CN112087439A - Block chain transaction query method, system, computer device and storage medium - Google Patents
Block chain transaction query method, system, computer device and storage medium Download PDFInfo
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Abstract
The application relates to a blockchain transaction query method, a system, a computer device and a storage medium. The method comprises the following steps: encrypting the index value of each transaction in the transaction list according to a preset addition homomorphic random encryption algorithm, determining a query vector, setting the index value of the target query transaction to be nonzero, setting the index values of other transactions to be zero, and decrypting the query result by using a decryption algorithm corresponding to the addition homomorphic random encryption algorithm after obtaining the query result to obtain the transaction content of the target query transaction. By adopting the method, the block chain platform side cannot deduce the query information, and the privacy of the query information and the query result in the transaction query process on the block chain is improved.
Description
Technical Field
The present application relates to the field of blockchain technologies, and in particular, to a method, a system, a computer device, and a storage medium for querying blockchain transactions.
Background
The transaction is the most basic data structure in the blockchain, the transaction list in the block is formed by sequencing through a consensus algorithm, the content of the transaction and the transaction load contains a large amount of valuable information, the information forms the content of the blockchain distributed account book, and the query of the transaction information is the most basic and common operation in the blockchain.
A chain constructed by multi-party business cooperation generally provides a query service of a transaction, a user obtains a certain desired transaction by submitting a query request to a query service provider, and a client and the query service provider have no trust relationship. Privacy issues in such typical interaction scenarios are rarely considered; the contents of the query constructed by the user, such as the query index contained in a certain transaction and the returned data contents, which all contain important privacy information, are all leaked to the transaction query facilitator. The service provider may even deduce certain private information of the user, such as information of interest to the customer, customer preferences, the business the customer transacts, etc., from the content submitted by the customer, as well as from the query result data. How to safely and privately query a certain transaction without knowing the query result and the specific query itself by the platform party is very important for the transaction query user. For example, a user may want to query a transaction that contains important business information, but may not want to divulge its own interest in the transaction and its index to the service provider.
Aiming at the problem that privacy information is revealed in transaction query on a blockchain in the related technology, an effective solution is not provided at present.
Disclosure of Invention
In view of the above, there is a need to provide a method, system, computer device and storage medium for querying blockchain transactions.
In a first aspect, an embodiment of the present application provides a method for querying a blockchain transaction, where the method includes:
encrypting the index value of each transaction in the transaction list according to a preset addition homomorphic random encryption algorithm, and determining a query vector, wherein the index value of the target query transaction is nonzero, and the index values of other transactions are zero;
after the query result is obtained, the query result is decrypted by using a decryption algorithm corresponding to the addition homomorphic random encryption algorithm to obtain the transaction content of the target query transaction, wherein the query result is obtained by performing an addition homomorphic query response algorithm on the query vector.
In one embodiment, the index value of the target query transaction is set to 1 and the index values of the other transactions are set to 0.
In one embodiment, the additive homomorphic random encryption algorithm is a Paillier probabilistic public key encryption algorithm.
In one embodiment, the query result is obtained by performing a modular exponentiation operation on the query vector and a transaction list.
In a second aspect, an embodiment of the present application further provides a method for querying blockchain transactions, where the method includes:
encrypting the index value of each transaction in the transaction list according to a preset addition homomorphic random encryption algorithm, and determining a query vector, wherein the index value of the target query transaction is nonzero, and the index values of other transactions are zero;
performing modular exponentiation operation on the query vector and the transaction list to obtain a query result;
and decrypting the query result by using a decryption algorithm corresponding to the addition homomorphic random encryption algorithm to obtain the transaction content of the target query transaction.
In one embodiment, the performing a modular exponentiation operation on the query vector and a transaction list to obtain the query result includes:
dividing each transaction in the transaction list into data pieces according to a preset dividing rule;
performing modular exponentiation on the data slice of a first transaction and a first query element to obtain an operation intermediate value, wherein the first transaction is a transaction in the transaction list, and the first query element is the encryption index value corresponding to the first transaction in the query vector;
multiplying the operation intermediate values in the same column to obtain a result element, wherein the data pieces of each transaction are arranged in sequence, and the data pieces with the same ordinal number belong to the same column;
and constructing the query result according to the result element.
In one embodiment, the dividing each transaction in the transaction list into data pieces according to a preset dividing rule includes:
and dividing each transaction in the transaction list into equal-length data pieces.
In a third aspect, an embodiment of the present application provides a method for querying a blockchain transaction, where the method includes:
acquiring a query vector, wherein the query vector is determined by encrypting the index value of each transaction in a transaction list according to a preset addition homomorphic random encryption algorithm, the index value of a target query transaction is nonzero, and the index values of other transactions are zero;
and performing an addition homomorphic query response algorithm on the query vector to obtain a query result and sending the query result, wherein the query result is decrypted through a decryption algorithm corresponding to the addition homomorphic random encryption algorithm to obtain the transaction content of the target query transaction.
