by the first agent to obtain preset parameters of the specified user.

And step 120, the first agent determines whether to provide preset service for the specified user based on the preset parameters of the specified user.

As described above, taking the service provider as an e-commerce company as an example, the user is designated as a new user, the preset parameter may be the preference degree of the user for a certain type of goods, and when the preference degree of the user for the certain type of goods is predicted to be higher, the certain type of goods may be recommended to the user.

It can be understood that the preset parameters and the preset services can be flexibly set according to actual application scenarios, which is not limited in this specification.

The method for processing personal data based on zero-knowledge proof provided by the embodiments of the present specification may provide modeling feature data for a service provider on the premise of protecting personal privacy of a user, and may further enable a first agent representing the service provider to train a target model based on features provided by second agents representing a plurality of users, help the service provider to achieve the purpose of machine learning modeling, and make a prediction on a preset parameter of a specified user by using the established target model, thereby making a decision whether to provide a preset service to the specified user.

Next, a method for processing personal data based on zero-knowledge proof provided by the embodiment of the present specification is described with reference to the interaction diagram shown in fig. 4.

As shown in fig. 4, an embodiment of the present disclosure provides a method for processing personal data based on zero-knowledge proof, which may include the following steps:

step 1.1, the service provider 11 authorizes the first agent 12 to enable the first agent 12 to collect user characteristics on its behalf, apply the collected user characteristics for modeling, make predictions using the established model, and so forth.

Step 1.2, the target user 21 authorizes the second agent 22, so that the second agent 22 can access its personal database on its behalf, in which the network transaction information, network browsing information, personal credit and personal basic information (such as sex, age, city, etc.) of the target user 21 can be stored.

In practical application, one user can maintain one personal database for one service provider, and also can maintain one personal database for a plurality of service providers.

Step 2, the first agent 12 presents a preset question to the second agent on behalf of the service provider 11 to obtain a corresponding answer.

Step 3.1, the second agent 22 queries the personal database 23 of the target user 21 on behalf of the target user 21 to obtain answers to the predetermined questions.

Step 3.2, the personal database 23 of the target user 21 returns the answer to the preset question to the second agent 22.

Step 3.3, the second agent 22 sends the answer to the preset question to the zero knowledge proof algorithm executor 24.

Step 3.4, the zero knowledge proof algorithm executor 24 generates a proof containing the answer to the preset question and returns to the second agent 22.

Step 4, the second agent 22 sends the proof to the first agent 12.

Step 5.1, the first agent 12 sends a proof to the zero knowledge proof algorithm executor 13 to verify that the proof is correct.

Step 5.2, the zero knowledge proof of knowledge algorithm executor 13 sends the verification result to the first agent 12.

And 6, when the verification result is correct, the first agent 12 adds the preset question and the answer thereof into the feature pool, when the features in the feature pool are sufficient and the number of samples is large enough, the first agent 12 trains the target model 14 based on the features in the feature pool, the target model 14 can be used for predicting preset parameters of the specified user, and the preset parameters can be used for the service provider 11 to determine whether to provide preset services for the specified user.

Step 7.1, the designated user 31 authorizes another second agent 32 to access his personal database on his behalf.

Step 7.2, the first agent 12 interacts with another second agent 32 to derive a set of characteristics of the specified user 31.

And 7.3, the first agent 12 inputs a series of characteristics of the designated user 31 into the trained target model, and predicts to obtain preset parameters of the designated user 31.

Having obtained the preset parameters of the specified user 31, the service provider 11 can make a decision whether to provide the preset service to the specified user 31 based on the preset parameters.

As shown in fig. 5, an embodiment of the present specification further provides a zero-knowledge proof-based personal data processing method, which may be applied to the first agent, and the method may include:

step 502, sending a preset question to a second agent, so that the second agent queries a personal database of a target user to obtain an answer to the preset question, generates a proof containing the answer through a zero-knowledge proof algorithm, and returns the proof, wherein the second agent obtains the authorization of the target user and can represent the agent of the target user.

And step 504, verifying the correctness of the proof based on the zero-knowledge proof algorithm, and obtaining the answer of the preset question from the proof after the correctness is verified.

Step 506, determining the preset questions and the answers of the preset questions as features for modeling.

It should be noted that, the method for processing personal data based on zero knowledge proof provided in the embodiment of this specification corresponds to the method for processing personal data based on zero knowledge proof provided in fig. 1, and can achieve the same technical effects, and details can refer to the method shown in fig. 1 and are not described again.

As shown in fig. 6, an embodiment of the present specification further provides a zero-knowledge proof-based personal data processing method, which may be applied to the second agent, where the method may include:

step 602, receiving a preset question sent by a first agent, wherein the first agent is an agent which obtains authorization of a service provider and can represent the service provider.

