CN112528308A

CN112528308A - Artificial intelligence big data sharing system and method based on block chain

Info

Publication number: CN112528308A
Application number: CN202011505530.3A
Authority: CN
Inventors: 张庆; 李奕; 管绍朋; 崔旭; 岳涛
Original assignee: Linyi Dima Block Chain Network Technology Co ltd
Current assignee: Linyi Dima Block Chain Network Technology Co ltd
Priority date: 2020-12-18
Filing date: 2020-12-18
Publication date: 2021-03-19
Anticipated expiration: 2040-12-18
Also published as: CN112528308B

Abstract

The utility model discloses a sharing system and method of artificial intelligence big data based on block chain, comprising an application layer and a protocol layer; the application layer is used for a publisher to publish asset information and a consumer to use a data science tool; and the protocol layer is used for sending the algorithm required by the data science tool to the publisher through the contract management module, accessing the asset information published by the publisher, calculating the accessed asset information according to the algorithm of the data science tool, and sending the calculation result to the consumer. The consumer is authorized to use the computing service of the publisher, so that the data does not leave the publisher when the consumer computes, and the safety of the data is protected.

Description

Artificial intelligence big data sharing system and method based on block chain

Technical Field

The invention relates to the technical field of block chain application, in particular to a system and a method for sharing artificial intelligence big data based on a block chain.

Background

The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.

With the rapid development of artificial intelligence technology, the importance of data information in the emerging technology is increasingly highlighted. The data is used as fuel of the algorithm and forms a basis for adherence of artificial intelligence together with the calculation power. In the era of digital economy, data is generated and identified in an accelerated manner, and large-scale, various and unstructured data, which are called big data for short, are formed. Big data is different from traditional data in structural features, carrier channels, operation logic, business modes and the like, so that data governance and privacy protection bring new challenges to data sharing and data security. The traditional legal mode, social value and protection means are used for dealing with the data sharing and protection of big data and artificial intelligence, and particularly, the range and the characteristic definition of data privacy in the artificial intelligence era are unclear, namely, the problems and the disordering of data and privacy disclosure, improper promotion, algorithm black box, big data killing and the like in the application of the artificial intelligence technology exist. These problems and phenomena detract from personal interests and social public interests, and also create barriers to the safety innovation and health exposure of artificial intelligence technology.

Data involved in artificial intelligence applications can be divided into three categories: the first type is raw data, identity data, originating from the user; the second type is data appearance reflecting user behavior through data collected by user daily life, network record, APP record and the like; the third category is the feature indicators and data derived from the algorithm. The data required by these three types of artificial intelligence applications all contain varying degrees of user privacy. The protection of data privacy in artificial intelligence application presents the characteristics of expanded data privacy range, dispersed data structure, intelligence of data technology and the like, and the traditional data protection mode of 'informed-consenting' is not attentive.

Disclosure of Invention

In order to solve the above problems, the present disclosure provides a system and a method for sharing artificial intelligence big data based on a blockchain, which authorize a consumer to use a computation service of a publisher, so that the data does not leave the publisher when the consumer computes, and the security of the data itself is protected.

In order to achieve the purpose, the following technical scheme is adopted in the disclosure:

in a first aspect, a system for sharing artificial intelligence big data based on a block chain is provided, which includes an application layer and a protocol layer;

the application layer is used for a publisher to publish asset information and a consumer to use a data science tool;

and the protocol layer is used for sending the algorithm required by the data science tool to the publisher, accessing the asset information published by the publisher, calculating the accessed asset information according to the algorithm of the data science tool, and sending the calculation result to the consumer.

Furthermore, the application layer comprises a data science tool used by a front end and a consumer, and the protocol layer comprises a protocol encapsulation module, a metadata access control module, a publisher data management module, an event processing module and a contract management module;

the front end is used for issuing asset information by a publisher, and is respectively connected with the metadata access control module and the contract management module through the protocol encapsulation module, the data science tool is respectively connected with the metadata access control module and the contract management module, the publisher data management module is connected with the contract management module, the front end, the publisher data management module and the data science tool are jointly connected with the contract management module to sign a contract, and the event processing module responds to an event of the sharing system.