In a fourth aspect, an embodiment of the present application provides a blockchain transaction query system, where the system includes a blockchain platform side and a query side node:
the inquiring party node encrypts the index value of each transaction in the transaction list according to a preset addition homomorphic random encryption algorithm to determine an inquiry vector, wherein the index value of the target inquiry transaction is nonzero, and the index values of other transactions are zero;
the block chain platform performs modular exponentiation operation on the query vector and the transaction list to obtain a query result;
and the inquiring party node decrypts the inquiring result by using a decryption algorithm corresponding to the addition homomorphic random encryption algorithm to obtain the transaction content of the target inquiring transaction.
In a fifth aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the above block chain transaction query method when executing the computer program.
In a sixth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the above block chain transaction query method.
The block chain transaction query method, the system, the computer equipment and the storage medium encrypt the index value of each transaction in the transaction list according to a preset addition homomorphic random encryption algorithm, determine the query vector, set the index value of the target query transaction to be nonzero, the index values for the other transactions are set to zero and, after the query result is obtained, the data is decrypted using a decryption algorithm corresponding to the additive homomorphic random encryption algorithm, the query result is decrypted to obtain the transaction content of the target query transaction, and the block chain platform party cannot deduce the query information in the query vector in time due to limited computing capacity, so the query mode in the embodiment does not reveal the query privacy information, and simultaneously, because the privacy of the query vector is strong, the privacy of the query result obtained according to the query vector is also strong, and the query result can not be revealed to the block chain platform party.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a diagram illustrating an application scenario of a blockchain transaction query method according to an embodiment of the present invention;
FIG. 2 is a flow diagram of a blockchain transaction query method according to one embodiment of the invention;
FIG. 3 is a block chain transaction query method according to an embodiment of the invention;
FIG. 4 is a flowchart illustrating operations performed on a blockchain platform query vector and a transaction list in a blockchain transaction query method according to an embodiment of the invention;
FIG. 5 is a diagram illustrating a MapReduce processing procedure in the blockchain transaction query method according to an embodiment of the present invention;
FIG. 6 is a flow diagram of a blockchain transaction query method according to another embodiment of the invention;
FIG. 7 is a schematic diagram illustrating interaction of algorithms in a blockchain transaction query method according to an embodiment of the present invention;
FIG. 8 is a block-chain transaction query computer apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments provided in the present application without any inventive work are within the scope of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The use of the terms "including," "comprising," "having," and any variations thereof herein, is meant to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, for example, "a and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference throughout this specification to the terms "first," "second," "third," and the like, merely distinguish between similar objects and do not denote a particular order to the objects.
The block chain is a distributed database, and data stored by each node participating in the block chain behavior is identified to keep data consistency. To facilitate understanding of the blockchain network in the storage system of the present application, the blockchain technique employed in the present application is first exemplified. In some embodiments, an electronic device runs the blockchain technique to become a node of the blockchain network, the blockchain platform infrastructure including a presentation layer, an application layer, a service layer, and a blockchain core support module of a blockchain. The display layer is used for displaying system functions so as to realize user interaction, and each file main body can log in an interface displayed by the corresponding display layer through a system client to access the application layer so as to acquire information resources, wherein the interface comprises a registration interface, an operation execution interface, a query interface, an application management interface, an administrator interface and the like. The application layer is used for displaying specific functions of the block chain network, is an important link of file storage, and can be divided into user management, authority control, directory management and the like according to different functions. The service layer is used for effectively integrating and managing application related functions through the distributed servers, such as user registration, user identity management, encryption and decryption services, distributed book services, intelligent contract services, data management services and the like. The block chain core support module comprises a data layer, a network layer, a consensus layer and a contract layer. The data layer is used for encapsulating underlying data blocks and related data encryption and time stamping technologies. The network layer encapsulates elements such as a P2P networking mode, a message propagation protocol, a data verification mechanism and the like of the blockchain network system, so that nodes are equal in status and mutually communicated in a flat topological structure, and the characteristics of distribution, autonomy, openness, free access and the like are possessed. Each node in the block chain network can participate in the checking and accounting process of the block data, and the block chain can be recorded only after the block data passes verification of most nodes in the whole network. The decentralized design of the block chain ensures that the file data cannot be tampered and forged. The consensus layer encapsulates a method for rapidly completing consensus in a topological network with highly dispersed decision weights so as to participate in a consensus mechanism of a block chain network. The contract layer is packaged with contract codes of data access strategy, when the condition in the contract codes is triggered, the corresponding transaction is automatically executed, and the corresponding data can be accessed by the corresponding access condition specified by the contract content.
