CN116821952A - Privacy data calculation traceability system and method based on block chain consensus mechanism - Google Patents
Privacy data calculation traceability system and method based on block chain consensus mechanism Download PDFInfo
- Publication number
- CN116821952A CN116821952A CN202310603310.1A CN202310603310A CN116821952A CN 116821952 A CN116821952 A CN 116821952A CN 202310603310 A CN202310603310 A CN 202310603310A CN 116821952 A CN116821952 A CN 116821952A
- Authority
- CN
- China
- Prior art keywords
- data
- privacy
- calculation
- node
- provider
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004364 calculation method Methods 0.000 title claims abstract description 187
- 230000007246 mechanism Effects 0.000 title claims abstract description 70
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 46
- 230000006870 function Effects 0.000 claims abstract description 6
- 230000006399 behavior Effects 0.000 claims description 34
- 238000011156 evaluation Methods 0.000 claims description 20
- 230000008569 process Effects 0.000 claims description 16
- 238000004891 communication Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 9
- 239000002184 metal Substances 0.000 claims description 9
- 229910052751 metal Inorganic materials 0.000 claims description 9
- 238000013467 fragmentation Methods 0.000 claims description 7
- 238000006062 fragmentation reaction Methods 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 4
- 238000013210 evaluation model Methods 0.000 claims description 4
- 238000013459 approach Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 10
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 239000003795 chemical substances by application Substances 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 230000008520 organization Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000000586 desensitisation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 238000005304 joining Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000007306 turnover Effects 0.000 description 1
- 238000004804 winding Methods 0.000 description 1
Landscapes
- Storage Device Security (AREA)
Abstract
The invention relates to a privacy data calculation traceability system and method based on a block chain consensus mechanism. The invention mainly completes the functions of calculating, protecting, tracing the behavior of the privacy data in the blockchain, and the like, and a user can realize the functions of calculating, protecting the privacy and tracing the privacy data by calling the data and the algorithm through the system.
Description
Technical Field
The invention relates to a system and a method for calculating and tracing privacy data based on a block chain consensus mechanism, in particular to a system and a method for credible calculation and traceability of user privacy data, belonging to the field of credible sharing and tracing of digital information.
Background
In recent years, blockchain technology has received attention from researchers in the field of digital information. In the traditional digital information exchange mode, a central data repository is used by a data management organization to support transaction flows and computations, the owners of the databases hold full control rights and can manage access rights and update rights of the databases, which limits the transparency and scalability of the data assets, making it difficult for external access parties to ensure that the data records have not been tampered with. Due to the limitation of network conditions and technology, the distributed database has the problems of poor feasibility, difficult data synchronization, difficult common knowledge problem and the like in the traditional data exchange mode, however, with the development and progress of data transmission and encryption technology, the characteristics of block chain decentralization, openness, independence, security and anonymity make the distributed database have unique advantages, so that the distributed trusted data sharing based on the bottom layer of the block chain is gradually possible.
The consensus mechanism refers to a process of achieving a unified agreement on the state of the network in a decentralization manner. The consensus mechanism helps verify and verify that information is added to the classified book, ensuring that only real transactions are recorded on the blockchain. However, the existing consensus mechanism has lower safety and larger resource consumption, and it is important to design a new traceable consensus mechanism, so that the data sharing and data traceability of the blockchain are realized by improving the consensus mechanism, namely the traceable consensus mechanism.
The blockchain tracing refers to the whole-course traceability from information acquisition record of a source, raw material source traceability, production process, processing link, storage information, inspection batch and logistics turnover to third party quality inspection, customs entry and exit and anti-counterfeiting authentication of the commodity by combining the unique and tamper-proof distributed account book recording characteristic with the technology such as the Internet of things by utilizing the blockchain technology. However, the tracing of the privacy data is still imperfect at present, and therefore, a privacy data calculation traceable system based on a blockchain consensus mechanism is designed to perfect the protection and data tracing of the privacy data of the blockchain.
The block chain realizes functions of data such as non-falsification, trace-back tracing and the like by using technical means such as time stamping, a consensus mechanism and the like, and provides technical support for the establishment of a cross-mechanism tracing system. Meanwhile, a third-party supervision organization and a consumer are brought into a supervision system, so that the information island is broken, information support is provided, and the transparency of the production flow is realized to a certain extent. However, at present, privacy data needs to be protected but also needs to be transparent, so that the relationship between the privacy data and the transparency data is particularly important. Therefore, the privacy data calculation module based on the intelligent contract is designed, and when privacy data is calculated, the transparent production flow is realized to a certain extent, and the privacy data is also protected.