Furthermore, the front end is also used for the consumer to list the assets published by the publisher and to consume the assets published by the publisher, and is connected with the protocol encapsulation module.

Further, the data publisher manages the asset information through the publisher data management module.

Further, a metadata access control module is used for access to different asset data stores, thereby integrating different metadata repositories.

Further, the metadata repository supports multiple types of data processing storage.

Further, the publisher data management module is connected with a cloud storage provider.

Further, the publisher data management module also collects-enables the provider's service credentials.

Further, the event processing module is also used for releasing the reward after triggering the publisher data management module to access the publisher assets.

Further, the event processing module queries the metadata access control module to obtain the data asset document.

In a second aspect, a method for sharing artificial intelligence big data based on a block chain is provided, which includes,

the publisher publishes the asset information;

and sending the algorithm required by the consumer data science tool to the publisher, accessing the asset information published by the publisher, calculating the accessed asset information according to the algorithm of the data science tool, and sending the calculation result to the consumer.

Furthermore, the consumer sends a consumption request to acquire the access right and pay, the property information requested by the consumer is accessed through the contract management module, and the property information is sent to the consumer.

Compared with the prior art, the beneficial effect of this disclosure is:

1. the method authorizes the consumer to use the calculation service of the publisher, the consumer sends the algorithm of the data science tool to the publisher, the property of the publisher is called through the contract management module, calculation is carried out according to the algorithm, when the consumer calculates, the data does not leave the publisher, and the safety of the data is protected.

2. The present disclosure can authorize the consumer to directly access the publisher's data set in addition to authorizing the consumer to use the publisher's computing services.

Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.

FIG. 1 is a system architecture diagram of embodiment 1 of the present disclosure;

fig. 2 is a schematic communication diagram of a front end and a protocol layer according to embodiment 1 of the present disclosure;

fig. 3 is a schematic diagram of a publisher data management module according to embodiment 1 of the present disclosure.

The specific implementation mode is as follows:

the present disclosure is further described with reference to the following drawings and examples.

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.

In the present disclosure, terms such as "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "side", "bottom", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only relational terms determined for convenience in describing structural relationships of the parts or elements of the present disclosure, and do not refer to any parts or elements of the present disclosure, and are not to be construed as limiting the present disclosure.

In the present disclosure, terms such as "fixedly connected", "connected", and the like are to be understood in a broad sense, and mean either a fixed connection or an integrally connected or detachable connection; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present disclosure can be determined on a case-by-case basis by persons skilled in the relevant art or technicians, and are not to be construed as limitations of the present disclosure.

Example 1

In this embodiment, a block chain-based artificial intelligence big data sharing System (DaiMa File System, DMFS) is disclosed, which no longer depends solely on the supervision and regulation of government departments, but makes data collectors and users assume responsibility for sharing and protecting data accordingly. The present embodiment provides a tokenized service layer, disclosing data, storage, computation and usage algorithms, and providing a deterministic proof of availability and integrity, providing verifiable services. The artificial intelligence big data sharing system provided by the embodiment enables the data of the publisher to be shared and sold in a safe, reliable and transparent manner by means of the block chain technology. On one hand, the data publisher and the data consumer are linked through the blockchain technology and the token, so that the traceability, the transparency and the trust of all interested parties are ensured, and the safe sharing of the data is promoted; on the other hand, the direct connection of the data publisher and the consumer breaks the monopoly of single market data, and enhances the market activity of artificial intelligence.

The system architecture of the artificial intelligence big data sharing system based on the block chain disclosed by the embodiment is shown in fig. 1, wherein a front end and a data science tool are positioned on the third layer of the system and belong to an application layer of the system; the metadata access control module, the publisher data management module and the event processing module are positioned at a second layer of the system; the contract management module is arranged at the first layer of the system, and the second layer and the first layer form the protocol layer of the system.

and the protocol layer is used for sending the algorithm required by the data science tool to the publisher through the contract management module, accessing the asset information published by the publisher, calculating the accessed asset information according to the algorithm of the data science tool, and sending the calculation result to the consumer.