Fig. 1 is an application scenario diagram of a blockchain transaction query method according to an embodiment of the present invention, and the blockchain transaction query method provided in the present application may be applied to the application environment shown in fig. 1. Computer device 12 may run any suitable computing system that enables it to access blockchain network 10 as a querier node in blockchain network 10. The blockchain network 10 includes querier nodes and a query platform deployed on the blockchain network 10. The inquiring party node encrypts the inquiry index according to a preset encryption algorithm to obtain an inquiry vector, wherein the inquiry index is used for indicating target inquiry transaction; after the query result is obtained, the query result is decrypted by using a decryption algorithm corresponding to the encryption algorithm to obtain the transaction content of the target query transaction.
The computer device 12 corresponding to the inquiring party node may be any electronic device, such as a server, a mobile phone, a computer, a tablet computer, and the like. In addition, the inquiring node is determined according to the identity information logged in through the node, in some embodiments, the block chain is a block private chain or a block alliance chain, identity registration is completed on a block chain platform in advance, corresponding principals of the node need to complete registration on the block chain platform in advance, corresponding public keys and private keys are obtained, and after the identity of the node is determined to be trusted, related services of the inquiry are allowed to be developed.
In an embodiment, fig. 2 is a flowchart of a blockchain transaction query method according to an embodiment of the present invention, and as shown in fig. 2, a blockchain transaction query method is provided, which is described by taking the method as an example applied to the querying party node in fig. 1, and includes the following steps:
step S210, encrypting the index value of each transaction in the transaction list according to a preset addition homomorphic random encryption algorithm, and determining a query vector. The index value of the target query transaction is non-zero, and the index values of other transactions are zero. Firstly, according to the sequence of the transactions in the transaction list, setting index values corresponding to the transactions, setting the index value of the transaction to be inquired as a, setting the index values of other transactions as 0, and encrypting the index values respectively through a preset addition homomorphic random encryption algorithm, wherein the random expression of the encryption algorithm is that the result of encrypting the same data is different every time. And finally, according to the transaction sequence in the transaction list, establishing the encrypted index values into a query vector. For example, in the case that n transactions are included in the transaction list, the querying party wants to query the content of the third transaction, and finally constructs a query vector as
Wherein q is0＝q1＝q3＝…＝qn-1＝E(0),q2E (a). And the inquiry party sends the inquiry vector to an inquiry platform, and the inquiry platform generates an inquiry result according to an inquiry response generation algorithm and feeds the inquiry result back to the inquiry party. In one embodiment, setting the index value of the target query transaction to 1 and the index values of the other transactions to 0 can further increase the operation rate.
In step S220, after the query result is obtained, the query result is decrypted by using a decryption algorithm corresponding to the encryption algorithm, so as to obtain the transaction content of the target query transaction. And the query result is obtained by performing an addition homomorphic query response algorithm on the query vector. After the inquiring party obtains the inquiring result, the inquiring result is decrypted according to the addition homomorphic decryption algorithm. In some embodiments, the addition homomorphic encryption and decryption algorithm is a Paillier probabilistic public key encryption algorithm, PailiThe er probability public key encryption system has the addition homomorphism, can encrypt the identifier '0' and '1', has random factors in the Paillier algorithm, and has different encryption results for the same data and different encryption results for '0' and '1' for multiple times in each operation. Taking Paillier probability public key encryption algorithm as an example, according to the homomorphism characteristic of addition, in the plaintext domain
Formula 1 and formula 2 hold true,
D(E(m0)·E(m1))＝m0+m1 equation 1
D(E(m)c) Cm equation 2
Wherein E (x) is an encryption process, D (x) is a decryption process,
it is further found that equations 3 and 4 hold,
D(E(0)x) Equation 3 as 0
D(E(1)x) X equation 4
In one embodiment, the blockchain platform performs modular exponentiation on the query vector and the transaction list to obtain a query result. Tx for transaction information in transaction listnThen, through the query response algorithm, the query result can be represented as
Decrypting the query result includes:
equation 5 illustrates that the client can obtain the content of the target query transaction according to the decryption of the query result. It should be noted that, in the above embodiment, the manner of encrypting the two index values "1" and "0" by using Paillier probabilistic public key encryption algorithm is described, but this embodiment is not limited theretoThe scheme provided by the application is not limited to the Paillier probability public key encryption algorithm, and any random algorithm meeting the homomorphism of addition can be used for realizing the block chain transaction query method; the index value of the target query transaction may also be other non-zero number "a", and accordingly, in the case that the index value of the target query transaction is a, the decryption result of the query result is aTx3And the decryption result can be processed by combining the index value, so that the content of the target query transaction is finally obtained. In the process of inquiring and responding to the inquiry vector and the transaction list, the block chain platform is not limited to the simplest modular exponentiation, and other preset inquiry response algorithms can be adopted, and the inquiry response algorithms need to meet the addition homomorphism characteristic, so that the final inquiry result can obtain the content of the target inquiry transaction after decryption.
