阅读说明：本技术 一种基于跨链的众包数据隐私保护方法 (Crowdsourcing data privacy protection method based on cross-chain ) 是由 林晖 胡嘉 汪晓丁 彭梦瑶 于 2021-06-25 设计创作，主要内容包括：本发明公开了一种基于跨链的众包数据隐私保护方法；本发明新的众包平台A通过其对应的众包主链A提交跨链查询请求；中继链对跨链查询请求进行有效验证并将验证结果进行广播,并将众包主链B对应的转换节点B的路由信息反馈给众包主链A所对应的转换节点A；转换节点A根据中继链反馈的路由信息,直接将跨链查询请求转发给转换节点B,然后根据众包主链B所返回的工人信息对主链A进行信息反馈；本发明为了保护众包中的任务数据隐私安全,将区块链跨链技术结合到众包技术中,提出了一种基于跨链的众包数据隐私保护方法,通过减少众包平台中的恶意工人比例,从而达到保护众包的任务数据隐私的目的。(The invention discloses a cross-chain-based crowdsourcing data privacy protection method; the novel crowdsourcing platform A submits a cross-chain query request through a corresponding crowdsourcing main chain A; the relay chain effectively verifies the cross-chain query request, broadcasts a verification result and feeds back routing information of the conversion node B corresponding to the crowdsourcing main chain B to the conversion node A corresponding to the crowdsourcing main chain A; the conversion node A directly forwards the cross-link query request to the conversion node B according to the routing information fed back by the relay link, and then feeds back the information of the main chain A according to the worker information returned by the crowdsourcing main chain B; the invention provides a cross-chain-based crowdsourcing data privacy protection method by combining a block chain cross-chain technology into a crowdsourcing technology in order to protect the privacy security of task data in crowdsourcing, and the purpose of protecting the privacy of the crowdsourced task data is achieved by reducing the proportion of malicious workers in a crowdsourcing platform.)

1. A cross-chain based crowd-sourced data privacy protection method is characterized by comprising the following steps:

s1, submitting a cross-chain query request by the new crowdsourcing platform A through the corresponding crowdsourcing main chain A, and requesting to query the crowdsourcing main chain B for relevant data of historical task completion conditions of crowdsourcing workers;

s2, the relay chain effectively verifies the cross-chain query request and broadcasts a verification result, then the verification result is stored on the relay chain, and the routing information of the conversion node B corresponding to the crowdsourcing main chain B is fed back to the conversion node A corresponding to the crowdsourcing main chain A;

and S3, the conversion node A directly forwards the cross-link query request to the conversion node B according to the route information fed back by the relay link, and then feeds back the information of the main chain A according to the worker information returned by the crowd-sourced main chain B.

2. The cross-chain based crowd-sourced data privacy protection method as claimed in claim 1,

the cross-link structure mainly comprises the crowdsourcing main chain A corresponding to the crowdsourcing platform A, the crowdsourcing main chain B corresponding to the crowdsourcing platform B and the relay link provided by the method.

3. The method for protecting privacy of crowd-sourced data based on cross-chain as claimed in claim 1, wherein the step S2 specifically includes:

s21, the cross-chain gateway A which is responsible for monitoring the cross-chain request on the crowdsourcing main chain A receives the cross-chain query request, and forwards the cross-chain query request to the conversion node A corresponding to the crowdsourcing main chain A;

s22, the conversion node A converts the cross-link query request into a general format of the message on the relay link, and forwards the converted cross-link query request to a maintenance node in the relay link;

and S23, the maintenance node verifies the validity of the cross-chain inquiry request, broadcasts the verification result, stores the verification result on the relay chain after broadcasting, and feeds back the routing information of the conversion node B corresponding to the crowdsourcing main chain B to the conversion node A.