Currently, in the application of blockchain technology, privacy protection of data has been very important. The conventional methods for protecting user data privacy are: (1) after desensitizing the data, winding up; (2) encrypting the data and then chaining. Both methods have certain problems: the use of desensitization techniques may result in incomplete data. Encryption techniques decrypt the data after it is acquired, which can greatly impact analysis efficiency if the encrypted data on the blockchain is processed with large data techniques.
Disclosure of Invention
The invention solves the technical problems: overcomes the defects of the prior art, provides a privacy data calculation traceability system and a privacy data calculation traceability method based on a block chain consensus mechanism, not only improves the efficiency, but also realizes the data calculation, the privacy data protection and the data traceability of the block chain,
the invention adopts the following technical scheme:
in a first aspect, the present invention provides a system for calculating traceability of private data based on a blockchain consensus mechanism, comprising: the system comprises a data calling module based on a blockchain, a privacy data calculating module based on an intelligent contract and a privacy calculation reliability traceability module based on a consensus mechanism:
a data calling module based on block chain: the block chain of the data requiring party is utilized to have a data calling function, and the unpublished privacy data of the data provider is called through any one of two methods, wherein one method is to call the unpublished privacy data provided by the data provider through a data interface of the data provider by utilizing a calling chain code of the block chain; another approach is for the blockchain to provide the private data that is not disclosed by the URL call data provider; the method comprises the steps that firstly, a blockchain records an acquisition mode of the undisclosed privacy data in an intelligent contract to ensure the safety, credibility and privacy of the data, and then the undisclosed privacy data is transmitted to a privacy data calculation module based on the intelligent contract;
Privacy data calculation module based on intelligent contract: the accounting person is selected by adopting a traceable consensus mechanism algorithm, and is used for the recording process, and the accounting person selected by the traceable consensus mechanism algorithm can trace and record the behavior of the accounting person, so that the record credibility of the accounting person is ensured; selecting a local node of a data provider or randomly allocating an idle anonymous computing node after selecting a billing person, and evaluating the credit of the node by using a cPoW credit model; in the nodes with qualified credit, the calculation of the unpublished privacy data of the data provider is completed through a TCCM consensus algorithm based on a threshold password scheme; after the calculation is completed, the calculation result of the private data which is not disclosed is encrypted by utilizing a data sharing and data tracing scheme, so that the calculation result of the private data which is not disclosed is not revealed and the reliability is traced; the method comprises the steps that a demand party calls an intelligent contract to obtain a calculation result of private data which is not disclosed, and the calculation result of the private data which is not disclosed is transmitted to a privacy calculation credibility traceability module based on a consensus mechanism;
privacy calculation credibility traceability module based on consensus mechanism: according to the calculation result of the private data which is not disclosed by the private data calculation module based on the intelligent contract, a billing person is found, and the data records of the data provider and the data demander are obtained through the billing person; the privacy calculation reliability tracing module based on the consensus mechanism receives data records of the data demand side and the data provider as data evidence, and performs behavior tracing of the data provider and the data demand side through the data evidence; and judging whether the data demand party is credible or not by utilizing the result obtained by tracing the behavior of the data demand party, judging whether the undisclosed privacy data calculation result provided by the data provider is credible or not by utilizing the result obtained by tracing the behavior of the data provider, and finally realizing tracing of the privacy data calculation credibility.
In the privacy data calculation module based on the intelligent contract, the cPoW credit model evaluation consists of a node credit model and a fragmentation rotation model, and the node credit model and the fragmentation rotation model simultaneously carry out credit evaluation on the node;
(1) The node credit model is used for evaluating the performance of each index of the node, in order to make the influence degree of the evaluation indexes of the credit equal, firstly, the attribute is uniformly quantized, a three-layer neural network credit evaluation model is designed, the input layer node is a secondary index x for credit evaluation, the hidden layer node is a primary index y, the output layer node z range is [0,1], the weight from the input layer to the hidden layer is v, and the weight from the hidden layer to the output layer is w;
(2) The time slicing rotation model is designed for making the influence degree of the evaluation index of the reliability fair, and the lucky degree of the nodes is evaluated by selecting different nodes according to different time.