Fig. 3 is a schematic diagram of a blockchain transaction query method according to an embodiment of the present invention, in which in a case that a transaction list includes 6 transactions, only the element corresponding to the target query transaction is encrypted with 1, and the others are encrypted with 0 to form a complete query vector
Wherein q is0＝E(0),q1＝ E(0),q2＝E(0),q3＝E(1),q4＝E(0),q5E (0), i.e., the element in the query vector corresponding to the target query transaction is q3And E (1), and the rest elements corresponding to the rest transactions are E (0), the query vector and the transaction list on the blockchain platform are used for generating a query response, so that a platform response result as shown in fig. 3 can be obtained, and the blockchain platform integrates the platform response result into the query result and returns the query result to the query node side.
According to the block chain transaction query method, in the process of constructing the query vector, the index value of the target query transaction is set to be nonzero, the index values of other transactions are set to be zero, encryption is performed through an addition homomorphic random encryption algorithm, the query vector is determined, transaction query is performed on the query platform through the query vector, and the query platform cannot deduce query information in the query vector in time due to limited computing capacity, so that query privacy information cannot be disclosed in the query mode in the embodiment. In addition, the characteristic of the addition homomorphic encryption algorithm enables the contents of the target query transaction to be accurately acquired after the query result is decrypted.
In an embodiment, fig. 4 is a flowchart illustrating operations performed on a query vector and a transaction list of a blockchain platform in a blockchain transaction query method according to an embodiment of the present invention, where as shown in fig. 4, performing modular exponentiation on the query vector and the transaction list to obtain a query result includes:
step S410, dividing each transaction in the transaction list into data pieces according to a preset dividing rule;
step S420, performing modular exponentiation on the first transaction data slice and the first query element to obtain an intermediate value. The first transaction is a transaction in the transaction list, and the first query element is an encrypted index value corresponding to the first transaction in the query vector;
and step S430, multiplying the operation intermediate values of the same column to obtain a result element. Arranging the data pieces of each transaction in sequence, wherein the data pieces with the same ordinal number belong to the same column;
and step S440, constructing a query result according to the result elements.
In the above steps S410 to S440, a MapReduce computing framework is actually introduced, which provides a huge but well-designed parallel computing software framework, can automatically complete parallelization processing of computing tasks, automatically divide computing data and computing tasks, automatically distribute and execute tasks on cluster nodes and collect computing results, and give the complex details of the bottom layers of many systems involved in parallel computing such as data distribution storage, data communication, fault-tolerant processing, and the like to the system for processing. The concepts "Map" and "Reduce" in MapReduce are its main ideas. By means of MapReduce, the query response speed of the block chain platform can be increased, and therefore query processing efficiency is improved. Fig. 5 is a schematic diagram of a MapReduce processing procedure in the blockchain transaction query method according to an embodiment of the present invention, and as shown in fig. 5, first, each transaction in the transaction list is divided into data pieces according to a preset division rule, where the preset division rule may be according to the number of entries of the data or the size of the divided data pieces. In one embodiment, to improve the efficiency of the segmentation and calculation of the transaction data, each transaction in the transaction list is segmented into equal-length data pieces. For example, f1 in FIG. 5 corresponds to the first transaction in the transaction list, f2 corresponds to the second transaction in the transaction list, and so on. Each transaction is divided into pieces of data as shown in the figure. Next, a mapping function (mapper) is to perform a specified operation on each element in the conceptual list composed of some independent elements, in this embodiment, a parallel modular exponentiation calculation is performed on the data slice of the transaction obtained after the splitting in step S410. The reduction operation then refers to performing an appropriate reduction operation on the elements of a list, and in this embodiment, the calculation results of the same column are multiplied to obtain a result element of the query result. And finally, constructing each result element into a query result vector. In the embodiment, the MapReduce programming model is used for query parallel data processing, the processing speed is higher in big data processing, and the query execution efficiency is improved.
According to another aspect of the present invention, there is provided a method for querying blockchain transactions, and fig. 6 is a flowchart of a method for querying blockchain transactions according to another embodiment of the present invention, as shown in fig. 6, the method includes the following steps:
step S610, encrypting index values of all transactions in the transaction list according to a preset addition homomorphic random encryption algorithm, and determining query vectors. The index value of the target query transaction is nonzero, and the index values of other transactions are zero;
step S620, performing modular exponentiation operation on the query vector and the transaction list to obtain a query result;
step S630, the query result is decrypted by using the decryption algorithm corresponding to the addition homomorphic random encryption algorithm, so as to obtain the transaction content of the target query transaction.