4. The cross-chain based crowdsourced data privacy protection method as claimed in claim 3, wherein for the selection of the conversion node, a dynamic election mode is adopted, specifically:

firstly, judging whether the conversion node exists or not, if not, entering an election program, otherwise, judging whether the response of the conversion node is overtime or not, and if so, entering the election program;

if the conversion node exists and the response is not overtime, judging whether other node requests to become the conversion node or not, if so, entering an election program;

after entering an election program, firstly waiting for preset time and collecting election requests of a worker end;

selecting the person with the largest current credit from the worker end requesting to become the conversion node;

if the credit is the largest, setting the credit as the conversion node;

and if the credit is more than one, selecting the worker end with the least number of executed tasks and setting the worker end as the conversion node.

5. The cross-chain based crowdsourcing data privacy protection method according to claim 3, wherein the common format of the messages on the relay chain is a preset cross-chain message format;

the cross-chain message format mainly comprises SBC, DBC, INDEX, TIMESTAMMP, HISREP, PROOF and EXTRA;

the SBC field represents a source crowdsourcing backbone of the cross-link query request, and is represented by an ID of the crowdsourcing backbone, and the ID of the crowdsourcing backbone is generated when the trunk link is added;

the DBC field represents a target crowdsourcing backbone to which the cross-chain query request arrives, the target crowdsourcing backbone providing credit-related data for queried workers;

the INDEX field represents an INDEX of the cross-chain query request, and is used for querying the cross-chain query request;

the TIMESTAMP field represents the timestamp generated by the cross-chain query request;

the HISREP field is inquired credit related data of workers, encryption can be carried out according to requirements in practical application, and the HISREP field is filled with the credit related data of the workers in a feedback stage of the cross-chain inquiry request;

the PROOF field represents a relevant certificate after the relay chain verifies the cross-chain inquiry request, and is used for verifying the authenticity of the inquiry request;

the EXTRA field represents a custom field, which can be defined in practical application according to the relevant requirements of the service.

6. The method as claimed in claim 3, wherein the step S3 specifically includes:

s31, the conversion node A directly forwards the cross-link query request to a conversion node B according to the routing information fed back by the relay link;

s32, the converting node B converts the cross-link query request from the general format on the relay link to a format applicable to the crowdsourcing main link B, and forwards the cross-link query request after converting the format to a cross-link gateway B corresponding to the crowdsourcing main link B;

s33, the cross-chain gateway B forwards the cross-chain query request to the crowdsourcing main chain B, and the crowdsourcing main chain B calls an intelligent contract to extract credit related data of workers;

s34, the crowdsourcing main chain B fills the extracted credit data into the relevant fields of the return message and forwards the return message to the conversion node B, and the conversion node B feeds the return message back to the conversion node A;

s35, the conversion node A converts the format of the return message into a format suitable for the crowdsourcing main chain A and forwards the converted return message to the crowdsourcing main chain A, and the crowdsourcing main chain A analyzes the return message and extracts relevant data of the historical task completion condition;

s36, the crowdsourcing main chain A calls a related intelligent contract to read related data of the historical task completion condition, and the crowdsourcing workers are authenticated and judged.

Technical Field

The invention relates to the technical field of block chains and crowdsourcing, in particular to a cross-chain based crowdsourcing data privacy protection method.

Background

Crowdsourcing refers to aggregating many distributed online computing resources with a population of people with specific knowledge or skills on the internet to solve some complex problems that require large amounts of computing resources to reduce cost and time. At present, data acquisition based on a crowdsourcing technology has many advantages, such as low cost, wide coverage range, high flexibility, wide application scene and the like. Due to the advantages, the crowdsourcing technology is widely applied to various fields such as medical health, transportation, environmental monitoring and the like. Furthermore, although the crowdsourcing technology can provide efficient data acquisition services for various fields, the data privacy protection of the crowdsourcing task still faces a great threat, and the development of the crowdsourcing technology is seriously influenced. At this time, the advent of the blockchain technology provides new ideas and directions for solving the above problems. Blockchain technology, a novel technology, with decentralized and non-tamper features, is beginning to be introduced into crowd-sourced data privacy protection.