Further, in the privacy data calculation module based on the intelligent contract, a TCCM consensus algorithm based on a threshold cryptographic scheme is implemented as follows:
the TCCM consensus algorithm based on the threshold password scheme, namely the consensus protocol of the threshold password scheme, adopts a guard metal model based on a threshold group signature theory, and before calculating privacy data, a data provider has a node ID, and pays a guard metal to a guard metal manager set through the node ID to prevent a Bayesian behavior; after the deposit is paid, the data provider signs a signature through the node ID and attaches the signature to the end of the deposit; the node ID completes the calculation of the privacy data; after the calculation is completed, the deposit manager sets to sign the node ID for paying deposit, returns deposit to the node ID, and the total time of the process is the time for encryption operation and the time after signing, which consumes little resource.
Further, in the privacy data calculation module based on the intelligent contract, the data sharing and data tracing scheme encrypts the calculation result of the private data by adopting a block chain data calculation method with a double-chain structure, wherein the double chain comprises two different types of block chains, namely a data block chain and a transaction block chain, and the data block chain encrypts and stores the private data which is not disclosed, so that the private data which is not disclosed is ensured to be safely stored and not accessed by other nodes; the transaction blockchain stores the calculation result of the private data which is not disclosed in an encryption way and then extracts the calculation result in a transaction link.
Further, the privacy calculation reliability tracing module based on the consensus mechanism specifically realizes the following method:
(1) According to the calculation result of the private data which is not disclosed by the private data calculation module based on the intelligent contract, a billing person is found, and the data records of the data provider and the data demander are obtained through the billing person;
(2) The privacy calculation reliability tracing module based on the consensus mechanism receives data records of a data demand side and a data provider as data evidence, wherein the data records comprise data parameters Args which are provided by the data demand side and are transmitted by the service at the time, a privacy calculation result Res which is provided by the data provider, a service record number LogID, service application time LogTime, data demand side ID, data provider ID and the like, the transmitted data parameters Args, the privacy calculation result Res and the service record number LogID are necessary parameters, and the record Log of the service is extracted by inquiring the LogID on a blockchain;
(3) The behavior of the data demand party is traced, the service content of the time is traced through records obtained in the proving stage of the two parties, the HashArgs of the hash value after the encryption of the private data is calculated by using an md5 algorithm according to the input data parameter Args provided by the data demand party, the HashArgs of the hash value is compared with the hash value of the input data parameter recorded in the record Log, if the hash value is the same, the credibility of the input data of the data demand party is proved, otherwise, the credibility of the input data of the data demand party is confirmed;
(4) The data provider acts in a trace, takes the input data parameter Args as input, and transmits the input data parameter Args to the data provider again, and calculates the type SerType according to the privacy provided by the service in the Log.
a. If sertype=url, confirming the mode of privacy calculation, registering URL of the available privacy calculation service API in the system by the data provider, sending the input data parameter Args as input to the URL address registered by the privacy calculation reliability tracing module based on the consensus mechanism, and obtaining the calculation result ReRes of the data provider;
b. if sertype=anonymous, confirming that the privacy calculation mode is that a privacy calculation reliability traceability module based on a consensus mechanism refers to an Anonymous calculation node for calculation, taking an incoming data parameter Args as input, submitting a privacy calculation algorithm to a designated calculation node for recalculation, and obtaining a calculation result ReRes;
Calculating a hash value HashReRes of the privacy data after encryption by an md5 algorithm, equally calculating a hash value HashRes of the privacy calculation result Res provided by the data provider, and comparing the hash value HashRes, the hash value HashReRes of the privacy data after encryption and a result hash value LogHashRes in a record Log:
a. if the three are the same, proving that the result provided by the data provider is credible;
b. if the HashRes is inconsistent with the HashReRes, proving that an algorithm provided by a data provider is unstable and causes errors;
c. if the HashRes is inconsistent with the LogHasRes, proving that the privacy calculation result provided by the data provider is wrong;
d. if the HashReRes is inconsistent with the LogHashRes, proving that the data provider provides a privacy calculation algorithm with errors;
and returning the service tracing and responsibility determining result FinalRes according to the comparison result, thereby realizing the tracing of the credibility of the privacy data.
