CN116414875A - Data processing apparatus and data processing method - Google Patents
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Abstract
The present disclosure relates to a data processing apparatus and a data processing method for secure data sharing and incentive based on blockchain. A blockchain architecture-based data processing device according to the present disclosure includes: a first interface unit that receives a data analysis request from a data user and transmits a verified data analysis result to the data user; a retrieval unit that retrieves matching data profiles in the blockchain ledger according to the data analysis request; a second interface unit that transmits a data analysis instruction to the corresponding data owner according to the retrieved data profile, and receives a data analysis result and a related proof obtained by the data owner analyzing the matched data according to the data analysis instruction; and a verification unit that verifies the data analysis result by aggregating the promises using the smart contract to obtain a verified data analysis result. According to the data processing technology disclosed by the invention, aggregation and sharing of multi-dimensional mass data can be safely and efficiently realized.
Description
Technical Field
The present disclosure relates generally to the field of data processing, and more particularly, to a data processing apparatus and a data processing method for secure sharing and incentive of data based on a blockchain architecture.
Background
Blockchains can be viewed as a distributed database that operates in a decentralized manner. The block chain technology realizes the point-to-point transaction, coordination and cooperation based on decentralization under the condition that transaction nodes in a distributed system do not need to trust each other by using means of data encryption, time stamping, distributed consensus, economic incentive and the like, thereby solving the problems of high cost, low efficiency, unsafe data storage and the like commonly existing in a decentralization mechanism.
In recent years, blockchains have been widely used in various fields such as finance, economy, science and technology, and even politics as a new form of distributed infrastructure with universality.
With the development and popularization of technologies such as mobile networks, internet of things and social networks, the total data amount at the present stage is increased in geometric progression, so that the technology is called as big data age. Currently, existing data is typically focused on a number of different institutions. For example, users' social networking data is mostly focused on large social networking sites; the online shopping data of the user are concentrated on each shopping website; the data of the user such as call, GPS moving path and the like are concentrated on each large mobile network operator; etc. However, due to privacy protection and data security concerns, these mechanisms form "data islands" with respect to each other, making sharing of data difficult.
If the secure aggregation of the multi-dimensional mass data can be realized, the intrinsic value of the data can be fully mined and utilized, and the 'treasure' hidden in the data can be found out. For example, by aggregating social network data of users stored by each large social networking site, hot spot problems in the social network can be quickly discovered. For example, by aggregating the GPS travel path data of users stored by each large mobile network operator, contact persons with the COVID-19 cases can be quickly discovered.
Data mining and utilization as described above requires aggregation of data from multiple data owners, however in practical applications, data integration and sharing presents difficulties. In particular, for data owners, they are reluctant to share data in view of policy regulations regarding data privacy and security (e.g., GDPR policy of the european union, data security laws of china, etc.). In addition, there is also a risk for the data users that they cannot verify the authenticity of the data provided by the data owners before they use the data. Furthermore, existing data sharing methods lack a certain incentive mechanism, so that it is difficult to attract other multidimensional data.
Disclosure of Invention
In order to solve the above-mentioned problems in the prior art, the present disclosure proposes a data processing technique for secure sharing and incentive of data based on blockchain. The data processing technique may register the data owner (i.e., the data source) on the blockchain, but only the aggregate commitments and data profile of the data owner are registered in the blockchain ledger for privacy protection. Further, according to the data processing technology of the present disclosure, when a data user requests data, a data profile of a data owner registered in a blockchain ledger is retrieved according to a data analysis request, thereby selecting a data item of the matched data owner, and triggering the data owner to perform data analysis. Subsequently, the data processing technique according to the present disclosure verifies the data analysis results of the data owners through the aggregate commitments using the intelligent contracts of the blockchain, and transmits the verified data analysis results to the data users. Furthermore, the data processing techniques of the present disclosure may also assign corresponding benefits to data owners as incentives for data sharing based on the data analysis results.
A brief summary of the disclosure will be presented below in order to provide a basic understanding of some aspects of the disclosure. It should be understood that this summary is not an exhaustive overview of the disclosure, nor is it intended to identify key or critical elements of the disclosure or to delineate the scope of the disclosure. Its purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is discussed later.
According to one aspect of the present disclosure, there is provided a data processing apparatus based on a blockchain architecture, comprising: a first interface unit configured to receive a data analysis request from a data user and to send a verified data analysis result to the data user; a retrieval unit configured to retrieve matching data profiles in a blockchain ledger in accordance with the data analysis request; a second interface unit configured to send a data analysis indication to a respective data owner according to the retrieved data profile, and to receive a data analysis result and a related proof obtained by the data owner analyzing the matched data item according to the data analysis indication; and a verification unit configured to verify the data analysis result by aggregating promises using a smart contract to obtain the verified data analysis result.
According to another aspect of the present disclosure, there is provided a data processing method based on a blockchain architecture, including: receiving a data analysis request from a data user; retrieving the matched data profile in the blockchain ledger according to the data analysis request, and sending a data analysis instruction to the corresponding data owner according to the retrieved data profile; receiving data analysis results and associated proofs obtained by the data owners from analyzing the matched data items according to the data analysis indications; and validating the data analysis results by aggregating promises using smart contracts and transmitting the validated data analysis results to the data user.