Step 604, inquiring a personal database of a target user to obtain an answer to the preset question, and generating a proof containing the answer through a zero-knowledge proof algorithm.

Step 606, the proof is sent to the first agent, so that the first agent verifies the correctness of the proof based on the zero-knowledge proof algorithm, after the proof passes the verification, an answer of the preset question is obtained from the proof, and the preset question and the answer of the preset question are determined as features for modeling.

The above is a description of the method embodiments provided in this specification, and a description of a zero-knowledge proof-based personal data processing system provided in the embodiments of this specification is provided below.

As shown in fig. 7, a personal data processing system based on zero-knowledge proof provided by an embodiment of the present specification may include: a first agent 71 and a second agent 72, wherein the first agent 71 is an agent that obtains authorization from and can represent a service provider, and the second agent 72 is an agent that obtains authorization from and can represent a target user of the service provider.

The first agent 71 is configured to send a preset question to the second agent, so that the second agent queries the personal database of the target user to obtain an answer to the preset question, generates a proof including the answer through a zero-knowledge proof algorithm, and returns the proof; verifying the correctness of the proof based on the zero-knowledge proof algorithm, and acquiring the answer of the preset question from the proof after the correctness is verified; and determining the preset questions and the answers of the preset questions as features for modeling.

A second agent 72, configured to receive the preset question sent by the first agent; inquiring a personal database of a target user to obtain answers of the preset questions, generating a proof containing the answers through a zero-knowledge proof algorithm, sending the proof to the first agent, enabling the first agent to verify the correctness of the proof based on the zero-knowledge proof algorithm, obtaining the answers of the preset questions from the proof after the proof passes the verification, and determining the preset questions and the answers of the preset questions as features for modeling.

It should be noted that the personal data processing system based on zero knowledge proof provided in the embodiment of this specification corresponds to the personal data processing method based on zero knowledge proof provided in fig. 1, and can obtain the same technical effect, and details can refer to the method shown in fig. 1, and are not described again.

The electronic device provided in this specification is described below.

Fig. 8 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification. Referring to fig. 8, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.

The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 8, but that does not indicate only one bus or one type of bus.

And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.

The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the personal data processing device based on the zero-knowledge proof on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:

Or, the processor executes the program stored in the memory, and is specifically configured to perform the following operations:

The above-mentioned personal data processing method based on zero knowledge proof as disclosed in the embodiment shown in fig. 5 or fig. 6 of the present specification can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in one or more embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with one or more embodiments of the present disclosure may be embodied directly in hardware, in a software module executed by a hardware decoding processor, or in a combination of the hardware and software modules executed by a hardware decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

The electronic device may further execute the method for processing personal data based on zero-knowledge proof according to the embodiment shown in fig. 5 or fig. 6, which is not described herein again.

Of course, besides the software implementation, the electronic device in this specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.

This specification embodiment also proposes a computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a portable electronic device comprising a plurality of application programs, are capable of causing the portable electronic device to perform the method of the embodiment shown in fig. 5, and in particular to perform the following operations:

This specification embodiment also proposes a computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a portable electronic device comprising a plurality of application programs, are capable of causing the portable electronic device to perform the method of the embodiment shown in fig. 6, and in particular to perform the following operations:

The following is a description of the apparatus provided in this specification.

As shown in fig. 9, one embodiment of the present specification provides a personal data processing apparatus 900 based on zero knowledge proof, and in one software implementation, the apparatus 900 may include: a questioning module 901, a verification module 902 and a feature determination module 903.

The question asking module 901 is configured to send a preset question to a second agent, so that the second agent queries a personal database of a target user to obtain an answer to the preset question, generates a proof including the answer through a zero-knowledge proof algorithm, and returns the proof, where the second agent obtains the authorization of the target user and can represent the target user.

A verifying module 902, configured to verify correctness of the proof based on the zero-knowledge proof algorithm, and obtain an answer to the preset question from the proof after the correctness is verified.

A feature determining module 903, configured to determine the preset question and the answer to the preset question as features for modeling.

It should be noted that the personal data processing apparatus 900 based on zero knowledge proof can implement the method of fig. 5 and achieve the same technical effect, and the detailed content may refer to the method shown in fig. 5 and will not be described again.

As shown in fig. 10, one embodiment of the present specification provides a personal data processing device 1000 based on zero knowledge proof, and in one software implementation, the device 1000 may include: a receiving module 1001, a querying module 1002 and a sending module 1003.

A receiving module 1001, configured to receive a preset question sent by a first agent, where the first agent is an agent that obtains authorization of a service provider and can represent the service provider.

The query module 1002 is configured to query a personal database of a target user to obtain an answer to the preset question, and generate a proof including the answer through a zero-knowledge proof algorithm.