In a second aspect, the invention provides a method for calculating traceability of privacy data based on a blockchain consensus mechanism, which comprises the following specific implementation steps:
(1) The block chain of the data demand party is used for calling the private data which is not disclosed by the data provider and carrying out preparation work before the private data calculation;
(2) Before calculation starts, firstly, a traceable consensus mechanism algorithm is adopted to select a billing person, the billing person is used for a recording process, and the billing person selected by the traceable consensus mechanism algorithm can trace and record the behavior of the billing person, so that the record credibility of the billing person is ensured. After selecting the billing agent, the local node of the data provider is selected or a free anonymous computing node is randomly assigned and the node's credit is assessed using the cPoW credit model. If the credit of the node is qualified, transmitting the private data which is not disclosed to the node for calculation;
(3) Starting calculation, firstly completing calculation of unpublished privacy data of a data provider through a TCCM consensus algorithm based on a threshold password scheme, and completing calculation;
(4) Encrypting the calculation result of the private data which is not disclosed in the step (3) by utilizing a data sharing and data tracing scheme, so as to ensure that the calculation result of the private data which is not disclosed is not revealed and traced in credibility;
(5) The data demand party obtains the calculation result of the private data which is not disclosed, and the calculation result of the private data which is not disclosed is obtained by calling the intelligent contract. In order to ensure the credibility of the calculation result of the private data which is not disclosed, the behavior of the data requiring party and the data providing party need to be traced;
(6) Firstly, finding a billing person through a calculation result of privacy data, and acquiring data records of a data provider and a data demander through the billing person; both sides prove that respective data records are provided as data evidences, and the behavior of the data provider and the data demander is traced through the data evidences; and judging whether the data demand party is credible or not by utilizing the result obtained by tracing the behavior of the data demand party, judging whether the undisclosed privacy data calculation result provided by the data provider is credible or not by utilizing the result obtained by tracing the behavior of the data provider, and finally realizing tracing of the privacy data calculation credibility.
In a third aspect, the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus;
a memory for storing a computer program;
and the processor is used for executing the computer program stored in the memory, and realizing a system and a method for calculating and traceability of the privacy data based on the block chain consensus mechanism during execution.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which when processed and executed implements a system and method for traceability of privacy data computation based on a blockchain consensus mechanism.
Compared with the prior art, the invention has the advantages that:
(1) In an actual blockchain system, in order to pursue reasonable fairness of a consensus mechanism, the existing consensus mechanism is quite complex, the existing blockchain consensus mechanism is low in safety and high in resource consumption, and it is particularly important to design a new consensus mechanism.
(2) In practical blockchain systems, many people still maintain the traditional concept of anonymity, which is considered to mean unsafe, while the blockchain technology has some defects, and when a large number of on-chain analysis tools are generated, the privacy characteristics of the blockchain are destroyed. The user remains largely suspicious of private data. The calculation process, encryption algorithm and chain code calling traceability of the privacy data adopted by the invention can completely ensure that the privacy of the data is not revealed in the whole process, and simultaneously has good data sharing property and traceability, thereby ensuring the transparency of the blockchain.
Drawings
FIG. 1 is a block chain consensus architecture of the present invention for a system for calculating traceability of private data;
FIG. 2 is a diagram of a smart contract-based privacy data calculation module in accordance with the present invention;
FIG. 3 is a block diagram of a data reliability trace back module based on a consensus mechanism in the present invention;
fig. 4 is a flowchart of a method for calculating traceability of privacy data based on a blockchain consensus mechanism according to the present invention.
Detailed Description
Specific embodiments of the system of the present invention are further described below with reference to the accompanying drawings.
As shown in fig. 1, the privacy data computing traceability system based on the blockchain consensus mechanism comprises a data calling module based on a blockchain, a privacy data computing module based on an intelligent contract and a data credibility traceability module based on the consensus mechanism.
The implementation process of each module is as follows:
1. block chain based data calling module embodiments:
(1) The calling chain code of the blockchain calls the privacy data provided by the data provider in two modes:
a. the data provider provides an interface to read private data, awaiting a blockchain response. The block chain reads and identifies the data provided by the data provider through the interface of the privacy data provided by the data provider, if the privacy data provided by the user meets the regulations, the uploading is successful, the next step is executed, otherwise, the uploading fails, and the data provider uploads the privacy data again;
b. The block chain allocates a specific privacy uniform resource locator for the user, and the data provider uploads the privacy data to the privacy data calculation traceable system based on the block chain consensus mechanism through accessing the special privacy uniform resource locator allocated by the block chain. The block link receives the privacy data uploaded by the user, reads and identifies whether the privacy data uploaded by the user meets the regulations, if so, the block link successfully reads and executes the next step, otherwise, the block link fails to upload, and the user uploads again.