According to another aspect of the present disclosure, there is provided a computer program capable of implementing the above-described data processing method. Furthermore, a computer program product in the form of at least a computer readable medium is provided, on which a computer program code for implementing the above-mentioned data processing method is recorded.
According to the data processing technology based on the block chain architecture, aggregation and sharing of multi-dimensional mass data can be safely and efficiently achieved.
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The above and other objects, features and advantages of the present disclosure will be more readily understood by reference to the following description of the embodiments of the disclosure taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a block diagram illustrating a data processing apparatus according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating a process performed by a data processing apparatus according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram showing an exemplary registration process performed by a registration unit according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram showing an exemplary retrieval process performed by the retrieval unit according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating data analysis results and associated credentials provided to a validation unit according to an embodiment of the present disclosure;
Fig. 6 is a schematic diagram illustrating an exemplary first authentication process performed by an authentication unit according to an embodiment of the present disclosure;
fig. 7 is a schematic diagram illustrating an exemplary second authentication process performed by an authentication unit according to an embodiment of the present disclosure;
FIG. 8 is a schematic diagram showing one application example of a data processing apparatus according to an embodiment of the present disclosure;
FIG. 9 is a flow chart illustrating a data processing method according to an embodiment of the present disclosure; and
fig. 10 is a block diagram illustrating a general-purpose machine that may be used to implement a data processing method and data processing apparatus according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, some embodiments of the present disclosure will be described in detail with reference to the attached illustrative drawings. Where elements of the drawings are designated by reference numerals, the same elements will be designated by the same reference numerals although the same elements are illustrated in different drawings. Further, in the following description of the present disclosure, a detailed description of known functions and configurations incorporated herein will be omitted where it may make the subject matter of the present disclosure unclear.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular is intended to include the plural unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes," and/or "having," when used in this specification, are intended to specify the presence of stated features, entities, operations, and/or components, but do not preclude the presence or addition of one or more other features, entities, operations, and/or components.
Unless defined otherwise, all terms including technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this inventive concept belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. The present disclosure may be practiced without some or all of these specific details. In other instances, only components that are germane to schemes according to the present disclosure have been shown in the drawings, while other details that are not germane to the present disclosure have been omitted in order to avoid obscuring the present disclosure with unnecessary detail.
Hereinafter, a data processing apparatus and a data processing method for data security sharing and incentive according to embodiments of the present disclosure will be described in detail with reference to the accompanying drawings, taking a Hyperledger Fabric architecture of a blockchain as an example. However, those skilled in the art will recognize that the inventive concepts of the present disclosure are equally applicable to other blockchain architectures. Furthermore, a blockchain may be any coalition chain in accordance with the present disclosure.
Fig. 1 is a block diagram illustrating a data processing apparatus 100 according to an embodiment of the present disclosure. Fig. 2 is a schematic diagram showing a process performed by the data processing apparatus 100 according to the embodiment of the present disclosure.
Blockchains are built based on a point-to-point (peer-to-peer) distributed network where nodes in the distributed network participate together in a consensus process to complete verification and logging of transactions. In the blockchain transaction process of the Hyperledger Fabric architecture, the client requests the peer nodes (distributed nodes) for the transaction, including parameters required for the transaction. The peer node executes the transaction by invoking the chain code through the smart contract, execution of the transaction resulting in a read-write set of state data reads and writes. The distributed node then endorses the read-write collection and broadcasts the transaction to the ordering nodes that provide the ordering service. The ordering nodes order the transactions in the pool of transactions based on the consensus and package them into chunks to form a chunk queue, which is then distributed to the individual peer nodes. Each peer node will verify all transactions in the block, including verification endorsement policies and version conflict verification, and transactions that do not pass verification will be marked as invalid. Finally, each peer node informs the client of the success or failure of the transaction, thereby completing the transaction. Valid transactions are submitted to the ledger, status database and history database. The above-described blockchain transaction and billing processes, and the blockchain ledger data generated thereby, are known to those of skill in the art, and thus, further detailed descriptions of details thereof are omitted herein for the sake of brevity.
As shown in fig. 1, a data processing apparatus 100 according to some embodiments of the present disclosure may include a first interface unit 101, a retrieval unit 102, a second interface unit 103, and a verification unit 104, shown in fig. 1 with solid line boxes.
According to an embodiment of the present disclosure, the first interface unit 101 may receive a data analysis request from a data user, and may transmit a verified data analysis result to the data user. According to embodiments of the present disclosure, the first interface unit 101 may interface with a data user to enable bi-directional communication with the data user.
According to embodiments of the present disclosure, retrieval unit 102 may retrieve matching data profiles in a blockchain ledger based on the data analysis request.
According to embodiments of the present disclosure, the second interface unit 103 may send a data analysis indication to the respective data owners according to the retrieved data profile, and may receive data analysis results and related proofs obtained by the data owners analyzing the matching data items according to the data analysis indication. According to embodiments of the present disclosure, the second interface unit 103 may interface with the data owner to enable bi-directional communication with the data owner.
