CN112528308B - System and method for sharing artificial intelligence big data based on blockchain - Google Patents
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Abstract
The system and the method for sharing the artificial intelligent big data based on the blockchain disclosed by the disclosure comprise an application layer and a protocol layer; an application layer for publishers to publish asset information and consumer use data science tools; 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 computation service of the publisher, so that the data does not leave the publisher when the consumer computes, and the security of the data is protected.
Description
Technical Field
The invention relates to the technical field of blockchain application, in particular to a blockchain-based artificial intelligence big data sharing system and method.
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 this emerging technology is becoming increasingly prominent. The data, as the fuel for the algorithm, together with the computational effort, forms the basis for adherence of artificial intelligence. In the digital economic age, data acceleration generation and acceleration are identified to form large-scale, multi-kind and unstructured data, which is called big data for short. Big data is much different in structural features, carrier channels, operation logic, business modes, etc. compared with traditional data, and therefore, data management 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 of big data and artificial intelligence and protecting the existence of loopholes and blanks, and especially the range and characteristic definition of the data privacy in the artificial intelligence era are not clear, namely the problems and mess of data and privacy leakage, 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 the personal and social public benefits and also create a barrier to the safe innovation and healthy fall-off of artificial intelligence technology.
Data involved in artificial intelligence applications can be divided into three categories: the first category is raw data and identity data generated from the user; the second type is data appearance reflecting user behaviors through data collected by daily life, network records, APP records and the like of the user; the third class is the feature indicators and data derived from the algorithm. The data required for these three classes of artificial intelligence applications all contain varying degrees of user privacy. The protection of data privacy in artificial intelligence application shows the characteristics of expanded data privacy range, scattered data structure, intelligence of the data technology and the like, and the traditional data protection mode of 'knowledge-consent' is worry.
Disclosure of Invention
In order to solve the above problems, the disclosure provides a system and a method for sharing big artificial intelligence data based on 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 is protected.
In order to achieve the above purpose, the present disclosure adopts the following technical scheme:
in a first aspect, a blockchain-based artificial intelligence big data sharing system is provided, including an application layer and a protocol layer;
an application layer for publishers to publish asset information and consumer use data science tools;
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.
Further, the application layer comprises a front end and a data science tool used by 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 publishing asset information by a publisher, the front end is respectively connected with the metadata access control module and the contract management module through the protocol packaging 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 contracts, and the event processing module responds to events of the sharing system.
Furthermore, the front end is also used for the consumer to list and consume the published assets of the publisher and is connected with the protocol encapsulation module.
Further, the data publisher manages asset information through the publisher data management module.
Further, the metadata access control module is configured to access different asset data stores to integrate different metadata stores.
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 service attestations of the provider.
Further, the event processing module is further configured to release the reward after triggering the access of the publisher data management module to the publisher asset.
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 big data of artificial intelligence based on blockchain is provided, including,
the publisher publishes asset information;
and sending an algorithm required by the consumer data science tool to a 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 a calculation result to the consumer.
Further, the consumer sends a consumption request, obtains access rights, pays, accesses the asset information requested by the consumer via the contract management module, and sends the asset information to the consumer.
Compared with the prior art, the beneficial effects of the present disclosure are:
1. the method and the system authorize the consumer to use the computation service of the publisher, the consumer sends the algorithm of the data science tool to the publisher, and the contract management module invokes the publisher asset and computes according to the algorithm, so that when the consumer computes, the data does not leave the publisher, and the security of the data is protected.
2. The present disclosure enables a consumer to be authorized to directly access a publisher's data set in addition to authorizing the consumer to use the publisher's computing services.
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 included to provide a further understanding of the application and 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 do not constitute an undue limitation to 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 in embodiment 1 of the disclosure;
fig. 3 is a schematic diagram of a publisher data management module of embodiment 1 of the disclosure.
The specific embodiment is as follows:
the disclosure is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present application. 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 in accordance with the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
In the present disclosure, terms such as "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "side", "bottom", and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, are merely relational terms determined for convenience in describing structural relationships of the various components or elements of the present disclosure, and do not denote any one of the components 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 coupled," "connected," and the like are to be construed broadly and refer to either a fixed connection or an integral or removable connection; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the terms in the disclosure may be determined according to circumstances, and should not be interpreted as limiting the disclosure, for relevant scientific research or a person skilled in the art.