(2) The block chain reads the privacy data uploaded by the identification data provider, and firstly, the privacy data is stored in a storage module of the block chain, so that the safety, the credibility and the privacy of the privacy data are ensured. The privacy data is then passed to a smart contract-based privacy data calculation module.
2. As shown in fig. 2: the privacy data calculation module based on the intelligent contract is specifically realized as follows: based on a consensus mechanism CPoW module (node credit model and fragment rotation model), a guarantee gold module of a TCCM threshold group signature theory, and a data sharing traceable consensus module:
(1) The traceable consensus mechanism selects the billing person, the billing person is used for recording the process, and the billing person selected by the traceable consensus mechanism algorithm can trace and record the behavior of the billing person. After the billing person is selected, a local node of the data provider is selected or an idle anonymous computing node is randomly allocated, so that the computation of the unpublished privacy data of the data provider is completed. To ensure that the node honest calculates the private data, the blockchain needs to evaluate the node's credits. Specifically, the blockchain system employs a cPoW credit model: the consensus algorithm consists of a node credit model and a fragmentation rotation model, and the credit degree of the node is estimated on the basis of the two models;
a. The node credit model is used for evaluating the performance of each index of the node, and in order to make the influence degree of the evaluation indexes of the credit equal, the attribute is firstly quantized uniformly. Designing a three-layer neural network credit evaluation model, wherein an input layer node is a secondary index x of credit evaluation, and the secondary index x comprises indexes of 3 aspects: 1. the financial capability of a node account includes two nodes: x1: coinage, x2: token flow ratio, 2, performance of nodes includes 4 nodes: x3: network delay duration, x4: offline times of node, x5: offline duration of node, x6: the number of times of searching space by the node, 3, the integrity level of the node: x7: node joining network duration, x8: number of times the node provides the bifurcation area x9: whether the node provides invalid blocks, x10: the credits of a round on a node. These three aspects have three different weights v. And then, network training is carried out to obtain a second round of hidden layer nodes serving as first-level indexes y, and three different weights w are arranged from y to an output layer. The range of the output layer node z is 0,1, so that the influence degree of the evaluation index of the utilization degree is the same after the unified quantization of the attribute is finished;
b. the time slicing rotation model is designed for making the influence degree of the evaluation index of the reliability fair, and different nodes are selected according to different time to further evaluate the reliability of the nodes. When a user submits a request, the node represented by the current time is selected to evaluate the reliability of the node, so that the randomness of the node is ensured, and the influence degree fairness of the evaluation index of the reliability is ensured.
(2) In order to prevent the cheating, off-line and other campaigns of the participating nodes, and further improve the throughput of the system and reduce the consumption of resources, a consensus protocol of a TCCM (text-based code division multiple access) based on the threshold password scheme, namely, a consensus protocol of the threshold password scheme is provided. The protocol is a gold model based on threshold group signature theory. The gold-guaranteed model based on the threshold group signature theory is used for preventing the Bayesian behavior of the participated nodes, the gold-guaranteed can be used for preventing the Bayesian behavior by mortgage, and the safety problem of the gold-guaranteed is guaranteed by adopting a signature technology.
The principal party has a payment principal node ID. The special transaction is carried out by two processes of paying the deposit and returning the deposit, the node ID pays the deposit to the deposit management set, each deposit paying node needs to sign a public key of the common transaction and deposit manager set with a random signature, the signature is attached to the end of the paid deposit, and the deposit is submitted to the manager. When the node ID dishonest completes the blockchain formula, the deposit manager gathers to sign the special transaction for paying the deposit and the node ID for returning the deposit, and returns the deposit to the ID. The total time of this process is the time to perform the encryption operation and the time to sign, consuming very little resources.
(3) Data-specific and data-traceback schemes: in order to ensure the integrity of data in the sharing process, the traceability of data sources and the privacy of users, and also to prevent the data from being stolen, a data sharing and data traceability scheme is provided on the basis of a blockchain technology and a threshold password signature technology, which is a blockchain data calculation scheme with a double-chain structure. The double chain mainly comprises two types of blockchains, namely a data blockchain and a transaction blockchain, and is used for encrypting and storing original data and encrypting and storing useful transaction information and then extracting a transaction link.