According to embodiments of the present disclosure, the validation unit 104 may validate the data analysis results through the aggregate commitment using the smart contract to obtain validated data analysis results.
According to other embodiments of the present disclosure, the data processing apparatus 100 may further comprise an allocation unit 105 and a registration unit 106, shown in fig. 1 with a dashed box.
According to an embodiment of the present disclosure, the allocation unit 105 may allocate the benefit corresponding to the verified data analysis result to the data owner using the smart contract.
According to embodiments of the present disclosure, registration unit 106 may generate a data profile and an aggregate promise based on information about data owned by a data owner and register the data profile and the aggregate promise to a blockchain ledger.
To facilitate an understanding of the technical solution of the present disclosure, the processing of the data processing apparatus 100 according to the embodiment of the present disclosure will be described first with reference to fig. 2. Fig. 2 is a schematic diagram showing a process performed by the data processing apparatus 100 according to the embodiment of the present disclosure.
As shown in fig. 2, the data processing apparatus 100 according to the embodiment of the present disclosure enables a data user to obtain a desired data analysis result from a data owner.
Specifically, as shown in fig. 2, the first interface unit 101 may receive a data analysis request from a data user. According to embodiments of the present disclosure, the data analysis request may include information indicative of data items of interest to the data user.
Subsequently, as shown in fig. 2, the retrieving unit 102 may retrieve the matching data profile in the blockchain ledger according to the data analysis request received by the first interface unit 101. According to embodiments of the present disclosure, the data analysis request sent by the data user may have the same format as the data profile registered in the blockchain ledger. In other words, the data analysis request may be a data profile having the same format as the data profile registered in the blockchain ledger. This will be described in more detail below.
The retrieval unit 102 may select a data owner having data items of interest to the data user, such as the first data owner, the second data owner and the third data owner shown in fig. 2, based on the matching data profile. The data owner may also be referred to herein as a "data source". Each data owner may have a data set made up of a plurality of data items. The data set of the data owners selected by the retrieval unit 102 comprises data items of interest to the data users.
Furthermore, those skilled in the art will recognize that the number of data owners selected by the retrieval unit 102 may be more or less than three.
Subsequently, as shown in fig. 2, the second interface unit 103 may send a data analysis instruction to the data owners selected by the retrieval unit 102, such as the first data owner, the second data owner, and the third data owner shown in fig. 2. Each data owner may perform a corresponding data analysis on the data items of interest to the data user based on the data analysis indication and send the data analysis results and corresponding credentials to the verification unit 104.
Subsequently, as shown in fig. 2, the verification unit 104 may verify the correctness of the data analysis result transmitted by the data owner based on the proof about the data analysis result transmitted by the data owner and the aggregate promise registered in the blockchain ledger using the intelligent contract of the blockchain, and transmit the data analysis result passing the verification to the data user via the first interface unit 101.
Alternatively, as shown in fig. 2, the allocation unit 105 may allocate the benefit corresponding to the verified data analysis result to the data owner who provides the data analysis result using the smart contract. According to embodiments of the present disclosure, the benefit may be determined based on at least one of several factors, as will be described in more detail below.
Further, as shown in fig. 2, in order to perform the above-described processing, it is necessary to first register information on data owned by the data owner on the blockchain ledger. According to embodiments of the present disclosure, registration unit 106 may generate a data profile and an aggregate promise based on information about data owned by a data owner and register the data profile and the aggregate promise to a blockchain ledger.
Fig. 3 is a schematic diagram illustrating an exemplary registration process performed by the registration unit 106 according to an embodiment of the present disclosure.
According to embodiments of the present disclosure, registration unit 106 may generate a data profile and an aggregate promise based on information about data owned by a data owner and register the data profile and the aggregate promise to a blockchain ledger. According to embodiments of the present disclosure, the information about the data owned by the data owner may include a hash value of the data item or at least one feature of the data item owned by the data owner. Thus, in accordance with embodiments of the present disclosure, the aggregate commitment may be an aggregate commitment associated with a hash value, and the data profile may be a location indication associated with the hash value.
Specifically, as shown in fig. 3, a specific example of registering data owned by a first data owner with a blockchain ledger is shown. According to embodiments of the present disclosure, in order to protect the privacy of the data owner, only the aggregate commitments and data profiles of the data owned by the data owner are registered into the blockchain ledger.
According to an embodiment of the present disclosure, the hash value may be submitted to the smart contract after being processed through randomization and through encryption using the private key of the data owner, and the registration unit 106 may recover the hash value through decryption using the public key of the data owner and through de-randomization using the smart contract.
In particular, as shown in FIG. 3, the data owned by the first data owner may be represented as being represented by a plurality of data items x i And x j The data set X is composed, wherein 1.ltoreq.i, j.ltoreq.N, N can be arbitrarily set according to design requirements, and is typically greater than the sum of the number of data items included in the data set of all data owners. The first data owner furthermore has a corresponding key { pk, sk }, where pk is the public key for decryption and sk is the private key for encryption.