The traditional crowdsourcing data privacy protection work mainly relates to the protection of position data privacy, user data privacy and task data privacy; how to effectively fuse the block chain technology into the crowdsourcing technology becomes a key for solving the crowdsourcing data privacy problem, and the conventional crowdsourcing data privacy protection strategy based on the block chain mainly utilizes a distributed structure with decentralized block chains and the advantages of undeniability, non-falsification and the like of the distributed structure to control the threat of malicious users to the privacy involved in the crowdsourcing data acquisition process so as to improve the completion rate and the efficiency of task data acquisition.

In conclusion, how to effectively integrate the block chain technology into the crowdsourcing technology has become a research hotspot for solving the crowdsourcing data privacy problem, and some related research results appear, Zou et al (CrowdLPS: A block chain-based location-privacy-preserving mobile crowdsourcing system, 2019, 16 (6): 4206) propose an effective crowdsourcing location privacy protection model CrowdLPS based on the block chain. The idea of block chain is introduced into the model, and firstly, the non-repudiation and the non-tamper property of the information are realized by adopting a distributed structure and a consensus mechanism. And secondly, the data perception quality is improved and the privacy of the staff is protected by a method of pre-registration and final selection of the workers. Lin et al (SecBCS: a secure and private-preserving block-based Crowdsourcing system, 2020, 63 (3): 1-14) propose a block chain-based crowdsourcing system SecBCS with an incentive mechanism, which ensures the security and privacy of the identity data in the crowdsourcing system by using an intelligent contract, and ensures the security of the crowdsourcing identity privacy data to some extent. Zhang et al (Privacy-forecasting Management for block-Based Mobile browsing, 2020, 1-9) propose a Reputation Management scheme Based on a block chain, which can identify malicious users in a crowd-sourced scene, and protect the Privacy of users at the same time. The scheme utilizes the safety and opening characteristics of the block chain to construct a reliable and efficient credit management platform, the credit value is calculated and updated on the block chain in a public mode, the credit value is reliable, and a verifiable secret sharing scheme is introduced into a feature trust algorithm to prevent personal information of a user from being leaked. Guo et al (FedCrowd: A Federated and Privacy-Preserving Crowdsourcing Platform on Block chain, 2020) propose a FedCrowd Platform for Privacy protection based on block chain technology. FedCrowd designs and implements a smart contract-based secure task matching protocol designed for decentralization, which allows users to securely perform keyword and scope-based queries on crowd-sourced task indices without the need for shared keys. Peng et al (A Privacy-monitoring Crowdensing System with Muti-Blockchain, 2020, 1944-. Zhang et al (PRVB: influencing Privacy-Preserving and Reliable Vehicular Crowdsening via Blockchain Oracle, 2020) proposed a new Privacy-Preserving and Reliable Vehicular crowdsourcing scheme PRVB to protect data Privacy and non-connectability between participant vehicles and sensory data. Zhu et al (Hybrid blockchain design for privacy preserving crowd sourcing platform, 2019, 26-33) propose a novel Hybrid blockchain-based distributed crowd sourcing platform, which controls user access by deploying intelligent contracts on public chains and sub-chains, protects user privacy, and can effectively balance privacy and transparency of transactions. Xu et al (A block-powered crown serving method with a private prediction in mobile environment, 2019, 6 (6): 1407 and 1419) propose a mobile crowdsourcing scheme BPCM based on a block chain, and firstly design a mobile crowdsourcing framework based on the block chain to protect the privacy of participants and keep the integrity of service request and service provision. Then, a density-based noisy application space clustering algorithm and an improved dynamic programming algorithm are adopted to cluster the requesters respectively and generate a service strategy. Through the strategy, the integrity of privacy and information is effectively protected. Yang et al (A block-based location privacy-preserving traversing system, 2019, 94: 408-418) propose a block chain-based crowd-sourcing location privacy protection framework for protecting the location privacy of users in a crowd-sourcing system, and simultaneously improve the success rate of assigned tasks through reward-based task allocation and hide the identity information of the users by using the anonymity characteristic of the block chain technology.