The data sharing and data tracing scheme adopts a Bayesian protocol between double-chain structures, the main characteristics of the Bayesian protocol are decentralization and arbitrary behavior fault tolerance, and by a distributed method, legal numbers or enough groups of nodes can reach consensus, and each node can decide trusted objects to complete the consensus without depending on the same participants. And a common protocol of a threshold password scheme is adopted among each type of single-chain blocks, so that the safety of data and the authenticity of information can be ensured.
In the case of a token transaction and a data transaction, a consensus protocol of a threshold cryptographic scheme is used when conducting the token transaction, and the data transaction nodes therein are recorded by node marks and are encrypted into blocks by an encryption algorithm. After the transaction blockchain completes data transaction, data transaction records appear on the data blockchain, nodes of the data blockchain record original data, the original data are divided into n data blocks and encrypted, and ciphertext sets are stored in the golden data blockchain through a threshold password scheme algorithm, so that the data checking speed of a user is increased, and meanwhile, the data are prevented from being resale.
(4) After the calculation is completed, the data demand side calls the intelligent contract to acquire a calculation result. And the result obtained by the privacy data calculation module based on the intelligent contract is continuously transmitted to the privacy calculation reliability traceability module based on the consensus mechanism.
3. The data reliability tracing module based on the consensus mechanism, as shown in fig. 3, is specifically implemented as follows:
(1) And according to the calculation result of the private data which is not disclosed by the private data calculation module based on the intelligent contract, a billing person is found, and the data records of the data provider and the data demander are acquired through the billing person.
(2) The privacy calculation reliability tracing module based on the consensus mechanism receives data records of a data demand side and a data provider as data evidence, wherein the data records comprise data parameters Args provided by the data demand side at the time, privacy calculation results Res provided by the data provider, service record numbers LogID, service application time LogTime, data demand side ID, data provider ID and the like, the data records Args, the privacy calculation results Res and the service record numbers LogID are necessary parameters, and the system inquires on a block chain through the LogID to extract system records Log of the service.
(3) The privacy calculation demand side behavior is traced, the system traces the service content through the system records acquired in the two side proving stages, the encrypted hash value HashArgs is calculated by utilizing an md5 algorithm according to the incoming data parameter Args provided by the data demand side, the hash value HashArgs is compared with the incoming parameter hash value recorded in the system records Log, if the hash value HashArgs is the same, the incoming data of the data demand side is proved to be credible, otherwise, the incoming data of the data demand side is confirmed to be unreliable.
(4) And the privacy calculation provider acts retrospectively, takes the input data parameter Args as input, and transmits the input data parameter Args to the data provider again, and the type SerType is calculated according to the privacy provided by the service in the Log.
a. If sertype=url, confirming the mode of privacy calculation this time, registering URL of available privacy calculation service API in the system for the data provider, sending the input data parameter Args as input to URL address registered in the system, and obtaining calculation result ReRes of the data provider.
b. If sertype=anonymous, confirming that the privacy calculation mode is that the system nominates Anonymous calculation nodes to calculate, taking the input data parameter Args as input, and delivering the privacy calculation algorithm to the designated calculation nodes to recalculate, so as to obtain a calculation result ReRes.
And calculating the hash value HashRes of the encrypted calculation result through an md5 algorithm, calculating the hash value HashRes of the privacy calculation result Res provided by the data provider, and comparing the hash value HashRes, hashReRes with the result hash value LogHashRes in the Log of the system.
a. If the three are the same, proving that the result provided by the data provider is credible;
b. if the HashRes is inconsistent with the HashReRes, proving that an algorithm provided by a data provider is unstable and causes errors;
c. if the HashRes is inconsistent with the LogHasRes, proving that the privacy calculation result provided by the data provider is wrong;
d. if HashReRes is inconsistent with LogHashRes, the data provider proves to be providing a privacy calculation algorithm.
The system returns the retrospective responsibility result FinalRes of the service according to the result.