According to an embodiment of the present disclosure, the first data owner may perform encryption processing and randomization processing on the hash value of each data item in its data set X using the private key sk, and transmit the processed hash value to the blockchain via the second interface unit 103 (not shown in fig. 3). For example, for data item x i And x j The first data owners may generate the processed hash values enc (hash (x i ),sk,r i ) And enc (hash (x j ),sk,r j ) Wherein enc () represents an encryption process using the private key sk; hash () represents a hash function, which may be any hash function known in the art; and r is i And r j Respectively represent for data item x i And x j The generated randomization parameters, which may be generated by any random source known in the art.
According to an embodiment of the present disclosure, a data item (e.g., x i And x j ) For example, hash value (hash (x) i ) And hash (x) j ) A hash value for the data item itself or a hash value for at least one feature of the data item.
As shown in fig. 3, according to an embodiment of the present disclosure, registration unit 106 may use a blockchain smart contract by using the public of the first data ownerThe key pk receives the encrypted hash value enc (hash (x i ),sk,r i ) And enc (hash (x j ),sk,r j ) Performing decryption processing and performing de-randomization processing, i.e. removing randomization parameter r i And r j To recover data item x i And x j Hash value hash (x i ) And hash (x) j )。
Subsequently, according to embodiments of the present disclosure, registration unit 106 may use the intelligent contract of the blockchain by using a hash value, such as a hash (x i ) And hash (x) j ) Merkle trees are constructed to generate aggregate promises. Specifically, as shown in fig. 3, the registration unit 106 may restore a restored hash value, such as a hash (x i ) And hash (x) j ) As leaf nodes of the Merkle tree, hash computation is performed two by two to obtain a root node (root) of the Merkle tree, which is indicated by a gray box in fig. 3. The root node of the Merkle tree may be considered an aggregate commitment of the data set X of the first data owner. Since Merkle tree structures are known to those skilled in the art, further detailed descriptions of their details are omitted herein for the sake of brevity.
Furthermore, according to an embodiment of the present disclosure, the registration unit 106 may map the hash values of all the data items of the data set X to the N-bit data profile through the mapping process. The data profile is a binary vector that can be considered as a location indication for the hash value. Specifically, as shown in fig. 3, each bit in the N-bit data profile may be initialized to 0, and when the hash value is mapped to the corresponding bit through the mapping process, the bit may be set to 1. That is, the registration unit 106 may store the data item (e.g., X) of the data set (e.g., X) of the first data owner i And x j ) For example, hash value (hash (x) i ) And hash (x) j ) Mapped to a specific location in the data profile in the form of an N-bit vector. The mapping process may be, for example, a Bloom Filter (Bloom Filter). In view of the bloom filters known to those skilled in the art, further detailed description of the details thereof is omitted herein for the sake of brevity.
For other data owners (e.g., the second data owner and the third data owner shown in fig. 2), the registration unit 106 may perform the same processing as described above for the first data owner. In this way, data owned by the data owner may be registered in the blockchain ledger in the form of aggregate commitments and data profiles, while protecting the privacy of the data owner.
Fig. 4 is a schematic diagram showing an exemplary search process performed by the search unit 102 according to the embodiment of the present disclosure. In view of the fact that the data user may be interested in only certain data items in the data sets of the respective data owners, the retrieving unit 102 may use the data profile to retrieve data items of interest to the data user in the respective data sets.
According to embodiments of the present disclosure, the data analysis request received from the data user via the first interface unit 101 may include a location indication. Accordingly, the retrieval unit 102 may retrieve the data profile matching the data analysis request according to the location indication included therein using the smart contract.
Specifically, as shown in fig. 4, the data analysis request may have the form of a binary vector of M bits to be consistent with the data profile registered in the blockchain ledger, where M may be arbitrarily set according to design requirements, and M may be set to be equal to N described above.
As shown in fig. 4, the data set X owned by the first data owner is described as an example. As described above, the data profile of data set X registered in the blockchain ledger has the form of a binary vector of N bits, which can be regarded as a positional indication of each data item included in data set X. For example, as shown in FIG. 4, a data profile representation corresponding to data set X in the form of an N-bit vector has data item X i And x j I.e. with data item x in an N-bit vector i And x j The bits of the corresponding positions have a value of 1, indicated by grey boxes in fig. 4. Further, as shown in FIG. 4, a data analysis request in the form of an M-bit vector indicates which data items the data user wishes to analyze. In particular, in an M-bit vector, data of interestThe bit corresponding to the item has a value of 1, represented in fig. 4 by a grey box, while the bit corresponding to the other data item not of interest has a value of 0.
Thus, the data is analyzed by a logical AND (AND) operation of the N-bit vector as the data profile AND the M-bit vector as the data analysis request (symbolized in FIG. 4 "&"representative"), the retrieval unit 102 may conveniently select data items of interest to the data user in the data set X. For example, as shown in fig. 4, the retrieval unit 102 may determine the data item X included in the data set X i Is a data item of interest to the data user. By the retrieval process by the retrieval unit 102 shown in fig. 4, it is possible to locate a data owner having a data item of interest to the data user without affecting the privacy of the data owner.