The specific limitations of the steps S610 to S630 may refer to the limitations of the blockchain transaction query method in the above embodiments, and are not described herein again. The embodiment provides a block chain transaction query method between a query party node and a block chain platform, in the process of constructing a query vector, an index value of a target query transaction is set to be nonzero, index values of other transactions are set to be zero, encryption is performed through an addition homomorphic random encryption algorithm to determine the query vector, transaction query is performed on the query platform through the query vector, and the query platform party cannot deduce query information in the query vector in time due to limited computing capability, so that query privacy information cannot be disclosed in the query mode in the implementation mode.
According to another aspect of the present invention, there is provided a method for querying blockchain transactions, the method including: the block chain platform side obtains a query vector, wherein the query vector is determined by encrypting the index value of each transaction in the transaction list by the block chain transaction query side according to a preset addition homomorphic random encryption algorithm, the index value of the target query transaction is nonzero, and the index values of other transactions are zero; and the block chain platform performs an addition homomorphic query response algorithm on the query vector to obtain a query result and sends the query result to the block chain transaction query party, and the transaction query party decrypts the query result through a decryption algorithm corresponding to the addition homomorphic random encryption algorithm to obtain the transaction content of the target query transaction. For specific limitations of the blockchain transaction query method in this embodiment, refer to the above limitations on the blockchain transaction query method, which are not described herein again.
In the block chain transaction query method, in the process of constructing the query vector, the index value of the target query transaction is set to be nonzero, the index values of other transactions are set to be zero, and homomorphic random access is performed through additionThe encryption algorithm is used for encrypting to determine a query vector, transaction query is carried out on the query platform through the query vector, and the query platform cannot deduce query information in the query vector in time due to limited computing capacity, so that the query privacy information cannot be disclosed in the query mode in the embodiment. In addition, the characteristic of the addition homomorphic encryption algorithm enables the contents of the target query transaction to be accurately acquired after the query result is decrypted. The embodiments of the present application are described and illustrated below by means of preferred embodiments. Fig. 7 is a schematic diagram illustrating interaction of algorithms in the blockchain transaction query method according to the embodiment of the present invention, and as shown in fig. 7, the scheme assumes that the database server is semi-honest (host-but-curous). The whole process of transaction inquiry comprises an interactive process of 3 algorithms: q is a user query generation algorithm, R is a server query response algorithm, and A is a query result reconstruction algorithm. Initially, based on using a random query generation algorithm Q, the querier constructs a query vector Q based on an index i of the target query transaction that is desired to be obtainediAnd sending the query vector to a server, namely a block chain platform; then, the blockchain platform according to the transaction set Tx and the query vector QiExecuting a response generating algorithm R and responding to the client with a query result RiWherein each transaction in the transaction list, such as Tx, is included in the transaction set1、Tx2And the like. In this process, the server cannot deduce the query vector Q within the polynomial time due to the limited computing power of the server (bound computing power)iAny information of index i. Finally, the client uses RiAnd extracting data elements of the target query transaction by the reconstruction algorithm A.
In the process, the calculation on the block chain platform side is modular exponentiation, so that a MapReduce calculation mode is introduced to accelerate the generation of a query result. And mapping the calculation process of the block chain platform side query processing to the 'Map' and 'Reduce' task phases of the MapReduce calculation mode. The query generation algorithm Q and the query result reconstruction algorithmThe encryption and decryption scheme used in A is a probability encryption scheme with Paillier having an addition homomorphism characteristic. Platform side dataset Txs＝{Tx0…Txj…Txk…TxnIs a set of n transactions, each transaction having l bits. The specific process of the block chain transaction query method comprises the following steps:
if the inquiring party wants to inquire and obtain the transaction Tx stored in the platform partyxX is more than or equal to 1 and less than or equal to n, and the query side node generates a query vector
Wherein q isx＝(1)，
Will vector
Sending the data to a block chain platform, wherein the platform is a Paillier encryption scheme with addition homomorphism, and a querier sends the data to a block chain platform
And transmitting to the block chain platform. In the blockchain platform, let k be the size of the transaction divided into equal-length data pieces (piece), and the size of each transaction data is the same, i.e. each transaction i is divided into l/k data pieces { B }i,1,Bi,2,...Bi,l/kAnd organizing the transactions into the form shown in table 1, and performing modular exponentiation (modulo exponentiation) operation on each data slice and the query vector
The platform multiplies the computed results of the same column to create a query result vector R ═ R (R)1,R2,…Rl/k) Wherein
J is more than or equal to 1 and less than or equal to l/k, the size of each element is k, the total size of the vector R is l bit, and R can be regarded as transaction TxxEncryption of (2). The blockchain platform will return to R to the querier node. Querier node use and generation queryAnd decrypting the R by using a decryption scheme corresponding to the encryption scheme with the same inquiry vector to recover the content of the target inquiry transaction.