As shown in fig. 4, a method for calculating traceability of privacy data based on a blockchain consensus mechanism is implemented as follows:
(1) The block chain of the data demand party is utilized to have a data calling function to call the private data which is not disclosed by the data provider, firstly, the block chain records the acquisition mode of the private data which is not disclosed in the intelligent contract, the safety, the credibility and the privacy of the data are ensured, and the preparation work before the calculation of the private data is carried out;
(2) Before calculation starts, firstly, a traceable consensus mechanism algorithm is adopted to select a billing person, the billing person is used for a recording process, and the billing person selected by the traceable consensus mechanism algorithm can trace and record the behavior of the billing person, so that the record credibility of the billing person is ensured. After selecting the billing agent, the local node of the data provider is selected or a free anonymous computing node is randomly assigned and the node's credit is assessed using the cPoW credit model. If the credit of the node is qualified, transmitting the private data which is not disclosed to the node for calculation;
(3) Starting calculation, firstly completing calculation of unpublished privacy data of a data provider through a TCCM consensus algorithm based on a threshold password scheme, and completing calculation;
(4) Encrypting the calculation result of the private data which is not disclosed in the step (3) by utilizing a data sharing and data tracing scheme, so as to ensure that the calculation result of the private data which is not disclosed is not revealed and traced in credibility;
(5) The data demand party obtains the calculation result of the private data which is not disclosed, and the calculation result of the private data which is not disclosed is obtained by calling the intelligent contract. In order to ensure the credibility of the calculation result of the private data which is not disclosed, the behavior of the data requiring party and the data providing party need to be traced;
(6) Firstly, finding a billing person through a calculation result of privacy data, and acquiring data records of a data provider and a data demander through the billing person; both sides prove that respective data records are provided as data evidences, and the behavior of the data provider and the data demander is traced through the data evidences; and judging whether the data demand party is credible or not by utilizing the result obtained by tracing the behavior of the data demand party, judging whether the undisclosed privacy data calculation result provided by the data provider is credible or not by utilizing the result obtained by tracing the behavior of the data provider, and finally realizing tracing of the privacy data calculation credibility.
From the description of the above embodiments, it will be apparent to those skilled in the art that the above embodiments may be implemented in software, or may be implemented by means of software plus a necessary general hardware platform. With such understanding, the technical solutions of the foregoing embodiments may be embodied in a software product, where the software product may be stored in a nonvolatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.), and include several instructions for causing a computer device (may be a personal computer, a server, or a network device, etc.) to perform the methods of the embodiments of the present invention.
Based on the same inventive concept, another embodiment of the present invention provides an electronic device (computer, server, smart phone, etc.) comprising a memory storing a computer program configured to be executed by the processor, and a processor, the computer program comprising instructions for performing the steps in the inventive method.
Based on the same inventive concept, another embodiment of the present invention provides a computer readable storage medium (e.g., ROM/RAM, magnetic disk, optical disk) storing a computer program which, when executed by a computer, implements the steps of the inventive method.
The above examples are provided for the purpose of describing the present invention only and are not intended to limit the scope of the present invention. The scope of the invention is defined by the appended claims. Various equivalents and modifications that do not depart from the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A system for calculating traceability of private data based on a blockchain consensus mechanism, comprising: the system comprises a data calling module based on a blockchain, a privacy data calculating module based on an intelligent contract and a privacy calculation reliability traceability module based on a consensus mechanism:
A data calling module based on block chain: the block chain of the data requiring party is utilized to have a data calling function, and the unpublished privacy data of the data provider is called through any one of two methods, wherein one method is to call the unpublished privacy data provided by the data provider through a data interface of the data provider by utilizing a calling chain code of the block chain; another approach is for the blockchain to provide the private data that is not disclosed by the URL call data provider; the method comprises the steps that firstly, a blockchain records an acquisition mode of the undisclosed privacy data in an intelligent contract to ensure the safety, credibility and privacy of the data, and then the undisclosed privacy data is transmitted to a privacy data calculation module based on the intelligent contract;
privacy data calculation module based on intelligent contract: the accounting person is selected by adopting a traceable consensus mechanism algorithm, and is used for recording the process, and the accounting person selected by the traceable consensus mechanism algorithm can trace and record the behavior of the accounting person; selecting a local node of a data provider or randomly allocating an idle anonymous computing node after selecting a billing person, and evaluating the credit of the node by using a cPoW credit model; in the nodes with qualified credit, the calculation of the unpublished privacy data of the data provider is completed through a TCCM consensus algorithm based on a threshold password scheme; after the calculation is completed, the calculation result of the private data which is not disclosed is encrypted by utilizing a data sharing and data tracing scheme, so that the calculation result of the private data which is not disclosed is not revealed and the reliability is traced; the data demand party calls the intelligent contract to acquire the calculation result of the private data which is not disclosed, and the calculation result of the private data which is not disclosed is transmitted to the privacy calculation reliability traceability module based on the consensus mechanism;
Privacy calculation credibility traceability module based on consensus mechanism: according to the calculation result of the private data which is not disclosed by the private data calculation module based on the intelligent contract, a billing person is found, and the data records of the data provider and the data demander are obtained through the billing person; the privacy calculation reliability tracing module based on the consensus mechanism receives data records of the data demand side and the data provider as data evidence, and performs behavior tracing of the data provider and the data demand side through the data evidence; and judging whether the data demand party is credible or not by utilizing the result obtained by tracing the behavior of the data demand party, judging whether the undisclosed privacy data calculation result provided by the data provider is credible or not by utilizing the result obtained by tracing the behavior of the data provider, and finally realizing tracing of the privacy data calculation credibility.