The above-described processing performed by the retrieval unit 102 may be implemented by a blockchain smart contract according to embodiments of the present disclosure. According to embodiments of the present disclosure, after determining which data owners possess data items of interest to the data user, the retrieval unit 102 may send data analysis indications to these data owners via the second interface unit 103, instructing these data owners to perform data analysis specified by the data user on the data items of interest to the data user.
According to embodiments of the present disclosure, data analysis of data items of interest to a user is performed locally by a data owner.
According to an embodiment of the present disclosure, after the data owner analyzes the matched data item according to the data analysis indication to obtain the data analysis result, the data analysis result and the related proof are sent to the verification unit 104 via the second interface unit 103. According to embodiments of the present disclosure, the validation unit 104 may validate the data analysis results through the aggregate commitments using the intelligent contracts of the blockchain to obtain validated data analysis results.
Fig. 5 is a schematic diagram showing data analysis results and related proofs provided to a verification unit according to an embodiment of the present disclosure. According to embodiments of the present disclosure, the attestation may include a data item attestation and a zero knowledge attestation.
The data item proves that the data item on which the data analysis performed by the data owner is based is correct. According to embodiments of the present disclosure, the data item proof may be a path generated by using a Merkle tree based on the data item to be analyzed. For example, as shown in FIG. 5, gray nodes in the Merkle tree represent data items x i And x j Paths in the Merkle tree, represented by grey boxes in fig. 5, respectively, may be used as data items x i And x j Is proved by the data item of the (a).
Furthermore, zero knowledge proves that the data analysis performed by the data owner is correct. This will be described in more detail below.
According to embodiments of the present disclosure, the verification unit 104 may verify the data analysis results returned by the data owner using the data item proof, the zero knowledge proof (Zero Knowledge Proof), and the aggregate promise. According to an embodiment of the present disclosure, the verification unit 104 may perform a first verification process for verifying whether a data item on which data analysis performed by a data owner is based is correct, and a second verification process for verifying whether a data analysis process performed by the data owner on the data item is correct.
Fig. 6 is a schematic diagram illustrating an exemplary first authentication process performed by the authentication unit 104 according to an embodiment of the present disclosure. As shown in fig. 6, according to an embodiment of the present disclosure, the first verification process may be performed using data item attestation and aggregate commitments. Specifically, as shown in FIG. 6, taking as an example the data set X possessed by the first data owner, the verification unit 104 may use the Merkle tree to verify its aggregate promise registered in the blockchain ledger (i.e., the root node of the Merkle tree, represented by the dark gray boxes in FIG. 6) against the return (e.g., X i And x j In fig. 6, represented by a light gray box) to determine that the data item on which the data analysis performed by the data owner is based is indeed the data item of interest to the data user.
Fig. 7 is a schematic diagram illustrating an exemplary second authentication process performed by the authentication unit 104 according to an embodiment of the present disclosure. As shown in fig. 7, the second verification process may be performed using zero knowledge proof and aggregate commitments, according to an embodiment of the disclosure.
Zero knowledge proof can verify the correctness of a general purpose calculation without exposing any calculation related information. The zero knowledge proof converts the general calculation into a calculation logic circuit according to the calculation steps, constrains each circuit gate, formalizes and unifies the constraints of all the circuit gates, and integrates the constraints to form a circuit constraint system. The correctness of the general calculation is converted into the satisfaction of the circuit constraint system. The circuit constraint system is converted into a polynomial representation, and the correctness of the general calculation is converted into the correctness of the polynomial again. And sampling verification is carried out on the value of the polynomial on the definition domain, so that the correctness verification of the general calculation is realized. In the process, through the cryptography scheme, the verification party can verify the correctness of the general calculation without obtaining any general calculation related information. Since zero knowledge proves to be known to those skilled in the art, further detailed description of the details thereof is omitted herein for the sake of brevity.
Specifically, as shown in FIG. 7, taking as an example the data set X that the first data owner has, the data owner has a function of selecting data items (e.g., X i And x j ) When data analysis is performed and a proof is generated, the specific content of the data item cannot be exposed for privacy reasons. Thus, the data owner can select the specific content (e.g., x i And x j As a private input for zero knowledge proof calculation, while simultaneously storing hash values (e.g., hash (x) i ) And hash (x) j ) As a public input of the zero knowledge proof, together with the calculated corresponding circuit constraint, is sent as a final zero knowledge proof to the verification unit 104.
According to embodiments of the present disclosure, the validation unit 104 may verify the correctness of the zero knowledge proof using a smart contract of the blockchain, including verifying the data item (e.g., x i And x j ) For example, hash value (hash (x) i ) And hash (x) j ) Whether the data set X matches, whether the process of verifying the data analysis is correct, and whether the result of the data analysis is indeed provided by the data owner of the data set X, i.e. the first data owner.
According to an embodiment of the present disclosure, after verifying the data analysis result returned from the data owner, the verification unit 104 may transmit the verified data analysis result to the data user via the first interface unit 101.
According to an embodiment of the present disclosure, after transmitting the verified data analysis result to the data user, the distribution unit 105 may determine the benefit of the data owner according to at least one of the quality of the data owned by the data owner, the integrity of the data owner, and the computational complexity of the data analysis performed by the data owner and distribute the benefit to the corresponding data owner.