The existing method still has the problem of neglecting the cross-platform execution task and the worker identity authentication strategy; therefore, designing a reasonable and effective cross-chain-based crowdsourcing data privacy protection method by combining the characteristics of the blockchain and the crowdsourcing technology becomes a technical problem to be solved by technical personnel in the field.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: the cross-chain-based crowdsourcing data privacy protection method can effectively solve the cross-platform authentication problem of crowdsourcing workers, and therefore the purpose of protecting task data privacy is achieved.

In order to solve the technical problems, the invention adopts the technical scheme that: a cross-chain based crowd-sourced data privacy protection method comprises the following steps:

s1, submitting a cross-chain query request by the new crowdsourcing platform A through the corresponding crowdsourcing main chain A, and requesting to query the crowdsourcing main chain B for relevant data of historical task completion conditions of crowdsourcing workers;

s2, the relay chain effectively verifies the cross-chain query request and broadcasts a verification result, then the verification result is stored on the relay chain, and the routing information of the conversion node B corresponding to the crowdsourcing main chain B is fed back to the conversion node A corresponding to the crowdsourcing main chain A;

and S3, the conversion node A directly forwards the cross-link query request to the conversion node B according to the route information fed back by the relay link, and then feeds back the information of the main chain A according to the worker information returned by the crowd-sourced main chain B.

The invention has the beneficial effects that: existing cross-chain operations can be basically divided into cross-chain queries and cross-chain transactions. The cross-chain authentication designed by the method belongs to cross-chain query, cross-chain query is carried out on relevant data of historical task completion conditions of crowdsourcing workers, and the crowdsourcing workers are authenticated. In the method, only the crowdsourcing main chain corresponding to each crowdsourcing platform participates in cross-chain query, and the crowdsourcing main chain is only responsible for maintaining and storing relevant data of historical task completion conditions of crowdsourcing workers in the crowdsourcing platform, so that queried data cannot affect protection of task data privacy, and the number of malicious workers introduced by the crowdsourcing platform is effectively controlled.

Drawings

Fig. 1 is a schematic diagram of a cross-chain-based crowd-sourced data privacy protection method according to an embodiment of the present invention;

fig. 2 is a schematic flowchart of a cross-chain-based crowd-sourced data privacy protection method according to an embodiment of the present invention.

Detailed Description

In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.

The most key concept of the invention is as follows: under the multi-sub-chain architecture, relevant data of historical task completion conditions of crowdsourcing workers are recorded on a credit main chain, and authenticity of the data is effectively guaranteed by using non-tamper property of a block chain. However, different crowdsourcing platforms may adopt different types of heterogeneous block chains to construct a multi-subchain architecture, and the heterogeneity among the platforms prevents the platforms from mutually sharing historical task completion data of crowdsourcing workers to authenticate and judge the crowdsourcing workers. Therefore, in order to solve the problem of crowdsourcing worker authentication between heterogeneous crowdsourcing platforms, the method provides a multi-chain architecture supporting heterogeneous cross-chain by utilizing a heterogeneous cross-chain technology, and provides a cross-chain query authentication mechanism for each heterogeneous crowdsourcing platform. Under the cross-chain architecture, related data of historical task completion conditions of crowdsourcing workers in the platform can be shared among different crowdsourcing platforms, scientific authentication strategies are provided for the crowdsourcing workers who execute tasks across the platform, the number of malicious workers introduced into the crowdsourcing platforms is effectively controlled, and therefore the purpose of protecting privacy is achieved.

Referring to fig. 1 and fig. 2, a cross-chain based method for protecting privacy of crowdsourced data includes the steps of:

s1, submitting a cross-chain query request by the new crowdsourcing platform A through the corresponding crowdsourcing main chain A, and requesting to query the crowdsourcing main chain B for relevant data of historical task completion conditions of crowdsourcing workers;

s2, the relay chain effectively verifies the cross-chain query request and broadcasts a verification result, then the verification result is stored on the relay chain, and the routing information of the conversion node B corresponding to the crowdsourcing main chain B is fed back to the conversion node A corresponding to the crowdsourcing main chain A;

and S3, the conversion node A directly forwards the cross-link query request to the conversion node B according to the route information fed back by the relay link, and then feeds back the information of the main chain A according to the worker information returned by the crowd-sourced main chain B.

From the above description, the beneficial effects of the present invention are: existing cross-chain operations can be basically divided into cross-chain queries and cross-chain transactions. The cross-chain authentication designed by the method belongs to cross-chain query, cross-chain query is carried out on relevant data of historical task completion conditions of crowdsourcing workers, and the crowdsourcing workers are authenticated. In the method, only the crowdsourcing main chain corresponding to each crowdsourcing platform participates in cross-chain query, and the crowdsourcing main chain is only responsible for maintaining and storing relevant data of historical task completion conditions of crowdsourcing workers in the crowdsourcing platform, so that queried data cannot affect protection of task data privacy, and the number of malicious workers introduced by the crowdsourcing platform is effectively controlled.

Further, the cross-link structure mainly includes the crowdsourcing main chain a corresponding to the crowdsourcing platform a, the crowdsourcing main chain B corresponding to the crowdsourcing platform B, and the relay link proposed by the method.

As can be seen from the above description, the interaction between the main chain a and the main chain B can be effectively realized through the relay function of the relay chain.

Further, the step S2 specifically includes:

s21, the cross-chain gateway A which is responsible for monitoring the cross-chain request on the crowdsourcing main chain A receives the cross-chain query request, and forwards the cross-chain query request to the conversion node A corresponding to the crowdsourcing main chain A;

s22, the conversion node A converts the cross-link query request into a general format of the message on the relay link, and forwards the converted cross-link query request to a maintenance node in the relay link;

and S23, the maintenance node verifies the validity of the cross-chain inquiry request, broadcasts the verification result, stores the verification result on the relay chain after broadcasting, and feeds back the routing information of the conversion node B corresponding to the crowdsourcing main chain B to the conversion node A.

As can be seen from the above description, the cross-link interaction is different from the general application program interaction, the heterogeneity of different block chains needs to be considered, and different block chains may adopt different message formats and cannot be directly interacted and shared.

Further, for the selection of the conversion node, a dynamic election mode is adopted, specifically:

firstly, judging whether the conversion node exists or not, if not, entering an election program, otherwise, judging whether the response of the conversion node is overtime or not, and if so, entering the election program;

if the conversion node exists and the response is not overtime, judging whether other node requests to become the conversion node or not, if so, entering an election program;

after entering an election program, firstly waiting for preset time and collecting election requests of a worker end;

selecting the person with the largest current credit from the worker end requesting to become the conversion node;

if the credit is the largest, setting the credit as the conversion node;

and if the credit is more than one, selecting the worker end with the least number of executed tasks and setting the worker end as the conversion node.

As can be seen from the above description, reliable workers are selected as the conversion nodes by comparing their credits with historical task conditions.

Further, the general format of the message on the relay link is a preset cross-link message format;

the cross-chain message format mainly comprises SBC, DBC, INDEX, TIMESTAMMP, HISREP, PROOF and EXTRA;

the SBC field represents a source crowdsourcing backbone of the cross-link query request, and is represented by an ID of the crowdsourcing backbone, and the ID of the crowdsourcing backbone is generated when the trunk link is added;

the DBC field represents a target crowdsourcing backbone to which the cross-chain query request arrives, the target crowdsourcing backbone providing credit-related data for queried workers;

the INDEX field represents an INDEX of the cross-chain query request, and is used for querying the cross-chain query request;

the TIMESTAMP field represents the timestamp generated by the cross-chain query request;

the HISREP field is inquired credit related data of workers, encryption can be carried out according to requirements in practical application, and the HISREP field is filled with the credit related data of the workers in a feedback stage of the cross-chain inquiry request;

the PROOF field represents a relevant certificate after the relay chain verifies the cross-chain inquiry request, and is used for verifying the authenticity of the inquiry request;

the EXTRA field represents a custom field, which can be defined in practical application according to the relevant requirements of the service.