A sending module 1003, configured to send the proof to the first agent, so that the first agent verifies the correctness of the proof based on the zero-knowledge proof algorithm, obtains an answer to the preset question from the proof after the verification passes, and determines the preset question and the answer to the preset question as features for modeling.

It should be noted that the personal data processing apparatus 1000 based on zero knowledge proof can implement the method shown in fig. 6 and achieve the same technical effect, and the detailed content may refer to the method shown in fig. 6 and is not repeated.

While certain embodiments of the present disclosure have been described above, other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

In short, the above description is only a preferred embodiment of the present disclosure, and is not intended to limit the scope of the present disclosure. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of the present disclosure should be included in the scope of protection of one or more embodiments of the present disclosure.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a plurality of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

Claims

1. A method for personal data processing based on zero-knowledge proof, comprising:

the second agent queries the personal database of the target user to obtain an answer to the preset question, judges whether the preset question can leak the privacy of the target user, and if the preset question cannot leak, generates a proof containing the answer through a zero-knowledge proof algorithm and sends the proof to the first agent, wherein the answer to the preset question cannot leak the sensitive information or the personal privacy of the target user, the preset question is a question related to a training target model, the personal database of the target user is controlled by the individual of the target user, and the personal database of the target user stores some personal data of the target user;

and the first agent determines the preset question and the answer of the preset question as features for modeling, the features are used for training the target model, the target model is used for predicting preset parameters of a specified user of the service provider, and the preset parameters are used for the service provider to determine whether to provide preset services for the specified user.

2. The method of claim 1, further comprising:

repeatedly executing the step of appointing the target user for multiple times so as to take multiple preset questions of the target user and answers corresponding to the multiple preset questions as multiple features for modeling, and adding the multiple features into a feature pool;

wherein the specifying step comprises: and the first agent sends a preset question to a second agent, and the first agent determines the preset question and the answer of the preset question as the characteristics for modeling.

3. The method of claim 2, further comprising:

respectively and repeatedly executing the appointed steps for more users to respectively take a plurality of preset questions of the more users and answers corresponding to the preset questions as a plurality of characteristics for modeling, and adding the characteristics into a characteristic pool;

wherein one user corresponds to one second agent.

4. The method of claim 3, further comprising:

the first agent trains a target model based on features in the pool of features.

5. The method of claim 4, wherein the first agent trains a target model based on features in the pool of features, comprising:

the first agent determines a label of a user corresponding to the feature in the feature pool;

and the first agent trains a target model based on the labels of the users corresponding to the features in the feature pool and the features in the feature pool.

6. The method of claim 5, wherein the first agent determining a label of a user to which a feature in the pool of features corresponds comprises:

and the first agent determines the label of the user corresponding to the feature in the feature pool based on the user data accumulated by the service provider.

7. The method of any of claims 4-6, further comprising:

repeatedly executing the step of appointing for the appointed user to obtain a plurality of preset questions of the appointed user and answers corresponding to the preset questions;

the first agent inputs a plurality of preset questions of the specified user and answers corresponding to the preset questions as features into the target model to obtain preset parameters of the specified user;

and the first agent determines whether to provide preset service for the specified user based on the preset parameters of the specified user.

8. The method of claim 1, wherein the first and second light sources are selected from the group consisting of,

the zero knowledge proof algorithm includes a zero knowledge succinct no-interaction knowledge authentication zkSNARK.

9. The method of claim 1, before the first agent sends the preset question to the second agent, the method further comprising:

the second agent accepts the authority granted by the target user to control the personal database of the target user;

and the first agent receives the authority which is granted by the service provider and represents the service provider to acquire the user characteristics.

10. The method of claim 1, after the second agent queries the personal database of the target user for an answer to the preset question, before generating a proof containing the answer based on a zero-knowledge proof algorithm, further comprising:

and if the second agent judges that the privacy of the target user is leaked in response to the preset question, the second agent refuses to provide the answer of the preset question to the first agent.

11. A method for personal data processing based on zero-knowledge proof, comprising:

sending a preset question to a second agent, so that the second agent queries a personal database of a target user to obtain an answer to the preset question, judging whether the preset question can leak the privacy of the target user, and if the preset question cannot be leaked, generating a proof containing the answer through a zero-knowledge proof algorithm and returning the proof, wherein the second agent obtains the authorization of the target user and can represent the target user, the answer to the preset question cannot leak the sensitive information or the personal privacy of the target user, the preset question is a question related to a training target model, the personal database of the target user is controlled by the individual of the target user, and the personal database of the target user stores some personal data of the target user;

and determining the preset questions and the answers of the preset questions as features for modeling, wherein the features are used for training the target model, the target model is used for predicting preset parameters of a specified user of a service provider, and the preset parameters are used for the service provider to determine whether to provide preset services for the specified user.