According to embodiments of the present disclosure, the quality of data owned by a data owner is determined based on the amount of data owned by the data owner that matches the data analysis request and the importance of the data analysis results returned by the data owner. For example, the larger the amount of data in the data set that matches the data analysis request of the data user, the higher the compliance with the data analysis request, and the higher the corresponding benefit. In addition, for example, the higher the importance of the data analysis results returned by the data owner, the more scarce the data analysis results are to the data user, and the higher the corresponding benefits.
According to embodiments of the present disclosure, the integrity of the data owner is determined based on the verification pass rate of the data analysis results returned by the data owner. For example, if the number of times the data analysis result returned by the data owner passes the verification by the verification unit 104 is greater, the higher the integrity of the data owner is indicated, the higher the corresponding benefit is.
Furthermore, similarly, the higher the computational complexity of the data analysis by the data owner, the higher the corresponding revenue.
Fig. 8 is a schematic diagram showing one application example of the data processing apparatus 100 according to the embodiment of the present disclosure.
As shown in FIG. 8, the data user may be, for example, a research institution for a COVID-19 epidemic that requires integrated analysis of the details of the COVID-19 cases in each of hospitals A, B and C. Hospitals a through C may be data owners. From a hospital point of view, they do not wish to provide any medical records to the research institution during the data analysis process; whereas the research institution must confirm that the hospital adopted the actual covd-19 cases and used the correct data analysis method during the data analysis.
Thus, according to embodiments of the present disclosure, hospitals a to C may register aggregated promises and data profiles regarding their medical records to the blockchain ledger via the second interface unit 103 through the registration unit 106. Medical records of hospitals a to C as data sets may include medical records regarding cases of various diseases, such as cancer, cardiovascular diseases, and covd-19 as data items. In this regard, the research institution is only interested in medical records of COVID-19. Thus, the data analysis request sent by the research institution to the retrieval unit 102 via the first interface unit 101 only contains an analysis request for the medical records of the covd-19. Accordingly, the retrieval unit 102 selects hospitals having medical records of covd-19 through the retrieval process described above, and transmits data analysis instructions for analyzing the medical records of covd-19 to these hospitals via the second interface unit 103. After the selected hospital performs data analysis required for the research institution on the medical records of the covd-19 according to the data analysis instruction, the data analysis result and the related certification are transmitted to the verification unit 104 through the second interface unit 103, the verification unit 104 may verify the data analysis result using the certification and the aggregate promise registered in the blockchain ledger, and transmit the verified data analysis result regarding the covd-19 to the data user through the first interface unit 101.
Accordingly, the distribution unit 105 may divide the revenue to hospitals that provide validated data analysis results, e.g., pay a corresponding fee for using its medical records and performing data analysis. In this way, the research institution can obtain the data analysis result about the covd-19 from the hospital under the condition of ensuring the safety and privacy, and the hospital can obtain the corresponding benefit.
The present disclosure also provides a data processing method for data security sharing and incentive based on the blockchain architecture. Fig. 9 is a flowchart illustrating a data processing method 900 according to an embodiment of the present disclosure.
The data processing method 900 starts in step S901.
Subsequently, in step S902, a data analysis request from a data user is received. Subsequently, in step S903, the matching data profile in the blockchain ledger is retrieved according to the data analysis request, and a data analysis indication is sent to the corresponding data owner according to the retrieved data profile. Subsequently, in step S904, a data analysis result and a related proof obtained by the data owner analyzing the matched data according to the data analysis instruction are received. Subsequently, in step 905, the data analysis result is verified through the aggregate promise using the smart contract and the verified data analysis result is transmitted to the data user.
According to other embodiments of the present disclosure, the data processing method 900 may further include step S906 (shown with a dashed box in fig. 9) in which a data profile and aggregate promise are generated based on information about data owned by the data owner and registered with the blockchain ledger.
Furthermore, according to other embodiments of the present disclosure, the data processing method 900 may further include a step S907 (shown with a dashed box in fig. 9) in which the revenue corresponding to the verified data analysis result is distributed to the data owners using the smart contracts.
According to an embodiment of the present disclosure, the processing in steps S902 to S907 described above may be implemented by, for example, the first interface unit 101, the retrieving unit 102, the second interface unit 103, the verifying unit 104, the allocating unit 105, and the registering unit 106 included in the data processing apparatus 100 described above with reference to fig. 1 to 8, and will not be described again here.
Finally, the data processing method 900 ends at step S908.
According to the data processing technology disclosed by the invention, aggregation and sharing of multi-dimensional mass data can be safely and efficiently realized.
In particular, by using aggregate promises, data users' needs for data sets can be quickly matched, but without revealing the private information of the original data set.
Further, by using the aggregate promise, the data item proof returned by the data owner, and the zero knowledge proof of the data analysis in combination, it can be verified that the data item employed by the data owner when performing the data analysis locally is indeed the data item of interest to the data user, and it can also be verified that the data owner has no objection in performing the data analysis locally.