Front end

The front-end, marketplace, publisher application, which typically runs in a web browser, implements the following high-level functions:

(1) publish-allow publishers to publish new assets to the network;

(2) consumption-allowing the consumer to list and consume published assets;

(3) market-support complex interactions and advanced functions, such as: publishing existing assets in a cloud provider, searching and discovering assets, screening, KYC (Know buyer) user registration using a specific process.

The way the front-end communicates with the second layer and all components in the first layer is shown in fig. 2. The protocol encapsulation module library shown in fig. 2 encapsulates the logic that handles DMFS components (e.g., contract management module nodes and metadata access control module nodes). The library of protocol encapsulation modules is written in various programming languages and is part of the second layer.

(II) a data science tool:

data science tools are interfaces provided for data scientists. These tools and libraries are typically written in Python language, but are not limited to Python language, which discloses a high-level application programming interface that allows data scientists to integrate the functionality of artificial intelligence algorithms into various computing pipelines without touching metadata.

(III) protocol encapsulation module

The Protocol encapsulation module is a high-level specification API that abstracts the interaction with the most relevant DMFS Protocol components. It allows one to use the DMFS functionality without having to worry about details of the underlying contract management module or metadata storage system, the protocol encapsulation module sending consumer consumption requests, and sending algorithm requests for data science tools.

(IV) metadata access control module

The metadata access control module is a Python application running at the back end that enables metadata management. It abstracts access to different metadata stores, allowing the metadata access control module to integrate different metadata repositories. The DMFS database plug-in system can be integrated to realize different data storage, and the meta repository supports various types of data processing storage. Wherein metadata storage refers to storage of asset data published by a publisher.

(V) publisher data management Module

The publisher data management module is a component that provides functionality for the publisher, as shown in FIG. 3. It interacts with the publisher's cloud and/or local infrastructure. The publisher data management module's most basic scheme is to provide access to assets owned or managed by the publisher. In addition to this, other extended services may be provided, such as a storage service that performs computations on top of the data without moving new derivative assets of the data, collection of service credentials-enabling different kinds of service credentials from different providers. For example-allow for obtaining a receipt from a cloud provider to verify service delivery.

The publisher data management module is connected with the cloud storage provider and accesses asset data information stored by the cloud storage provider.

And the publisher data management module accesses the asset information of the publisher according to the consumption request of the consumer sent by the protocol encapsulation module.

The publisher data management module can also access the asset information of different publishers according to the algorithm of the data science tool used by the consumer and the required asset information request sent by the protocol encapsulation module, and calculate the accessed asset information through the algorithm of the data science tool.

The publisher data management module interacts with the cloud and/or local infrastructure of different publishers through the metadata access control module or searches published assets, so that access to asset information of different publishers is realized.

(VI) event processing module

The event handler module is a separate tool that needs to be run by its provider to perform the functions of providing program roles in response to new service agreements and granting access to consumers, as part of these service agreements, the event handler performs the following tasks:

(1) the monitoring protocol encapsulation module sends a new service protocol event (providing an EtherFang address) to a specific provider account, namely a consumption request or an algorithm request sent by the monitoring protocol encapsulation module;

(2) the on-chain access control-event handling program is responsible for on-chain access control;

(3) after providing access to the consumer, the payment/reward set in the service agreement is released.

The tool interacts directly with a DMFS Protocol keeper network by observing events, querying and submitting transactions related to a service execution Protocol, and it also has a metadata access control module to obtain data asset documents associated with the service Protocol.

The event processing module monitors the consumption request or algorithm request sent by the protocol encapsulation module, and after verifying that the right of a consumer passes, the event processing module triggers the publisher data management module to access the publisher asset information through the contract management module, and calculates the accessed asset information according to the algorithm sent by the protocol encapsulation module.

(VII) contract management Module

The contract management module contract is a block chain intelligent contract specific to DMFS-Protocol, and can be deployed to run on any dispersed Ethernet virtual machine, and the front end, the publisher data management module and the data science tool are connected with the contract management module together to sign a contract.

The system disclosed by the embodiment comprises a publisher, namely a data owner; consumers, i.e. data users, such as researchers, data scientists. There are two data sharing methods:

(1) the consumer selects the asset information to be consumed through the front end, and sends a consumption request through the protocol encapsulation module, the event processing module monitors the consumption request sent by the protocol encapsulation module, and after the consumer right verification passes, the publisher data management module is triggered to access the asset information of the publisher through the contract management module, namely, the consumer is authorized to directly access the data set of the publisher. Such as sharing data by publishers by providing URL download links to the data set.