Further, step S3 specifically includes:

s31, the conversion node A directly forwards the cross-link query request to a conversion node B according to the routing information fed back by the relay link;

s32, the converting node B converts the cross-link query request from the general format on the relay link to a format applicable to the crowdsourcing main link B, and forwards the cross-link query request after converting the format to a cross-link gateway B corresponding to the crowdsourcing main link B;

s33, the cross-chain gateway B forwards the cross-chain query request to the crowdsourcing main chain B, and the crowdsourcing main chain B calls an intelligent contract to extract credit related data of workers;

s34, the crowdsourcing main chain B fills the extracted credit data into the relevant fields of the return message and forwards the return message to the conversion node B, and the conversion node B feeds the return message back to the conversion node A;

s35, the conversion node A converts the format of the return message into a format suitable for the crowdsourcing main chain A and forwards the converted return message to the crowdsourcing main chain A, and the crowdsourcing main chain A analyzes the return message and extracts relevant data of the historical task completion condition;

s36, the crowdsourcing main chain A calls a related intelligent contract to read related data of the historical task completion condition, and the crowdsourcing workers are authenticated and judged.

According to the description, in order to protect the privacy security of the task data in crowdsourcing, the block chain cross-chain technology is combined into the crowdsourcing technology, the crowdsourcing data privacy protection method based on the cross-chain is provided, and the purpose of protecting the privacy of the crowdsourcing task data is achieved by reducing the proportion of malicious workers in a crowdsourcing platform.

Referring to fig. 1 and fig. 2, a first embodiment of the present invention is:

a cross-chain based crowd-sourced data privacy protection method is characterized in that a defined multi-chain cross-chain architecture is as follows: the multi-chain cross-chain architecture provided by the method mainly comprises three key parts, namely a relay chain, a cross-chain gateway, a crowdsourcing main chain and the like; the exchanged data is related to historical task completion conditions of crowdsourcing workers on each crowdsourcing platform, and the crowdsourcing workers are authenticated according to the data.

A cross-chain based crowd-sourced data privacy protection method comprises the following steps:

s1, submitting a cross-chain query request by the new crowdsourcing platform A through the corresponding crowdsourcing main chain A, and requesting to query the crowdsourcing main chain B for relevant data of historical task completion conditions of crowdsourcing workers;

and S2, the relay chain effectively verifies the cross-chain inquiry request and broadcasts a verification result, then stores the verification result on the relay chain, and feeds back the routing information of the conversion node B corresponding to the crowdsourcing main chain B to the conversion node A corresponding to the crowdsourcing main chain A.

After backbone a initiates a cross-link query request, the relay link needs to perform a series of authentication operations on the request.

The step S2 specifically includes:

s21, the cross-chain gateway A which is responsible for monitoring the cross-chain request on the crowd-sourced main chain A receives the cross-chain query request and forwards the cross-chain query request to the conversion node A corresponding to the crowd-sourced main chain A.

For the selection of the conversion node, a dynamic election mode is adopted, and the method specifically comprises the following steps:

firstly, judging whether the conversion node exists or not, if not, entering an election program, otherwise, judging whether the response of the conversion node is overtime or not, and if so, entering the election program;

if the conversion node exists and the response is not overtime, judging whether other node requests to become the conversion node or not, if so, entering an election program;

after entering an election program, firstly waiting for preset time and collecting election requests of a worker end;

selecting the person with the largest current credit from the worker end requesting to become the conversion node;

if the credit is the largest, setting the credit as the conversion node;

and if the credit is more than one, selecting the worker end with the least number of executed tasks and setting the worker end as the conversion node.

S22, the conversion node A converts the cross-link inquiry request into a general format of the message on the relay link, and forwards the converted cross-link inquiry request to the maintenance node in the relay link.

The cross-chain interaction is different from the interaction of a general application program, the heterogeneity of different block chains needs to be considered, different block chains may adopt different message formats and cannot be directly interacted and shared, therefore, the method designs a cross-chain message format used on a relay chain, and effectively eliminates interaction obstacles caused by heterogeneity.