Fig. 10 is a block diagram illustrating a general machine 1000 that may be used to implement a data processing method 900 and a data processing apparatus 100 according to embodiments of the present disclosure. The general-purpose machine 1000 may be, for example, a computer system. It should be noted that the general machine 1000 is only one example, and does not imply limitation on the scope of use or functions of the data processing method and data processing apparatus of the present disclosure. Nor should the general machine 1000 be construed as having dependencies or requirements on any of the components shown in the data processing methods or data processing apparatus described above, or a combination thereof.
In fig. 10, a Central Processing Unit (CPU) 1001 performs various processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage section 1008 to a Random Access Memory (RAM) 1003. In the RAM 1003, data necessary when the CPU 1001 executes various processes and the like is also stored as needed. The CPU 1001, ROM 1002, and RAM 1003 are connected to each other via a bus 1004. An input/output interface 1005 is also connected to the bus 1004.
The following components are also connected to the input/output interface 1005: an input section 1006 (including a keyboard, a mouse, and the like), an output section 1007 (including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like), a storage section 1008 (including a hard disk, and the like), and a communication section 1009 (including a network interface card such as a LAN card, a modem, and the like). The communication section 1009 performs communication processing via a network such as the internet. The drive 1010 may also be connected to the input/output interface 1005, as desired. A removable medium 1011 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like may be installed on the drive 1010 as needed, so that a computer program read out therefrom can be installed into the storage section 1008 as needed.
In the case where the series of processes described above is implemented by software, a program constituting the software may be installed from a network such as the internet or from a storage medium such as the removable medium 1011.
It will be understood by those skilled in the art that such a storage medium is not limited to the removable medium 1011 shown in fig. 10, in which the program is stored, which is distributed separately from the apparatus to provide the program to the user. Examples of the removable medium 1011 include a magnetic disk (including a floppy disk), an optical disk (including a compact disk read only memory (CD-ROM) and a Digital Versatile Disk (DVD)), a magneto-optical disk (including a Mini Disk (MD) (registered trademark)), and a semiconductor memory. Alternatively, the storage medium may be a hard disk or the like contained in the ROM 1002, the storage section 1008, or the like, in which a program is stored, and distributed to users together with a device containing them.
The present disclosure also provides a program product having stored thereon machine-readable instruction code. The instruction codes, when read and executed by a machine, may perform the data processing method according to the present disclosure described above. Accordingly, various storage media, as enumerated above, for carrying such program products are included within the scope of the present disclosure.
Specific embodiments of an apparatus and/or method according to embodiments of the present disclosure have been described above in detail with reference to block diagrams, flowcharts, and/or embodiments. When such block diagrams, flowcharts, and/or implementations comprise one or more functions and/or operations, it will be apparent to those skilled in the art that the functions and/or operations of such block diagrams, flowcharts, and/or implementations may be implemented by various hardware, software, firmware, or virtually any combination thereof. In one embodiment, portions of the subject matter described in this specification can be implemented by an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), or other integrated form. However, those skilled in the art will recognize that some aspects of the embodiments described herein can be equivalently implemented in whole or in part in the form of one or more computer programs running on one or more computers (e.g., in the form of one or more computer programs running on one or more computer systems), in the form of one or more programs running on one or more processors (e.g., in the form of one or more programs running on one or more microprocessors), in the form of firmware, or in virtually any combination thereof, and that designing the circuitry for and/or writing the code for the software and/or firmware of this disclosure is well within the skill of one of skill in the art in light of this disclosure.
While the disclosure has been disclosed by the foregoing description of specific embodiments thereof, it will be understood that various modifications, improvements, or equivalents may be devised by those skilled in the art that will fall within the spirit and scope of the appended claims. Such modifications, improvements, or equivalents are intended to be included within the scope of this disclosure.
In summary, in embodiments according to the present disclosure, the present disclosure provides the following, but is not limited thereto:
a first interface unit configured to receive a data analysis request from a data user and to send a verified data analysis result to the data user;
a retrieval unit configured to retrieve matching data profiles in a blockchain ledger in accordance with the data analysis request;
a second interface unit configured to send a data analysis indication to a respective data owner according to the retrieved data profile, and to receive a data analysis result and a related proof obtained by the data owner analyzing the matched data item according to the data analysis indication; and
And a verification unit configured to verify the data analysis result by aggregating promises using a smart contract to obtain the verified data analysis result.
The data processing apparatus according to claim 1, further comprising:
an allocation unit configured to allocate a benefit corresponding to the verified data analysis result to the data owner using the smart contract.
The data processing apparatus according to claim 1, further comprising:
a registration unit configured to generate the data profile and the aggregate promise based on information about data owned by the data owner, and register the data profile and the aggregate promise to the blockchain ledger.
The data processing apparatus according to claim 3, wherein the information includes a hash value of a data item owned by the data owner or at least one feature of the data item.
The data processing apparatus of claim 4, wherein the aggregate commitment is an aggregate commitment associated with the hash value and the data profile is a location indication associated with the hash value.
Scheme 6. According to the data processing device of scheme 4,
Wherein the hash value is submitted to the smart contract after being processed by randomization and by encryption using a private key of the data owner, an
Wherein the registration unit restores the hash value by a decryption process using the public key of the data owner and by a derandomization process using the smart contract.