12. A method for personal data processing based on zero-knowledge proof, comprising:

inquiring a personal database of a target user to obtain an answer to the preset question, judging whether the preset question is leaked to the privacy of the target user or not, if not, generating a proof containing the answer through a zero-knowledge proof algorithm, wherein the answer to the preset question cannot leak the sensitive information or the personal privacy of the target user, the preset question is a question related to a training target model, the personal database of the target user is controlled by the individual of the target user, and some personal data of the target user are stored in the personal database of the target user;

and sending the certification to the first agent, so that the first agent verifies the correctness of the certification based on the zero-knowledge certification algorithm, acquires an answer to the preset question from the certification after the verification is passed, and determines the preset question and the answer to the preset question as features for modeling, wherein the features are used for training the target model, the target model is used for predicting preset parameters of a specified user of the service provider, and the preset parameters are used for the service provider to determine whether to provide preset services for the specified user.

13. A zero knowledge proof based personal data processing system comprising: a first agent and a second agent, wherein the first agent is an agent that obtains authorization from a service provider and is capable of representing the service provider, and the second agent is an agent that obtains authorization from a target user of the service provider and is capable of representing the target user;

the first agent is used for sending a preset question to the second agent so that the second agent queries a personal database of a target user to obtain an answer to the preset question, generates a proof containing the answer through a zero-knowledge proof algorithm and returns the proof; verifying the correctness of the proof based on the zero-knowledge proof algorithm, and acquiring the answer of the preset question from the proof after the correctness is verified; determining the preset questions and answers of the preset questions as features for modeling, wherein the features are used for training a target model, the target model is used for predicting preset parameters of a specified user of the service provider, and the preset parameters are used for the service provider to determine whether to provide preset services for the specified user;

the second agent is used for receiving the preset problem sent by the first agent; inquiring a personal database of a target user to obtain an answer to the preset question, judging whether the privacy of the target user is leaked when the preset question is answered, if not, a proof containing the answer is generated by a zero-knowledge proof algorithm and sent to the first agent, such that the first agent verifies the correctness of the proof based on the zero-knowledge proof algorithm, obtaining an answer to the preset question from the proof after the verification is passed, and determining the preset question and the answer to the preset question as features for modeling, wherein the answer to the preset question cannot reveal sensitive information or personal privacy of the target user, the preset questions are questions related to training the target model, the personal database of the target user is controlled by the target user person, the personal database of the target user stores some personal data of the target user.

14. A zero knowledge proof based personal data processing apparatus comprising:

the question-asking module is used for sending a preset question to a second agent so that the second agent queries a personal database of a target user to obtain an answer to the preset question, judging whether the preset question can leak the privacy of the target user or not, if the preset question cannot be leaked, generating a certificate containing the answer through a zero-knowledge certificate algorithm and returning the certificate, wherein the second agent obtains the authorization of the target user and can represent the target user, the answer to the preset question cannot leak the sensitive information or the personal privacy of the target user, the preset question is a question related to a training target model, the personal database of the target user is controlled by the target user, and the personal database of the target user stores some personal data of the target user;

and the feature determination module is used for determining the preset questions and answers of the preset questions as features for modeling, the features are used for training the target model, the target model is used for predicting preset parameters of a specified user of a service provider, and the preset parameters are used for the service provider to determine whether to provide preset services for the specified user.

15. A zero knowledge proof based personal data processing apparatus comprising:

the query module is used for querying a personal database of a target user to obtain an answer to the preset question, judging whether the preset question can leak the privacy of the target user or not, and if the preset question cannot leak, generating a certificate containing the answer through a zero-knowledge certificate algorithm, wherein the answer to the preset question cannot leak the sensitive information or the personal privacy of the target user, the preset question is a question related to a training target model, the personal database of the target user is controlled by the individual of the target user, and the personal database of the target user stores some personal data of the target user;

the sending module is configured to send the proof to the first agent, so that the first agent verifies the correctness of the proof based on the zero-knowledge proof algorithm, obtains an answer to the preset question from the proof after the proof passes the verification, and determines the preset question and the answer to the preset question as features for modeling, where the features are used to train the target model, the target model is used to predict preset parameters of an appointed user of the service provider, and the preset parameters are used by the service provider to determine whether to provide preset services for the appointed user.

16. An electronic device, comprising:

a processor; and

17. A computer-readable storage medium storing one or more programs that, when executed by an electronic device including a plurality of application programs, cause the electronic device to:

18. An electronic device, comprising:

a processor; and

19. A computer-readable storage medium storing one or more programs that, when executed by an electronic device including a plurality of application programs, cause the electronic device to: