CN116226062A

CN116226062A - Data sharing method and device based on privacy protection

Info

Publication number: CN116226062A
Application number: CN202111467585.4A
Authority: CN
Inventors: 庞严
Original assignee: Chongqing New National University Research Institute; National University of Singapore
Current assignee: Chongqing New National University Research Institute; National University of Singapore
Priority date: 2021-12-03
Filing date: 2021-12-03
Publication date: 2023-06-06

Abstract

Embodiments of the present disclosure relate to methods, nodes, distributed systems, and storage media for data sharing with privacy preservation, and to the field of computers. According to the method, private data is obtained from local data comprising private data and first non-private data, the first non-private data being associated with the private data; generating a hash value of the private data; generating a first association between a local identifier for the local data and the hash value; generating a global identifier for indexing the hash value in a distributed system, the distributed system being associated with the current node; generating a second association between the global identifier and the hash value; and sending the second association to other nodes in the distributed system to store the second association in the distributed system for connection sharing of the first non-private data. Thus, sharing of non-private data can be performed securely while protecting local private data.

Description

Data sharing method and device based on privacy protection

Technical Field

Embodiments of the present disclosure relate generally to the field of computers, and more particularly, to a method for data sharing, a data providing node, a data using node, a distributed system, and a computer storage medium with privacy preservation.

Background

In the artificial intelligence era, data is an important strategic resource. The protection of data privacy is becoming more and more stringent by countries and businesses based on strategic and security requirements. On the other hand, data analysis and artificial intelligence model training often require a large amount of data. How to perform data analysis and artificial intelligence model training on the premise of protecting data privacy becomes a very important research direction in the current computer field.

Disclosure of Invention

Provided are a method for data sharing, a data providing node, a data using node, a distributed system, and a computer storage medium, capable of securely sharing non-private data while protecting local private data.

According to a first aspect of the present disclosure, a method for data sharing is provided. The method comprises the following steps: obtaining private data from local data comprising private data and first non-private data, the first non-private data being associated with the private data; generating a hash value of the private data; generating a first association between a local identifier for the local data and the hash value; generating a global identifier for indexing the hash value in a distributed system, the distributed system being associated with the current node; generating a second association between the global identifier and the hash value; and sending the second association to other nodes in the distributed system to store the second association in the distributed system for connection sharing of the first non-private data.

According to a second aspect of the present disclosure, a method for data sharing is provided. The method comprises the following steps: generating data condition information based on joint metadata stored in the distributed system, the joint metadata at least comprising an association between a global identifier and a plurality of pieces of attribute information of a plurality of pieces of non-private data, the global identifier being associated with a hash value of the private data, the private data being associated with the plurality of pieces of non-private data, the plurality of pieces of non-private data being located at a plurality of data providing nodes in the distributed system; transmitting a data request to a plurality of data providing nodes, the data request including data condition information; and receiving, from at least one of the plurality of data providing nodes, at least one item of non-private data that matches the data condition information.

According to a third aspect of the present disclosure, there is provided a data providing node. The data providing node includes: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to the first aspect.

According to a fourth aspect of the present disclosure, there is provided a data usage node. The data usage node includes: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to the second aspect.

According to a fifth aspect of the present disclosure, a distributed system is provided. The distributed system includes: a plurality of data providing nodes according to the third aspect of the present disclosure; and a data usage node according to a fourth aspect of the present disclosure.

In a sixth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method according to the first or second aspect of the present disclosure.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements.

Fig. 1 is a schematic diagram of a distributed system 100 according to an embodiment of the present disclosure.

Fig. 2 is a schematic diagram of a method 200 for data sharing according to an embodiment of the present disclosure.

Fig. 3 is a schematic diagram of a method 300 for data sharing according to an embodiment of the present disclosure.

Fig. 4 is a schematic diagram of a method 400 for data sharing according to an embodiment of the present disclosure.

Fig. 5 is a schematic diagram of a method 500 for data sharing according to an embodiment of the disclosure.

Fig. 6 is a schematic diagram of a method 600 for data sharing according to an embodiment of the disclosure.

Fig. 7 is a schematic diagram of a method 700 for data sharing according to an embodiment of the disclosure.

Fig. 8 is a block diagram of an electronic device for implementing a method for data sharing of an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The term "comprising" and variations thereof as used herein means open ended, i.e., "including but not limited to. The term "or" means "and/or" unless specifically stated otherwise. The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment. The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like, may refer to different or the same object. Other explicit and implicit definitions are also possible below.

As described above, in actual data analysis and artificial intelligence model training tasks, data to different nodes or regions (including individuals, companies, or countries) is often required. According to the data security method, private data related to the identity of an individual node or region cannot leave the node or region. In a typical information system design, local data in a node or region may be queried and extracted by a Local identification code (LID). Each LID is often used to uniquely identify a different individual in the data, including but not limited to a patient number, a customer number, and the like. Because the methods of LID generation in system designs of different nodes or regions tend to be inconsistent, LIDs for local data of the same individual at different nodes or regions tend to be different.