Scheme 7. The data processing apparatus according to scheme 5, wherein the registration unit generates the aggregate promise by constructing a Merkle tree using the hash value.
Scheme 8. The data processing device according to scheme 5,
wherein the data analysis request includes a location indication, an
Wherein the retrieving unit retrieves a data profile matching the location indication included in the data analysis request using the smart contract.
Scheme 9. The data processing apparatus according to scheme 1, wherein the attestation comprises a data item attestation and a zero knowledge attestation.
Scheme 12. The data processing apparatus according to scheme 11, wherein the verification unit is configured to verify, using the data item proof and the aggregate promise, whether the data item on which the data analysis performed by the owner of the data is based is correct.
Scheme 13. The data processing apparatus according to scheme 11, wherein the verification unit is configured to verify, using the zero knowledge proof, whether the data analysis process performed by the data owner on the data item is correct.
Scheme 14. The data processing apparatus according to scheme 2, wherein the allocation unit is configured to determine the benefit according to at least one of a quality of data owned by the data owner, an integrity of the data owner, and a computational complexity of data analysis by the data owner.
The data processing apparatus according to claim 14, wherein the quality of the data owned by the data owner is determined based on the amount of data owned by the data owner that matches the data analysis request and the importance of the data analysis result returned by the data owner.
Scheme 16. The data processing apparatus of scheme 14 wherein the trustworthiness of the data owner is determined based on a verification pass rate of the data analysis results returned by the data owner.
Scheme 17. A data processing method based on blockchain architecture, comprising:
receiving a data analysis request from a data user;
retrieving the matched data profile in the blockchain ledger according to the data analysis request, and sending a data analysis instruction to the corresponding data owner according to the retrieved data profile;
receiving data analysis results and associated proofs obtained by the data owners from analyzing the matched data items according to the data analysis indications; and
validating the data analysis results through aggregate commitments using smart contracts and sending the validated data analysis results to the data user.
Scheme 18. The data processing method according to scheme 17, further comprising:
and allocating benefits corresponding to the verified data analysis result to the data owners using the smart contracts.
The data processing method according to claim 17, further comprising:
the data profile and the aggregate promise are generated based on information about data owned by the data owner, and the data profile and the aggregate promise are registered with the blockchain ledger.
Scheme 20. A computer readable storage medium having stored thereon a program for implementing a data processing method based on a blockchain architecture, the data processing method comprising:
Receiving a data analysis request from a data user;
retrieving the matched data profile in the blockchain ledger according to the data analysis request, and sending a data analysis instruction to the corresponding data owner according to the retrieved data profile;
receiving data analysis results and associated proofs obtained by the data owners from analyzing the matched data items according to the data analysis indications; and
validating the data analysis results through aggregate commitments using smart contracts and sending the validated data analysis results to the data user.
Claims (10)
1. A blockchain architecture-based data processing device, comprising:
a first interface unit configured to receive a data analysis request from a data user and to send a verified data analysis result to the data user;
a retrieval unit configured to retrieve matching data profiles in a blockchain ledger in accordance with the data analysis request;
a second interface unit configured to send a data analysis indication to a respective data owner according to the retrieved data profile, and to receive a data analysis result and a related proof obtained by the data owner analyzing the matched data item according to the data analysis indication; and
And a verification unit configured to verify the data analysis result by aggregating promises using a smart contract to obtain the verified data analysis result.
2. The data processing apparatus of claim 1, further comprising:
an allocation unit configured to allocate a benefit corresponding to the verified data analysis result to the data owner using the smart contract.
3. The data processing apparatus of claim 1, further comprising:
a registration unit configured to generate the data profile and the aggregate promise based on information about data owned by the data owner, and register the data profile and the aggregate promise to the blockchain ledger.
4. A data processing apparatus according to claim 3, wherein the information comprises a hash value of a data item owned by the data owner or at least one feature of the data item.
5. The data processing apparatus of claim 4, wherein the aggregate commitment is an aggregate commitment associated with the hash value and the data profile is a location indication associated with the hash value.
6. The data processing apparatus according to claim 4,
Wherein the hash value is submitted to the smart contract after being processed by randomization and by encryption using a private key of the data owner, an
Wherein the registration unit restores the hash value by a decryption process using the public key of the data owner and by a derandomization process using the smart contract.
7. The data processing apparatus of claim 5, wherein the registration unit generates the aggregate commitment by constructing a Merkle tree using the hash value.
8. The data processing apparatus according to claim 5,
wherein the data analysis request includes a location indication, an
Wherein the retrieving unit retrieves a data profile matching the location indication included in the data analysis request using the smart contract.
9. The data processing apparatus of claim 1, wherein the attestation includes a data item attestation and a zero knowledge attestation.
10. A data processing method based on a blockchain architecture, comprising:
receiving a data analysis request from a data user;
retrieving the matched data profile in the blockchain ledger according to the data analysis request, and sending a data analysis instruction to the corresponding data owner according to the retrieved data profile;
Receiving data analysis results and associated proofs obtained by the data owners from analyzing the matched data items according to the data analysis indications; and
validating the data analysis results through aggregate commitments using smart contracts and sending the validated data analysis results to the data user.
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