The general format of the message on the relay link is a preset cross-link message format;

the cross-chain message format mainly comprises SBC, DBC, INDEX, TIMESTAMMP, HISREP, PROOF and EXTRA;

the SBC field represents a source crowdsourcing backbone of the cross-link query request, and is represented by an ID of the crowdsourcing backbone, and the ID of the crowdsourcing backbone is generated when the trunk link is added;

the DBC field represents a target crowdsourcing backbone to which the cross-chain query request arrives, the target crowdsourcing backbone providing credit-related data for queried workers;

the INDEX field represents an INDEX of the cross-chain query request, and is used for querying the cross-chain query request;

the TIMESTAMP field represents the timestamp generated by the cross-chain query request;

the HISREP field is inquired credit related data of workers, encryption can be carried out according to requirements in practical application, and the HISREP field is filled with the credit related data of the workers in a feedback stage of the cross-chain inquiry request;

the PROOF field represents a relevant certificate after the relay chain verifies the cross-chain inquiry request, and is used for verifying the authenticity of the inquiry request;

the EXTRA field represents a custom field, which can be defined in practical application according to the relevant requirements of the service.

And S23, the maintenance node verifies the validity of the cross-chain inquiry request, broadcasts the verification result, stores the verification result on the relay chain after broadcasting, and feeds back the routing information of the conversion node B corresponding to the crowdsourcing main chain B to the conversion node A.

And S3, the conversion node A directly forwards the cross-link query request to the conversion node B according to the route information fed back by the relay link, and then feeds back the information of the main chain A according to the worker information returned by the crowd-sourced main chain B.

The step S3 specifically includes:

s31, the conversion node A directly forwards the cross-link query request to a conversion node B according to the routing information fed back by the relay link;

s32, the converting node B converts the cross-link query request from the general format on the relay link to a format applicable to the crowdsourcing main link B, and forwards the cross-link query request after converting the format to a cross-link gateway B corresponding to the crowdsourcing main link B;

s33, the cross-chain gateway B forwards the cross-chain query request to the crowdsourcing main chain B, and the crowdsourcing main chain B calls an intelligent contract to extract credit related data of workers;

s34, the crowdsourcing main chain B fills the extracted credit data into the relevant fields of the return message and forwards the return message to the conversion node B, and the conversion node B feeds the return message back to the conversion node A;

s35, the conversion node A converts the format of the return message into a format suitable for the crowdsourcing main chain A and forwards the converted return message to the crowdsourcing main chain A, and the crowdsourcing main chain A analyzes the return message and extracts relevant data of the historical task completion condition;

s36, the crowdsourcing main chain A calls a related intelligent contract to read related data of the historical task completion condition, and the crowdsourcing workers are authenticated and judged.

Specifically, in order to protect the privacy and the safety of task data in crowdsourcing, the block chain cross-chain technology is combined into the crowdsourcing technology, the crowdsourcing data privacy protection method based on the cross-chain is provided, and the purpose of protecting the crowdsourcing task data privacy is achieved by reducing the proportion of malicious workers in a crowdsourcing platform.

The cross-chain-based crowdsourcing data privacy protection system comprises a crowdsourcing main chain A, a crowdsourcing main chain B and a relay chain, so as to realize the steps in the first embodiment.

In conclusion, the invention has the following beneficial effects:

(1) from privacy protection performance analysis: according to the method, the privacy protection performance is analyzed by taking the malicious worker ratio as an index, wherein the malicious worker ratio refers to the ratio of the number of malicious workers to the total number of workers;

(2) from multi-chain validation performance analysis: the invention analyzes the performance of the multi-chain from time consumption, verification success rate and request delay, wherein the time consumption represents the sum of time consumed for completing verification of all cross-chain requests in the system; the verification success rate represents the ratio of the number of requests that the system can successfully verify to the total number of requests per second; the request latency represents the difference between the time when the request is generated and the time when the request deployment is complete.

(3) From cross-chain query performance analysis: analyzing the cross-chain query performance from network overhead, CPU occupation rate and query time consumption, wherein the network overhead represents the communication overhead of the whole network during cross-chain query; the CPU occupancy rate represents the proportion of the CPU occupied when the cross-chain query is carried out; query elapsed time represents the total time elapsed from submission of the cross-chain request to the query feedback.

The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.