TABLE 1
| 1 | 2 | 3 | … | l/k | |
| q1^→Tx1 | B1,1 | B1,2 | B1,3 | … | B1,l/k |
| q2^→Tx2 | B2,1 | B2,2 | B2,3 | … | B2,l/k |
| q3^→Tx3 | B3,1 | B3,2 | B3,3 | … | B3,l/k |
| … | … | … | … | … | … |
| qn^→Txn | Bn,1 | Bn,2 | Bn,3 | … | Bn,l/k |
In the block chain transaction query process, a query party constructs a query vector aiming at a target query transaction desired by the query party and submits the query vector to a block chain platform party, the block chain platform party executes data operation on the query vector and a transaction list, and finally a query result is returned, and the query party decrypts the result through a decryption algorithm to obtain query content. In the query process, the query vector is the encrypted query requirement, the block chain platform cannot deduce the query content, namely the block chain platform performs the disappearing processing, so that the transaction query method for protecting the query privacy of the query party and the query result privacy is realized, the user, namely the query party node, submits the transaction query request to the server terminal, and the query operation is completed under the condition that the user query privacy information is not leaked.
It should be understood that, although the respective steps in the flowcharts in fig. 2 to 6 are sequentially shown as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-6 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, there is also provided a blockchain transaction query system, where the system includes a blockchain platform side and a query side node:
the inquiring party node encrypts the index value of each transaction in the transaction list according to a preset addition homomorphic random encryption algorithm to determine an inquiry vector, wherein the index value of the target inquiry transaction is nonzero, and the index values of other transactions are zero;
the block chain platform performs modular exponentiation operation on the query vector and the transaction list to obtain a query result;
and the inquiring party node decrypts the inquiring result by using a decryption algorithm corresponding to the addition homomorphic random encryption algorithm to obtain the transaction content of the target inquiring transaction.
For the specific definition of the blockchain transaction query system, reference may be made to the above definition of the blockchain transaction query method, which is not described herein again. The various modules in the blockchain transaction query system described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, or can be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, and fig. 8 is a schematic structural diagram of a blockchain transaction query computer device according to an embodiment of the present invention, where the computer device may be a server, and an internal structural diagram thereof may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing block chain transaction query data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a blockchain transaction query system method.
The block chain transaction inquiry computer equipment encrypts the index value of each transaction in the transaction list according to a preset addition homomorphic random encryption algorithm, determines an inquiry vector, sets the index value of a target inquiry transaction to be nonzero, the index values for the other transactions are set to zero and, after the query result is obtained, the decryption algorithm corresponding to the addition homomorphic random encryption algorithm is utilized, the query result is decrypted to obtain the transaction content of the target query transaction, and the block chain platform party cannot deduce the query information in the query vector in time due to limited computing capability, so the query mode in the embodiment does not reveal the query privacy information, and simultaneously, because the privacy of the query vector is strong, the privacy of the query result obtained according to the query vector is also strong, and the query result can not be revealed to the block chain platform party.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the blockchain transaction query method described above.
The block chain transaction query computer-readable storage medium encrypts the index value of each transaction in the transaction list according to a preset addition homomorphic random encryption algorithm, determines a query vector, sets the index value of a target query transaction to be nonzero, the index values for the other transactions are set to zero and, after the query result is obtained, the data is decrypted using a decryption algorithm corresponding to the additive homomorphic random encryption algorithm, the query result is decrypted to obtain the transaction content of the target query transaction, the block chain platform party cannot deduce the query information in the query vector in time due to limited computing capability, therefore, the query method in this embodiment will not reveal the query privacy information, and at the same time, because the privacy of the query vector is strong, the privacy of the query result obtained according to the query vector is also high, and the query result can not be revealed to the block chain platform party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (11)
1. A method for querying blockchain transactions, the method comprising:
encrypting the index value of each transaction in the transaction list according to a preset addition homomorphic random encryption algorithm to determine a query vector, wherein the index value of the target query transaction is nonzero, and the index values of other transactions are zero;
after the query result is obtained, the query result is decrypted by using a decryption algorithm corresponding to the addition homomorphic random encryption algorithm to obtain the transaction content of the target query transaction, wherein the query result is obtained by performing an addition homomorphic query response algorithm on the query vector.
2. The method of claim 1, further comprising: setting the index value of the target query transaction to 1, and setting the index values of the other transactions to 0.
3. The method of claim 2, wherein the additive homomorphic random encryption algorithm is a Paillier probabilistic public key encryption algorithm.
4. The method of claim 2, wherein the query result is obtained by performing a modular exponentiation of the query vector with a transaction list.
5. A method for querying blockchain transactions, the method comprising:
encrypting the index value of each transaction in the transaction list according to a preset addition homomorphic random encryption algorithm to determine a query vector, wherein the index value of the target query transaction is nonzero, and the index values of other transactions are zero;
performing modular exponentiation operation on the query vector and the transaction list to obtain a query result;
and decrypting the query result by using a decryption algorithm corresponding to the addition homomorphic random encryption algorithm to obtain the transaction content of the target query transaction.