In addition, all source data cannot be directly shared between various nodes or areas due to the data privacy protection requirement. In data analysis and training of artificial intelligence models, it is often desirable to correlate data of different nodes or regions to improve data coverage and result accuracy of the data analysis and model training. How to correlate and share non-private data content of different areas to perform related data analysis and artificial intelligence model training is a very important problem on the premise of protecting private data.

To at least partially address one or more of the above problems, as well as other potential problems, example embodiments of the present disclosure propose a scheme for data sharing. In this scheme, a data providing node obtains private data from local data including private data and first non-private data, the first non-private data being associated with the private data. The data providing node generates a hash value of the private data and generates a first association between the local identifier for the local data and the hash value. The data providing node generates a global identifier for indexing the hash value in a distributed system, the distributed system being associated with the current node, and generates a second association between the global identifier and the hash value. The data providing node then sends the second association to other nodes in the distributed system to store the second association in the distributed system for connection sharing of the first non-private data. In this way, sharing of non-private data can be securely performed with the local private data protected by a first association between the local identifier of the local data and the hash value of the private data and a second association between the global identifier and the hash value of the private data stored in the distributed system. Furthermore, a second association between the global identifier stored in the distributed system and the hash value of the private data also facilitates associating different non-private data at different nodes in the distributed system for data analysis and model training.

Hereinafter, specific examples of the present scheme will be described in more detail with reference to the accompanying drawings.

Fig. 1 shows a schematic diagram of an example of a distributed system 100 according to an embodiment of the present disclosure. The distributed system 100 may include a data usage node 110 and a plurality of data providing nodes 120-1 through 120-n (hereinafter collectively referred to as 120), where n is greater than or equal to 2.

Data usage node 110 and data providing node 120 include, for example, but are not limited to, personal computers, desktop computers, laptop computers, tablet computers, wearable devices, personal digital assistants, smart phones, in-vehicle electronics, server computers, multiprocessor systems, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

Each data providing node 120 may store local data. The local data may have a local data identifier (LID) for identifying and indexing the local data at the data providing node 120. The local data may include private data and non-private data, wherein the non-private data is associated with the private data. The privacy data is related to the identity of the individual, which may include, but is not limited to, a certificate number, a cell phone number, a name, an address zip code, and the like. The non-private data is independent of the identity of the individual and may include, but is not limited to, age, gender, city, disease history, merchandise purchase history, and the like. The non-private data may include one or more items of attribute information, such as cancer drugs, hypertensive drugs, and the like. Different data providing nodes 120 may have different non-private data associated with the same private data. For example, data providing node 120-1 may have cancer drug information for user A, while data providing node 120-2 may have hypertension drug information for user A.

Different data providing nodes 120 may be located in different areas, e.g. belonging to different individuals, companies or countries etc.

In some embodiments, distributed system 100 may be a blockchain system, and data use node 110 and data providing node 120 may be blockchain nodes.

The data providing node 120 may be configured to obtain private data from local data comprising private data and first non-private data, the first non-private data being associated with the private data; generating a hash value (PHash) of the private data; generating a first association (LID-PHash) between a Local Identifier (LID) and a hash value for the local data; generating a Global Identifier (GID) for indexing the hash value in the distributed system 100, the distributed system 100 being associated with the current node; generating a second association (GID-PHash) between the global identifier and the hash value; and sending the second association to other nodes in the distributed system 100 (e.g., the data providing nodes 120-2 through 120-n and the data using node 110) to store the second association in the distributed system 100 for connection sharing of the first non-private data.

Furthermore, the data providing node 120-1 may also be configured to obtain first attribute information of the first non-private data from the current node; obtaining one or more pieces of second attribute information of one or more pieces of second non-private data associated with private data from one or more additional data providing nodes (e.g., data providing nodes 120-2 to 120-n) in the distributed system 100; generating, based on the second association, federated metadata (Federated Metadata) comprising at least an association between the global identifier, the first attribute information, and one or more items of second attribute information; and sending the federated metadata to other nodes in the distributed system 100 (e.g., the data providing nodes 120-2 through 120-n and the data consuming node 110) to store the federated metadata in the distributed system 100 for connection sharing of the first non-private data and the one or more second non-private data.

The data usage node 110 may be configured to generate data condition information based on joint metadata stored in the distributed system 100, the joint metadata including at least an association between a global identifier associated with a hash value of private data and a plurality of pieces of attribute information of non-private data associated with the plurality of pieces of non-private data located at the plurality of data providing nodes 120 in the distributed system 100; transmitting a data request to the plurality of data providing nodes 120, the data request including data condition information; and receiving at least one item of non-private data matching the data condition information from at least one data providing node of the plurality of data providing nodes 120.

Thus, sharing of non-private data can be securely performed while protecting local private data by a first association between a local identifier of the local data and a hash value of the private data and a second association between a global identifier and a hash value of the private data stored in the distributed system. Furthermore, a second association between the global identifier stored in the distributed system and the hash value of the private data also facilitates associating different non-private data at different nodes in the distributed system for data analysis and model training. In addition, by storing in the distributed system joint metadata that associates different attribute information of different non-private data at different data providing nodes, the data usage node can conveniently learn which attribute information exists for the entire system without knowing any private data.

Fig. 2 illustrates a flow chart of a method 200 for data sharing according to an embodiment of the present disclosure. For example, the method 200 may be performed by the data providing node 120-1 as shown in FIG. 1. It should be understood that method 200 may also include additional blocks not shown and/or that the blocks shown may be omitted, the scope of the disclosure being not limited in this respect. It should also be appreciated that while illustrated with data providing node 120-1, it may be implemented by other data providing nodes 120-2 through 120-n, the scope of the disclosure being not limited in this respect.

At block 202, the data providing node 120-1 obtains private data from local data comprising private data and first non-private data, the first non-private data being associated with the private data.

The privacy data may include, for example, but is not limited to, an identification number, a name + address zip code, or a name + cell phone number, etc.

At block 204, the data providing node 120-1 generates a hash value (PHash) of the private data.

For example, a Hash Function (Hash Function) may be performed on private data such as an identification number, a name+address zip code, or a name+mobile phone number to generate a Hash value. The hash function includes, for example, but is not limited to, SHA-256, SHA-512, and the like.

In some embodiments, the data providing node 120-1 may process the private data based on a predetermined format to generate format-processed private data, and perform a hash function calculation on the format-processed private data to generate a hash value. The predetermined format is the same for different data providing nodes, thereby ensuring consistency of hash calculation results.

At block 206, the data providing node 120 generates a first association (LID-PHash) between a Local Identifier (LID) and a hash value (PHash) for the local data.

At the current or same node, the hash value (PHash) of the private data and the Local Identifier (LID) may correspond one-to-one. In different nodes, the Local Identifier (LID) is often different for the same individual, such as the same patient, but the hash value (PHash) of the private data is the same. Non-private data of the same individual of different nodes may be associated by a hash value (PHash) of the private data. The hash value (PHash) of the private data may be shared into the off-node distributed system for data connection. Due to the unidirectional Hiding characteristic (sharing Property) of the hash function, the data using node has no way to reversely deduce private data information of any relevant personal identity according to the hash value, so that local private data is effectively protected.

At block 208, the data providing node 120-1 generates a Global Identifier (GID) for indexing a hash value (PHAsh) in the distributed system 100, the distributed system 100 being associated with the current node.

The Global Identifier (GID) may be generated according to any suitable identifier index requirement.

In some embodiments, data providing node 120-1 may first determine whether a global identifier associated with the hash value exists in distributed system 100. If it is determined that there is no global identifier associated with the hash value, a global identifier is generated for indexing the hash value (PHash) in the distributed system 100. If it is determined that there is a global identifier associated with the hash value, then the global identifier for indexing the hash value (PHash) in the distributed system 100 is not generated.

At block 210, the data providing node 120-1 generates a second association (GID-PHAsh) between the Global Identifier (GID) and the hash value (PHAsh).

At block 212, the data providing node 120-1 sends a second association to other nodes in the distributed system 110 to store the second association in the distributed system 110 for connection sharing of the first non-private data. It should be understood that connection sharing herein refers to that a first non-private data may be connected or otherwise associated with other non-private data associated with the same private data at other data providing nodes to a data usage node.

For example, data providing node 120-1 may send the second association to data providing nodes 120-2 through 120-n and data using node 110 in distributed system 100 such that the nodes store the second association, thereby enabling storage of the second association in distributed system 110.

The Global Identifiers (GIDs) may be in one-to-one correspondence with hash values (PHash). Since the hash values at different nodes are the same for the same user or patient, their corresponding Global Identifiers (GIDs) are also consistent. The global identifier is visible between different nodes, which the data user can use to generate data requests.

In some embodiments, the distributed system 100 may be a blockchain system, and the second association (GID-PHAsh) between the global identifier and the hash value may be stored in the blockchain system, i.e., appended to the blockchain. The blockchain system may be a public chain (permissionless chain) or a license chain (permissioned chain).

Thus, sharing of non-private data can be securely performed while protecting local private data by a first association between a local identifier of the local data and a hash value of the private data and a second association between a global identifier and a hash value of the private data stored in the distributed system. Furthermore, a second association between the global identifier stored in the distributed system and the hash value of the private data also facilitates associating different non-private data at different nodes in the distributed system for data analysis and model training.

In addition, the first association between the private data, the non-private data and the Local Identifier (LID) and the hash value (PHash) for the local data is stored locally, and the second association between the global identifier and the hash value is stored to the blockchain, so that the hybrid storage architecture is realized, the non-falsification of the second association is ensured, and the problem of low storage efficiency of the blockchain system is avoided.

After the second association between the global identifier and the hash value is generated and stored in the distributed system, different non-private data at different nodes may also be associated with the second association. As will be described below with reference to fig. 3.

Fig. 3 illustrates a flow chart of a method 300 for data sharing according to an embodiment of the present disclosure. For example, the method 300 may be performed by the data providing node 120-1 as shown in FIG. 1. It should be understood that method 300 may also include additional blocks not shown and/or that the blocks shown may be omitted, the scope of the disclosure being not limited in this respect. It should also be appreciated that while illustrated with data providing node 120-1, it may be implemented by other data providing nodes 120-2 through 120-n, the scope of the disclosure being not limited in this respect.

At block 302, the data providing node 120-1 obtains first attribute information for first non-private data from a current node.

The first non-private data may include first attribute information and first attribute value information. Examples of the first attribute information include the entry "cancer drugs". Examples of the first attribute value information include cancer drug information such as the name, number, etc. of the cancer drug.

At block 304, the data providing node 120-1 obtains one or more items of second attribute information for one or more items of second non-private data associated with the private data from one or more additional data providing nodes (e.g., data providing nodes 120-2 through 120-n) in the distributed system 100.

The second non-private data may include second attribute information and second attribute value information. Examples of the second attribute information include the item "hypertension medicine", the item "rheumatism medicine", and the like. Examples of the second attribute value information include hypertension medicine information, rheumatism medicine information, such as a hypertension medicine name, a rheumatism medicine name, and the like.

At block 306, the data providing node 120-1 generates and stores, in the distributed system 100, federated metadata including at least an association between a Global Identifier (GID), first attribute information, and one or more items of second attribute information, based on the second association.

For example, the first attribute information includes an item "cancer medicine", the plurality of pieces of second attribute information includes an item "hypertension medicine", an item "rheumatism medicine", and one example of the joint metadata includes an association between a Global Identifier (GID), an item "cancer medicine", an item "hypertension medicine", and an item "rheumatism medicine". In addition, the joint metadata may further include description information for a Global Identifier (GID), an item "cancer medicine", an item "hypertension medicine", and an item "rheumatism medicine".

At block 308, the data providing node 120-1 sends the federated metadata to other nodes in the distributed system 100 to store the federated metadata in the distributed system 100 for connection sharing of the first non-private data and the one or more second non-private data.

For example, the data providing node 120-1 may send the federated metadata to the data providing nodes 120-2 through 120-n and the data consuming node 110 in the distributed system 100 so that the nodes store the federated metadata, thereby enabling the federated metadata to be stored in the distributed system 100. After the joint metadata is stored in the distributed system 100, the data usage node 110 can know which GIDs and which attribute information are in the distributed system 100 and what the association relationship between them is based on the joint metadata, so as to generate data condition information according to the requirement for data request.

In some embodiments, distributed system 100 may be a blockchain system, and the federated metadata may be stored in the blockchain system, e.g., appended to the blockchain.

By storing the joint metadata of different attribute information of different non-private data at different data providing nodes in the distributed system, the data using node can conveniently know which attribute information exists in the whole system without knowing any private data, and can conveniently associate and share the different non-private data at the different data providing nodes for data analysis, artificial intelligent model training and the like. Complex data analysis or artificial intelligent model training tasks which cannot be performed or have low efficiency in the traditional privacy protection calculation mode can be supported, and the obtained data analysis and model results are often more accurate.

In addition, storing the first association between the private data, the non-private data, and the Local Identifier (LID) and hash value (PHash) for the local data locally, and storing the second association between the global identifier and hash value, and the associated metadata to the blockchain, a hybrid storage architecture is implemented that both ensures non-tampering of the second association and avoids the problem of low storage efficiency of the blockchain system.

After the federated metadata is generated and stored in the distributed system, the data usage node 110 may utilize the federated metadata for data requests, and the data providing node 120 may obtain corresponding non-private data for sharing for the data requests. As will be described below with reference to fig. 4.

Fig. 4 illustrates a flow chart of a method 400 for data sharing according to an embodiment of the present disclosure. For example, the method 400 may be performed by the data providing node 120-1 as shown in FIG. 1. It should be understood that method 400 may also include additional blocks not shown and/or that the blocks shown may be omitted, the scope of the disclosure being not limited in this respect. It should also be appreciated that while illustrated with data providing node 120-1, it may be implemented by other data providing nodes 120-2 through 120-n, the scope of the disclosure being not limited in this respect.

At block 402, the data providing node 120-1 receives a data request from a data usage node 110 in the distributed system 100, the data request including data condition information, the data condition information being generated based on the federated metadata.

For example, the data usage node 110 may generate data condition information based on attribute information in the federated metadata. One example of the data condition information may be "SELECT GID, cancer media, hypertension medicine, age FROM Metadata WHERE age > = 20AND age < = 40", where "GID", "cancer media", "hypertension medicine", AND "age" may be global identifier, attribute information included in the joint metadata. The data condition information means a global identifier, cancer drug information, hypertension information, and age information of between 20and 40 years of age is required.

Also for example, the data usage node 110 may generate data condition information based on a global identifier in the federated metadata. For example, the data condition information includes one or more global identifiers.