(2) The consumer uses the data science tool, sends the algorithm request of the data science tool for consumption use through the protocol encapsulation module, the event processing module monitors the consumption request sent by the protocol encapsulation module, and after the consumer right verification passes, the consumer data management module is triggered to access the asset information of the publisher through the contract management module, and the accessed asset information is calculated according to the algorithm of the data science tool, namely, the consumer is authorized to use the calculation service of the publisher. After the publisher's audit session, the consumer can submit the computational code of the data set so that the data does not leave the publisher, thereby protecting the security of the data itself.

In the artificial intelligence big data sharing system based on the block chain, two modes of data publishing are realized by designing a bottom layer protocol of a system architecture, a protocol encapsulation module, a metadata access control module, a publisher data management module and the like, and the two modes are respectively used for authorizing a consumer to directly access a data set of a publisher and authorizing the consumer to use a computing service of the publisher.

According to the method, the consumer is authorized to use the calculation service of the publisher, the consumer sends the algorithm of the data science tool to the contract management module, the contract management module calls the assets of different publishers and calculates according to the algorithm, so that the data does not leave the publisher when the consumer calculates, and the safety of the data is protected.

The present disclosure can authorize the consumer to directly access the publisher's data set in addition to authorizing the consumer to use the publisher's computing services.

Example 2

In this embodiment, a method for sharing artificial intelligence big data based on a blockchain is disclosed, comprising,

the publisher publishes the asset information;

a consumer sends an algorithm request required by a data science tool;

after the contract management module provides the access right for the consumer, releasing the payment/reward set in the service agreement;

after the consumer pays, the contract management module accesses the asset information required by the algorithm, calculates the accessed asset information according to the algorithm, and sends the calculation result to the consumer.

Further, the consumer sends a consumption request to obtain the access right and pay, the property information requested by the consumer is accessed through the contract management module, and the property information is sent to the consumer

The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims

1. The system for sharing artificial intelligence big data based on the blockchain is characterized by comprising an application layer and a protocol layer;

2. The system for sharing artificial intelligence big data based on the blockchain as claimed in claim 1, wherein the application layer comprises data science tools used by a front end and a consumer, and the protocol layer comprises a protocol encapsulation module, a metadata access control module, a publisher data management module, an event processing module and a contract management module;

3. The system for sharing artificial intelligence big data based on the blockchain as claimed in claim 2, wherein the front end is further used for the consumer to list and consume the assets published by the publisher, and is connected with the protocol encapsulation module.

4. The system for sharing artificial intelligence big data based on the blockchain as claimed in claim 2, wherein the data publisher performs asset information management through the publisher data management module.

5. The system for sharing artificial intelligence big data based on the blockchain as claimed in claim 2, wherein the metadata access control module is used for accessing different asset data stores, thereby integrating different metadata repositories.

6. The system for sharing artificial intelligence big data based on the blockchain as claimed in claim 2, wherein the publisher data management module is interactively connected with a cloud or a local infrastructure of the publisher;

the publisher data management module also collects-enables the provider's service credentials.

7. The system for sharing artificial intelligence big data based on the blockchain as claimed in claim 2, wherein the event processing module is further configured to release the reward after triggering the publisher data management module to access the publisher asset.

8. The system for sharing artificial intelligence big data based on the blockchain as claimed in claim 4, wherein the event processing module further queries the metadata access control module to obtain the data asset document.

9. A method for sharing artificial intelligence big data based on a block chain is characterized by comprising the following steps,

the publisher publishes the asset information;

and sending the algorithm required by the consumer data science tool to the publisher through the kee contract, accessing the asset information published by the publisher, calculating the accessed asset information according to the algorithm of the data science tool, and sending the calculation result to the consumer.

10. The method for sharing artificial intelligence big data based on the blockchain as claimed in claim 9, wherein the consumer sends a consumption request, obtains an access right, pays, accesses the asset information requested by the consumer through the contract management module, and sends the asset information to the consumer.