Example 1
In this embodiment, a blockchain-based artificial intelligence big data sharing system (DaiMa File System, DMFS) is disclosed that does not rely solely on government agency supervision and regulation, but rather allows data collectors, users to assume responsibility for corresponding data sharing and protection. The present embodiment provides a tokenized service layer, exposing algorithms for data, storage, computation, and use, and providing a confirmatory 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 mode by means of the block chain technology. On one hand, the data publisher and the consumer are linked through the blockchain technology and the token, so that traceability, transparency and trust of all stakeholders are ensured, and the safe sharing of data is promoted; on the other hand, the direct connection between the data publisher and the consumer breaks monopoly of single market data, and enhances market activity of artificial intelligence.
The embodiment discloses a block chain-based artificial intelligence big data sharing system, the system architecture is shown in fig. 1, wherein the front end, the data science tool is positioned at the third layer of the system, and belongs to the application layer of the system; the metadata access control module, the publisher data management module, the event processing module is in the second layer of the system; the contract management module is positioned at a first layer of the system, and the second layer and the first layer form a protocol layer of the system.
An application layer for publishers to publish asset information and consumer use data science tools;
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 (one)
The front end typically runs a marketplace, publisher application in a web browser that implements the following high-level functions:
(1) Publish-allow publishers to publish new assets to the network;
(2) Consumption-allowing a consumer to list and consume published assets;
(3) Market-support complex interactions and advanced functions, such as: publishing the existing assets in the cloud provider, searching and discovering the assets, screening, performing KYC (Know Your Customer, knowledge of the buyer) user registration using a specific procedure.
The manner in which the front end communicates with all components in the second layer and the first layer is shown in fig. 2. The protocol encapsulation module library shown in fig. 2 encapsulates 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) data science tools:
data science tools are interfaces provided for data scientists. These tools and libraries are typically written in the Python language, but are not limited to Python language, which discloses a high-level application program 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 interactions with the most relevant DMFS Protocol components. It allows one to use the DMFS functionality without having to worry about the details of the underlying contract management module or the metadata storage system, the protocol encapsulation module sends consumer consumption requests, and sends algorithm requests of the data science tools.
(IV) metadata Access control Module
The metadata access control module is a Python application running on the back end, and metadata management can be enabled. It abstracts access to different metadata stores, allowing the metadata access control module to integrate different metadata stores. The DMFS database plug-in system can integrate different data storage, and the meta-storage base supports multiple types of data processing storage. Where metadata storage refers to the storage of asset data published by publishers.
(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 most basic approach for a publisher data management module is to provide access to the assets that the publisher owns or manages. In addition to this, other extended services may be provided, such as a storage service that computes on top of the data without moving the data new derivative asset, collection of service attestation-enabling different kinds of service attestations from different providers. For example-allowing a receipt to be obtained 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 at the cloud storage provider.
The publisher data management module accesses the asset information of the publisher according to the consumer consumption request 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 base structures of different publishers or searches published assets through the metadata access control module, so that access to the asset information of different publishers is realized.
(six) event processing Module
The event handling module is a stand-alone tool that needs to be run by its provisioning program to fulfill the provisioning program role functions in response to new service agreements and granting consumer access rights as part of these service agreements, the event handling program performs the following tasks:
(1) The monitoring protocol packaging module sends a new service protocol event (providing an Ethernet address) to a specific provider account, namely a consumption request or an algorithm request sent by the monitoring protocol packaging module;
(2) On-chain access control-event handlers are responsible for on-chain access control;
(3) After providing the consumer with access rights, the payment/rewards set in the service agreement are released.
The tool interacts directly with the DMFS Protocol keeper network by observing events, querying and submitting transactions related to the service execution protocol, and metadata access control module to obtain data asset documents associated with the service protocol.
The event processing module monitors a consumption request or an algorithm request sent by the protocol encapsulation module, and after the consumer authority passes through verification, 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.
(seventh) contract management Module
The contract management module contract is a block chain intelligent contract specific to the DMFS-Protocol, can be deployed to run on any scattered Ethernet virtual machine, and is jointly connected with the contract management module by a front end, a publisher data management module and a data science tool to sign contracts.
The disclosed system includes a publisher, namely a data owner; consumers, i.e. data users, such as researchers, data scientists. There are two data sharing modes:
(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 authority verification is passed, 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 publishers sharing data by providing URL download links to data sets.
(2) The consumer uses the data science tool, the algorithm request of the data science tool used for consumption is sent through the protocol packaging module, the event processing module monitors the consumption request sent by the protocol packaging module, after the consumer authority verification is passed, the publisher 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 may submit the computing code of the data set so that the data does not leave the publisher, thereby protecting the security of the data itself.