In some embodiments, distributed system 100 includes a blockchain system, with data providing node 120 and data using node 110 being blockchain nodes. Receiving a data request from the data usage node 110 includes receiving a first smart contract from the data usage node 110, the first smart contract including data condition information. Because of the non-tamperable nature of blockchain data, once a smart contract is deployed, it will not be able to be directly modified on the original contract. Thus, the footprint of the data use can be conveniently searched later, and the shared non-private data is ensured not to be abused.

At block 404, the data providing node 120-1 obtains first non-private data that matches the data condition information.

Taking the above data condition information "SELECT GID, cancer media, hypertension medicine, age FROM Metadata WHERE age > = 20AND age < = 40" as an example, if the first non-private data includes the first attribute information "cancer media", the corresponding first attribute value information, AND the age attribute value is 30, the first non-private data is matched with the data condition information.

In the case where the data condition information includes a global identifier, the data providing node 120-1 may determine a hash value of the private data associated with the global identifier based on a second association between the global identifier and the hash value of the private data stored in the distributed system 100. Subsequently, the data providing node 120-1 may determine a local identifier associated with the hash value of the private data based on the locally stored first association between the local identifier and the hash value of the private data, and obtain first non-private data in the local data corresponding to the local identifier as first non-private data that matches the data condition information.

In some embodiments, the data request further includes a node address of the data usage node 110. The data providing node 120-1 may also determine whether the node address of the data using node 110 matches a preset node address. For example, the data use node 110 and the data providing node 120-1 may negotiate in advance to adopt the address of the data use node 110 as a preset node address.

The data providing node 120-1 acquires first non-private data matched with the data condition information if it is determined that the node address of the data using node 110 matches the preset node address. Thus, only data requests from preset node addresses will be processed by the data providing nodes, so that data is directionally shared.

At block 406, the data providing node 120-1 may also obtain at least one item of second attribute information for at least one item of second non-private data of the one or more items of second non-private data from at least one of the one or more additional data providing nodes (e.g., the data providing nodes 120-2 through 120-n), the at least one item of second attribute information matching the data condition information.

The at least one item of second attribute information of the acquired at least one item of second non-private data may be acquired and transmitted by the at least one further data providing node from local data of the at least one further data providing node based on the data condition information in response to receiving a data request from the data using node 110.

At block 408, the data providing node 120-1 generates a third association between the global identifier, the first attribute information, and the at least one item of second attribute information based on the second association (GID-PHAsh).

At block 410, the data providing node 120-1 sends a third association to other nodes in the distributed system 100 to store the third association in the distributed system 100 such that the data usage node 110 associates the first non-private data obtained from the current node with the at least one second non-private data obtained from the at least one additional data providing node based on the third association.

At block 412, the data providing node 120-1 transmits the acquired first non-private data to the data using node 110.

For example, the first non-private data may be sent to the data using node by way of an open API or Web service or direct file download, etc. In some embodiments, the data providing node 120-1 may send the acquired first non-private data to the data using node 110 after receiving the data request from the data using node 110. In other embodiments, the data providing node 120-1 may send the acquired first non-private data directly to the data using node 110 without a request from the data using node 110.

Therefore, the data using node can generate data condition information based on the joint metadata stored in the distributed system to perform data request, and the data providing node can acquire matched non-private data and send the matched non-private data to the data using node, so that the data using node can realize safe sharing of the non-private data on the premise that the data using node does not know any private data. In addition, attribute information of a plurality of non-private data matched with the data condition information at a plurality of data providing nodes can be associated, so that the data using nodes can acquire the plurality of non-private data at the plurality of data providing nodes to conduct data analysis and model training.

Fig. 5 illustrates a flow chart of a method 500 for data sharing according to an embodiment of the present disclosure. For example, the method 500 may be performed by the data providing node 120-1 as shown in FIG. 1. It should be understood that method 500 may also include additional blocks not shown and/or that the blocks shown may be omitted, the scope of the disclosure being not limited in this respect. It should also be appreciated that while illustrated with data providing node 120-1, it may be implemented by other data providing nodes 120-2 through 120-n, the scope of the disclosure being not limited in this respect. It should be appreciated that in the embodiment with respect to fig. 5, distributed system 110 is a blockchain system.

At block 502, the data providing node 120-1 receives a first smart contract from the data using node 110, the first smart contract including data condition information and incentive mechanism information for data sharing, the data condition information being generated based on federated metadata.

At block 504, the data providing node 120-1 determines whether there is first non-private data in the local data that matches the data condition information.

If the data providing node 120-1 determines at block 504 that there is first non-private data in the local data that matches the data condition information, then a determination is made at block 506 as to whether the incentive mechanism information passes the evaluation.

In some embodiments, determining whether the incentive mechanism information passes the evaluation may include prompting the incentive mechanism information, and determining whether a positive acknowledgement for the prompt is received. If a positive acknowledgement is received, the incentive scheme information is determined to pass the evaluation, otherwise, the incentive scheme information is determined not to pass the evaluation.