6. The method of claim 5, wherein performing a modular exponentiation operation on the query vector and a transaction list to obtain the query result comprises:
dividing each transaction in the transaction list into data pieces according to a preset dividing rule;
performing modular exponentiation on the data slice of a first transaction and a first query element to obtain an operation intermediate value, wherein the first transaction is a transaction in the transaction list, and the first query element is the encrypted index value corresponding to the first transaction in the query vector;
multiplying the operation intermediate values in the same column to obtain a result element, wherein the data pieces of each transaction are arranged in sequence, and the data pieces with the same ordinal number belong to the same column;
and constructing the query result according to the result element.
7. The method of claim 6, wherein dividing each transaction in the transaction list into data pieces according to a preset dividing rule comprises:
and dividing each transaction in the transaction list into equal-length data pieces.
8. A method for querying blockchain transactions, the method comprising:
acquiring a query vector, wherein the query vector is determined by encrypting the index value of each transaction in a transaction list according to a preset addition homomorphic random encryption algorithm, the index value of a target query transaction is nonzero, and the index values of other transactions are zero;
and performing an addition homomorphic query response algorithm on the query vector to obtain a query result and sending the query result, wherein the query result is decrypted through a decryption algorithm corresponding to the addition homomorphic random encryption algorithm to obtain the transaction content of the target query transaction.
9. A blockchain transaction query system, the system comprising a blockchain platform side and a query side node:
the inquiring party node encrypts index values of all transactions in the transaction list according to a preset addition homomorphic random encryption algorithm to determine an inquiry vector, wherein the index value of a target inquiry transaction is nonzero, and the index values of other transactions are zero;
the block chain platform performs modular exponentiation operation on the query vector and the transaction list to obtain a query result;
and the inquiring party node decrypts the inquiring result by using a decryption algorithm corresponding to the addition homomorphic random encryption algorithm to obtain the transaction content of the target inquiring transaction.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 8 are implemented by the processor when executing the computer program.
11. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 8.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112966022A (en) * | 2021-03-10 | 2021-06-15 | 安徽航天信息科技有限公司 | Information query method, device and system for data transaction platform |
| CN113158247A (en) * | 2021-04-27 | 2021-07-23 | 同盾控股有限公司 | User query method and device, storage medium and electronic equipment |
| WO2022048077A1 (en) * | 2020-09-02 | 2022-03-10 | 杭州趣链科技有限公司 | Blockchain transaction query method and system, and computer device and storage medium |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115269938B (en) * | 2022-09-22 | 2023-02-14 | 深圳市洞见智慧科技有限公司 | Homomorphic encryption-based keyword track hiding query method, system and related device |
| CN115965388B (en) * | 2022-12-30 | 2023-12-22 | 国网数字科技控股有限公司 | Block chain-based industrial chain financial secret state tracing method, device and related equipment |
| CN116541582B (en) * | 2023-07-06 | 2023-09-01 | 隐数科技(杭州)有限责任公司 | Method and device for quickly inquiring safety data based on homomorphic encryption |
| CN116628285B (en) * | 2023-07-21 | 2023-11-03 | 北京邮电大学 | Block chain transaction data query method and device |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103345526A (en) * | 2013-07-22 | 2013-10-09 | 武汉大学 | Efficient privacy protection encrypted message querying method in cloud environment |
| CN105468986A (en) * | 2015-12-02 | 2016-04-06 | 深圳大学 | Confidential information retrieval method and system |
| CN106549749A (en) * | 2016-12-06 | 2017-03-29 | 杭州趣链科技有限公司 | A kind of block chain method for secret protection encrypted based on additive homomorphism |
| CN107273556A (en) * | 2017-08-23 | 2017-10-20 | 上海点融信息科技有限责任公司 | Block chain data index method and equipment |
| CN107454070A (en) * | 2017-07-24 | 2017-12-08 | 华东师范大学 | A kind of personalized privacy information search method based on block chain |
| CN107547525A (en) * | 2017-08-14 | 2018-01-05 | 复旦大学 | A kind of method for secret protection of big data query processing |
| CN109189782A (en) * | 2018-08-02 | 2019-01-11 | 哈尔滨工程大学 | A kind of indexing means in block chain commodity transaction inquiry |