According to the block chain-based artificial intelligence big data sharing system disclosed by the embodiment, two modes of data release 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 a data set of a publisher directly accessed by an authorized consumer and a computing service of the publisher used by the authorized consumer.
The method and the system authorize the consumer to use the computation service of the publisher, the consumer sends the algorithm of the data science tool to the contract management module, and different publisher assets are called by the contract management module and are computed according to the algorithm, so that when the consumer computes, the data does not leave the publisher, and the security of the data is protected.
The present disclosure enables a consumer to be authorized to directly access a publisher's data set in addition to authorizing the consumer to use the publisher's computing services.
Example 2
In this embodiment, a method of sharing blockchain-based artificial intelligence big data is disclosed, comprising,
the publisher publishes asset information;
the consumer sends an algorithm request required by the data science tool;
the contract management module releases payment/rewards set in the service agreement after providing access rights to the consumer;
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 request for consumption, obtains access rights, pays, accesses the asset information requested by the consumer via the contract management module, and sends the asset information to the consumer
The foregoing is merely a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and variations may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
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 aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. The block chain-based artificial intelligence big data sharing system is characterized by comprising an application layer and a protocol layer;
an application layer for publishers to publish asset information and consumer use data science tools;
the protocol layer is used for sending an algorithm required by the data science tool to a publisher through the contract management module, accessing asset information published by the publisher, calculating the accessed asset information according to the algorithm of the data science tool and sending a calculation result to a consumer;
the data science tool is an interface provided for a data scientist; high-level application program interfaces are disclosed that allow data scientists to integrate the functionality of artificial intelligence algorithms into various computing pipelines without touching metadata; the metadata refers to asset data published by a publisher;
the publisher data management module is a component for providing functions for publishers to interact with the publisher's cloud and/or local infrastructure; 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 consumer uses the data science tool, the algorithm request of the data science tool used for consumption is sent through the protocol packaging module, the event processing module monitors the consumption request sent by the protocol packaging module, after the consumer authority verification is passed, the publisher 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 reviews, the consumer can submit the computing code of the data set so that the data does not leave the publisher, thereby protecting the security of the data itself.
2. The blockchain-based artificial intelligence big data sharing system of claim 1, wherein the application layer includes a front end and a data science tool used by consumers, and the protocol layer includes 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 publishing asset information by a publisher, the front end is respectively connected with the metadata access control module and the contract management module through the protocol packaging 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 contracts, and the event processing module responds to events of the sharing system.
3. The blockchain-based artificial intelligence big data sharing system of claim 2, wherein the front end is further configured to list and consume the published assets of the publisher, and is coupled to the protocol encapsulation module.
4. The blockchain-based artificial intelligence big data sharing system of claim 2, wherein the data publisher performs asset information management through a publisher data management module.
5. The blockchain-based artificial intelligence big data sharing system of claim 2, wherein the metadata access control module is configured to access different asset data stores to integrate different metadata repositories.
6. The blockchain-based artificial intelligence big data sharing system of claim 2, wherein the publisher data management module is interactively connected with a publisher's cloud or local infrastructure;
the publisher data management module also collects-enables service attestations of the provider.
7. The blockchain-based artificial intelligence big data sharing system of claim 2, wherein the event handling module is further configured to release the reward upon triggering access to the publisher asset by the publisher data management module.
8. The blockchain-based artificial intelligence big data sharing system of claim 4, wherein the event processing module further queries the metadata access control module to obtain the data asset document.
9. A block chain based artificial intelligence big data sharing method is applied to the block chain based artificial intelligence big data sharing system as claimed in claim 1, and is characterized by comprising the following steps of,
the publisher publishes asset information;
and transmitting an algorithm required by the consumer data science tool to a publisher through a 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 transmitting a calculation result to the consumer.
10. The method for sharing big data of artificial intelligence based on blockchain as in claim 9, wherein the consumer sends the request for consumption, obtains access rights, pays, accesses the asset information requested by the consumer through the contract management module, and sends the asset information to the consumer.
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| CN108985089A (en) * | 2018-08-01 | 2018-12-11 | 清华大学 | Internet data shared system |
| CN109729168A (en) * | 2018-12-31 | 2019-05-07 | 浙江成功软件开发有限公司 | A kind of data share exchange system and method based on block chain |
| CN109639832A (en) * | 2019-01-21 | 2019-04-16 | 深圳市祥云万维科技有限公司 | It is a kind of to service shared network and method |
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