In other embodiments, determining whether the incentive scheme information passes the assessment may include determining whether the incentive scheme information meets a preset condition, and if the incentive scheme information is determined to meet the preset condition, determining that the incentive scheme information passes the assessment, otherwise, determining that the incentive scheme information does not pass the assessment.

If the data providing node 120-1 determines that the incentive mechanism information passes the assessment at block 506, a second intelligent contract is generated and deployed in the blockchain system at block 508, the second intelligent contract including data condition information, incentive mechanism information, a node address and a digital wallet address of the data using node, and a node address and a digital wallet address of the current node.

At block 510, the data providing node 120-1 determines whether a second smart contract is deployed in the blockchain system.

If the data providing node 120-1 determines that the second smart contract is deployed in the blockchain system at block 510, then the first non-private data that matches the data condition information is obtained at block 512.

At block 514, the data providing node 120-1 may also obtain at least one item of second attribute information for at least one item of second non-private data of the one or more items of second non-private data from at least one of the one or more additional data providing nodes (e.g., the data providing nodes 120-2 through 120-n), the at least one item of second attribute information matching the data condition information.

At block 516, the data providing node 120-1 may generate a third association between the global identifier, the first attribute information, and the at least one item of second attribute information based on the second association (GID-PHash).

At block 518, the data providing node 120-1 sends a third association to other nodes in the blockchain system to store the third association in the blockchain system such that the data using node 110 associates the first non-private data obtained from the current node with the at least one second non-private data obtained from the at least one additional data providing node based on the third association.

At block 520, the data providing node 120-1 transmits the acquired first non-private data to the data using node 110. The first non-private data is transmitted in a similar manner to the above, and will not be described in detail here.

Therefore, the data using node can generate data condition information based on the joint metadata stored in the blockchain system and perform data request through the intelligent contract, and the data providing node can acquire matched non-private data and send the matched non-private data to the data using node, so that safe sharing of the non-private data is realized on the premise that the data using node does not know any private data. Once the data request is linked up through the intelligent contract, the record cannot be changed, so that the data use footprint can be conveniently searched later, and the shared non-private data is ensured not to be abused. In addition, attribute information of a plurality of non-private data matched with data condition information at a plurality of data providing nodes can be associated, so that data analysis and model training can be conveniently carried out after the data using nodes associate the plurality of non-private data at the plurality of data providing nodes. In addition, a data sharing incentive mechanism is established by utilizing the intelligent contract, and a more effective data sharing platform can be established through a digital rights sharing design. Meanwhile, the data providing node can obtain fair benefit return after contributing non-private data to a data analysis or artificial intelligent model training task.

In some embodiments, the data providing node 120-1 may also determine whether the transmission of the first non-private data is complete.

If the data providing node 120-1 determines that the transmission of the first non-private data is completed, transferring digital rights corresponding to the first non-private data from the digital wallet address of the data using node to the digital wallet address of the current node is performed based on incentive mechanism information through the second smart contract.

For example, transferring digital rights corresponding to the first non-private data from the digital wallet address of the data usage node to the digital wallet address of the current node may be performed by sending a message to the address of the second smart contract to invoke the associated code in the second smart contract based on incentive mechanism information.

Therefore, after the data providing node finishes data sharing, the corresponding transfer of the data rights and interests can be realized, and the incentive for data sharing is realized.

Fig. 6 illustrates a flow chart of a method 600 for data sharing according to an embodiment of the present disclosure. For example, method 600 may be performed by data usage node 110 as shown in FIG. 1. It should be understood that method 600 may also include additional blocks not shown and/or that the blocks shown may be omitted, the scope of the disclosure being not limited in this respect.

At block 602, the data usage node 110 generates data condition information based on joint metadata stored in the distributed system 100, the joint metadata including at least an association between a global identifier associated with a hash value of private data and a plurality of pieces of attribute information of non-private data associated with a plurality of pieces of non-private data located at a plurality of data providing nodes in the distributed system. The federated metadata is stored into the distributed system by a plurality of data providing nodes 120 in the distributed system 100 by following the method 300 described above.

At block 604, the data usage node 110 sends a data request to the plurality of data providing nodes 120, the data request including data condition information.

In some embodiments, the data request may also include the node address of the data usage node 110.

In some embodiments, distributed system 100 is a blockchain system. Further, the data request may be sent in the form of a smart contract.

At block 606, the data usage node 110 receives at least one item of non-private data that matches the data condition information from at least one of the plurality of data providing nodes 120.

Therefore, the data using node can generate data condition information based on the joint metadata stored in the distributed system to perform data request, and the data providing node can acquire matched non-private data and send the matched non-private data to the data using node, so that the data using node can realize safe sharing of the non-private data on the premise that the data using node does not know any private data.

Fig. 7 illustrates a flow chart of a method 700 for data sharing according to an embodiment of the present disclosure. For example, method 700 may be performed by data usage node 110 as shown in fig. 1. It should be understood that method 700 may also include additional blocks not shown and/or that the blocks shown may be omitted, the scope of the disclosure being not limited in this respect.