| CN109670331A (en) * | 2019-02-21 | 2019-04-23 | 哈尔滨工程大学 | It is a kind of that encryption method symmetrically can search for based on block chain |
| US10289816B1 (en) * | 2018-06-08 | 2019-05-14 | Gsfm Llc | Methods, systems, and devices for an encrypted and obfuscated algorithm in a computing environment |
| CN110135986A (en) * | 2019-04-23 | 2019-08-16 | 武汉大学 | It is a kind of to can search for encrypted file data method based on what block chain intelligence contract was realized |
| US20190280855A1 (en) * | 2016-11-24 | 2019-09-12 | Alibaba Group Holding Limited | Method, system and apparatus for data storage and data access |
| CN110378143A (en) * | 2019-04-26 | 2019-10-25 | 杭州数焓科技有限公司 | A kind of EHR based on block chain can search for encryption method |
| CN110837491A (en) * | 2019-11-25 | 2020-02-25 | 广州知弘科技有限公司 | Block chain financial big data processing system and method |
| CN111008228A (en) * | 2020-03-09 | 2020-04-14 | 支付宝(杭州)信息技术有限公司 | Method and device for inquiring account privacy information in block chain |
| CN111339106A (en) * | 2020-05-18 | 2020-06-26 | 杭州趣链科技有限公司 | Block chain data indexing method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190258991A1 (en) * | 2018-02-22 | 2019-08-22 | Idlogiq Inc. | System and methods for querying the distribution path of product units within a supply chain |
| CN112087439B (en) * | 2020-09-02 | 2022-05-17 | 杭州趣链科技有限公司 | Block chain transaction query method, system, computer device and storage medium |
-
2020
- 2020-09-02 CN CN202010907873.6A patent/CN112087439B/en active Active
- 2020-12-28 WO PCT/CN2020/139869 patent/WO2022048077A1/en active Application Filing
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103345526A (en) * | 2013-07-22 | 2013-10-09 | 武汉大学 | Efficient privacy protection encrypted message querying method in cloud environment |
| CN105468986A (en) * | 2015-12-02 | 2016-04-06 | 深圳大学 | Confidential information retrieval method and system |
| US20190280855A1 (en) * | 2016-11-24 | 2019-09-12 | Alibaba Group Holding Limited | Method, system and apparatus for data storage and data access |
| CN106549749A (en) * | 2016-12-06 | 2017-03-29 | 杭州趣链科技有限公司 | A kind of block chain method for secret protection encrypted based on additive homomorphism |
| CN107454070A (en) * | 2017-07-24 | 2017-12-08 | 华东师范大学 | A kind of personalized privacy information search method based on block chain |
| CN107547525A (en) * | 2017-08-14 | 2018-01-05 | 复旦大学 | A kind of method for secret protection of big data query processing |
| CN107273556A (en) * | 2017-08-23 | 2017-10-20 | 上海点融信息科技有限责任公司 | Block chain data index method and equipment |
| US10289816B1 (en) * | 2018-06-08 | 2019-05-14 | Gsfm Llc | Methods, systems, and devices for an encrypted and obfuscated algorithm in a computing environment |
| CN109189782A (en) * | 2018-08-02 | 2019-01-11 | 哈尔滨工程大学 | A kind of indexing means in block chain commodity transaction inquiry |
| CN109670331A (en) * | 2019-02-21 | 2019-04-23 | 哈尔滨工程大学 | It is a kind of that encryption method symmetrically can search for based on block chain |
| CN110135986A (en) * | 2019-04-23 | 2019-08-16 | 武汉大学 | It is a kind of to can search for encrypted file data method based on what block chain intelligence contract was realized |
| CN110378143A (en) * | 2019-04-26 | 2019-10-25 | 杭州数焓科技有限公司 | A kind of EHR based on block chain can search for encryption method |
| CN110837491A (en) * | 2019-11-25 | 2020-02-25 | 广州知弘科技有限公司 | Block chain financial big data processing system and method |
| CN111008228A (en) * | 2020-03-09 | 2020-04-14 | 支付宝(杭州)信息技术有限公司 | Method and device for inquiring account privacy information in block chain |
| CN111339106A (en) * | 2020-05-18 | 2020-06-26 | 杭州趣链科技有限公司 | Block chain data indexing method |
Non-Patent Citations (2)
| Title |
|---|
| SHLOMI LINOY: "Scalable Privacy-Preserving Query Processing Over Ethereum Blockchain", 《2019 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN)》 * |
| 刘培鹤: "应用于物联网的Paillier同态信息检索方案设计", 《北京电子科技学院学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022048077A1 (en) * | 2020-09-02 | 2022-03-10 | 杭州趣链科技有限公司 | Blockchain transaction query method and system, and computer device and storage medium |
| CN112966022A (en) * | 2021-03-10 | 2021-06-15 | 安徽航天信息科技有限公司 | Information query method, device and system for data transaction platform |
| CN112966022B (en) * | 2021-03-10 | 2024-04-05 | 安徽航天信息科技有限公司 | Information query method, device and system of data transaction platform |
| CN113158247A (en) * | 2021-04-27 | 2021-07-23 | 同盾控股有限公司 | User query method and device, storage medium and electronic equipment |
| CN113158247B (en) * | 2021-04-27 | 2023-03-14 | 同盾控股有限公司 | User query method and device, storage medium and electronic equipment |
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|---|---|
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