At block 702, the data usage node 110 generates data condition information based on joint metadata stored in the blockchain system, the joint metadata including at least an association between a global identifier associated with a hash value of the private data and a plurality of pieces of attribute information of non-private data associated with the plurality of pieces of non-private data located at a plurality of data providing nodes in the distributed system. The federated metadata is stored into the blockchain system by one or more data providing nodes 120 in the blockchain system by following the method 300 described above.

At block 704, the data usage node 110 sends a first smart contract to a plurality of data providing nodes 120 in the blockchain system, the first smart contract including data condition information and incentive mechanism information for data sharing.

The data using node can call the system intelligent contract template to modify or directly write and generate a first intelligent contract according to own requirements, and the first intelligent contract is deployed on the blockchain, so that a data request is issued to each node in the blockchain system. Because of the non-tamperable nature of blockchain data, once a smart contract is deployed, it will not be able to be directly modified on the original contract. If the intelligent contract clauses are required to be modified, the system calls a contract destroying submodule to destroy the original intelligent contract on the premise of agreeing with the link points of the participating blocks, and generates a new intelligent contract for deployment according to the new requirements. All contract generation and destruction records are recorded on the non-tamperable blockchain block data, so that the sharing condition of the data request can be tracked conveniently, and the data security can be protected better.

At block 706, the data usage node 110 obtains at least one node address of the at least one individual data providing node 120 based on at least one second smart contract stored in the blockchain system, the at least one second smart contract stored by the at least one data providing node in the blockchain system in response to the first smart contract, each of the at least one second smart contract including data condition information, incentive mechanism information, a node address of the data usage node and a digital wallet address, and a node address and a digital wallet address of the corresponding data providing node. At least one second smart contract is stored in the blockchain system by at least one data providing node 120 through the method 500 described above.

At block 708, the data usage node 110 obtains at least one item of non-private data matching the data condition information from the at least one data providing node 120 based on the at least one node address.

Therefore, the data using node can generate data condition information based on the joint metadata stored in the blockchain system and perform data request through the intelligent contract, and the data providing node can acquire matched non-private data and send the matched non-private data to the data using node, so that safe sharing of the non-private data is realized on the premise that the data using node does not know any private data. Once the data request is linked up through the intelligent contract, the record cannot be changed, so that the data use footprint can be conveniently searched later, and the shared non-private data is ensured not to be abused. In addition, a data sharing incentive mechanism is established by utilizing the intelligent contract, and a more effective data sharing platform can be established through a digital rights sharing design. Meanwhile, the data providing node can obtain fair benefit return after contributing non-private data to a data analysis or artificial intelligent model training task.

In some embodiments, obtaining at least one item of non-private data matching the data condition information from at least one data providing node 120 comprises obtaining at least two items of non-private data matching the data condition information from at least two data providing nodes 120. The data usage node 110 may also associate at least two items of non-private data based on an association between a global identifier and at least two items of attribute information stored in the distributed system. The association between the global identifier and the at least two items of attribute information is stored by the at least two data providing nodes in the blockchain system in response to the first smart contract.

Therefore, the method and the device can be used for data analysis and model training after the association of the at least two pieces of non-private data at the at least two data providing nodes based on the association between the attribute information of the at least two pieces of non-private data matched with the data condition information at the plurality of data providing nodes, and are beneficial to improving the accuracy of the data analysis and the model training.

Fig. 8 shows a schematic block diagram of an example device 800 that may be used to implement embodiments of the present disclosure. For example, the data usage node 110 and the data providing node 120 as shown in fig. 1 may be implemented by the device 800. As shown, the device 800 includes a Central Processing Unit (CPU) 801 that can perform various suitable actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM) 802 or loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the random access memory 803, various programs and data required for the operation of the device 800 may also be stored. The central processing unit 801, the read only memory 802, and the random access memory 803 are connected to each other through a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.

Various components in device 800 are connected to input/output interface 805, including: an input unit 806, such as a keyboard, mouse, microphone, etc.; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, or the like. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The various processes and treatments described above, such as methods 200-700, may be performed by the central processing unit 801. For example, in some embodiments, the methods 200-700 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 800 via read only memory 802 and/or communication unit 809. One or more of the acts of the methods 200-700 described above may be performed when a computer program is loaded into the random access memory 803 and executed by the central processing unit 801.

The present disclosure relates to methods, apparatus, systems, electronic devices, computer readable storage media, and/or computer program products. The computer program product may include computer readable program instructions for performing various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.

The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.

Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvement of the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for data sharing, comprising:

obtaining private data from local data comprising the private data and first non-private data, the first non-private data being associated with the private data;

generating a hash value of the private data;

generating a first association between a local identifier for the local data and the hash value;

generating a global identifier for indexing the hash value in a distributed system, the distributed system being associated with a current node;

generating a second association between the global identifier and the hash value; and

The second association is sent to other nodes in the distributed system to store the second association in the distributed system for connection sharing of the first non-private data.

2. The method of claim 1, further comprising:

acquiring first attribute information of the first non-private data from a current node;

obtaining one or more items of second attribute information of one or more items of second non-private data from one or more further data providing nodes in the distributed system, the one or more items of second non-private data being associated with the private data;

generating, based on the second association, federated metadata including at least an association between the global identifier, the first attribute information, and the one or more items of second attribute information; and

the federated metadata is sent to the other nodes in the distributed system to store the federated metadata in the distributed system for connection sharing of the first non-private data and the one or more second non-private data.

3. The method of claim 2, further comprising:

receiving a data request from a data usage node in the distributed system, the data request including data condition information, the data condition information being generated based on the federated metadata;

Acquiring first non-private data matched with the data condition information; and

and sending the acquired first non-private data to the data using node.

4. A method according to claim 3, further comprising:

obtaining, from at least one of the one or more further data providing nodes, at least one item of second attribute information of at least one item of second non-private data of the one or more items of second non-private data, the at least one item of second non-private data matching the data condition information;

generating a third association between the global identifier, the first attribute information, and the at least one item of second attribute information based on the second association; and

the third association is sent to other nodes in the distributed system to store the third association in the distributed system, so that the data usage node associates the first non-private data acquired from the current node with the at least one second non-private data acquired from the at least one further data providing node based on the third association.

5. The method of claim 3 or 4, wherein the data request further includes a node address of the data usage node, and acquiring first non-private data that matches the data condition information includes:

Determining whether the node address is matched with a preset node address; and

and if the node address is determined to be matched with the preset node address, acquiring first non-private data matched with the data condition information.

6. The method of claim 3 or 4, wherein the distributed system comprises a blockchain system, and receiving a data request from a data-using node comprises receiving a first smart contract from the data-using node, the first smart contract comprising the data condition information.

7. The method of claim 6, wherein the first smart contract further includes incentive mechanism information for data sharing, and acquiring first non-private data that matches the data condition information comprises:

determining whether first non-private data matched with the data condition information exists in the local data;

if it is determined that first non-private data matched with the data condition information exists in the local data, determining whether the incentive mechanism information passes evaluation;

generating and deploying a second smart contract in the blockchain system if it is determined that the incentive mechanism information passes the evaluation, the second smart contract including the data condition information, the incentive mechanism information, a node address and a digital wallet address of the data usage node, and a node address and a digital wallet address of a current node; and

If it is determined that the second smart contract is deployed in the blockchain system, first non-private data that matches the data condition information is obtained.

8. The method of claim 7, further comprising:

if it is determined that the transmission of the first non-private data is completed, performing, by the second smart contract, transfer of digital rights corresponding to the first non-private data from the digital wallet address of the data usage node to the digital wallet address of the current node based on the incentive mechanism information.

9. A method for data sharing, comprising:

generating data condition information based on joint metadata stored in a distributed system, the joint metadata comprising at least an association between a global identifier and a plurality of items of attribute information of a plurality of items of non-private data, the global identifier being associated with a hash value of private data, the private data being associated with the plurality of items of non-private data, the plurality of items of non-private data being located at a plurality of data providing nodes in the distributed system;

transmitting a data request to the plurality of data providing nodes, the data request including the data condition information; and

At least one item of non-private data matching the data condition information is received from at least one data providing node of the plurality of data providing nodes.

10. The method of claim 9, wherein the distributed system comprises a blockchain system.

11. The method of claim 9, wherein sending the data request to the plurality of data providing nodes comprises sending a first smart contract to the plurality of data providing nodes, the first smart contract comprising the data condition information.

12. The method of claim 11, wherein the smart contract further includes incentive mechanism information for data sharing, and the obtaining at least one item of non-private data matching the data condition information from at least one of the plurality of data providing nodes comprises:

obtaining at least one node address of the at least one data providing node based on at least one second smart contract stored in the blockchain system, the at least one second smart contract stored by the at least one data providing node in response to the first smart contract, each of the at least one second smart contract including the data condition information, the incentive mechanism information, a node address and a digital wallet address of the data using node, and a node address and a digital wallet address of a corresponding data providing node; and

At least one item of non-private data matching the data condition information is acquired from the at least one data providing node based on the at least one node address.

13. The method of any of claims 9-12, wherein receiving at least one item of non-private data from at least one of the plurality of data providing nodes that matches the data condition information comprises receiving at least two items of non-private data from at least two of the plurality of data providing nodes that match the data condition information, and the method further comprises:

the plurality of non-private data is associated based on an association between the global identifier and at least two items of attribute information stored in the distributed system, the association between the global identifier and at least two items of attribute information being stored by at least two data providing nodes in the blockchain system in response to the first smart contract.

14. A data providing node, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

15. A data usage node, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 9-13.

16. A distributed system, comprising:

a plurality of data providing nodes according to claim 14; and

the data usage node of claim 15.

17. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1-13.