2. The blockchain consensus mechanism-based privacy data computing traceability system according to claim 1, wherein: in the privacy data calculation module based on the intelligent contract, the cPoW credit model evaluation consists of a node credit model and a fragmentation rotation model, and the node credit model and the fragmentation rotation model simultaneously carry out credit evaluation on the node;
(1) The node credit model is used for evaluating the performance of each index of the node, in order to make the influence degree of the evaluation indexes of the credit equal, firstly, the attribute is uniformly quantized, a three-layer neural network credit evaluation model is designed, the input layer node is a secondary index x for credit evaluation, the hidden layer node is a primary index y, the output layer node z range is [0,1], the weight from the input layer to the hidden layer is v, and the weight from the hidden layer to the output layer is w;
(2) The time slicing rotation model is designed for making the influence degree of the evaluation index of the reliability fair, and the lucky degree of the nodes is evaluated by selecting different nodes according to different time.
3. The blockchain consensus mechanism-based privacy data computing traceability system according to claim 1, wherein: in the privacy data calculation module based on the intelligent contract, a TCCM consensus algorithm based on a threshold password scheme is realized as follows:
the TCCM consensus algorithm based on the threshold password scheme, namely the consensus protocol of the threshold password scheme, adopts a guard metal model based on a threshold group signature theory, and before calculating privacy data, a data provider has a node ID, and pays a guard metal to a guard metal manager set through the node ID to prevent a Bayesian behavior; after the deposit is paid, the data provider signs a signature through the node ID and attaches the signature to the end of the deposit; the node ID completes the calculation of the privacy data; after the calculation is completed, the deposit manager sets to sign the node ID for paying deposit, returns deposit to the node ID, and the total time of the process is the time for encryption operation and the time after signing, which consumes little resource.
4. The blockchain consensus mechanism-based privacy data computing traceability system according to claim 1, wherein: in the privacy data calculation module based on intelligent contracts, a data sharing and data tracing scheme encrypts a calculation result of private data which is not disclosed by adopting a block chain data calculation method with a double-chain structure, wherein the double chain comprises two different types of block chains, namely a data block chain and a transaction block chain; the data blockchain encrypts and stores the private data which is not disclosed, so that the private data which is not disclosed is ensured to be stored safely and not to be accessed by other nodes; and the transaction blockchain stores the calculation result of the private data which is not disclosed in an encryption way and then extracts the calculation result in a transaction link.
5. The blockchain consensus mechanism-based privacy data computing traceability system according to claim 1, wherein: the privacy calculation reliability traceability module based on the consensus mechanism specifically realizes the following steps:
(1) According to the calculation result of the private data which is not disclosed by the private data calculation module based on the intelligent contract, a billing person is found, and the data records of the data provider and the data demander are obtained through the billing person;
(2) The privacy calculation reliability tracing module based on the consensus mechanism receives data records of a data demand side and a data provider as data evidence, wherein the data records comprise data parameters Args which are provided by the data demand side and are transmitted by the service at the time, a privacy calculation result Res which is provided by the data provider, a service record number LogID, service application time LogTime, data demand side ID and data provider ID, the transmitted data parameters Args, the privacy calculation result Res and the service record number LogID are necessary parameters, and the record Log of the service is extracted by inquiring the LogID on a blockchain;
(3) The behavior of the data demand party is traced, the service content of the time is traced through records obtained in the proving stage of the two parties, the HashArgs of the hash value after the encryption of the private data is calculated by using an md5 algorithm according to the input data parameter Args provided by the data demand party, the HashArgs of the hash value is compared with the hash value of the input data parameter recorded in the record Log, if the hash value is the same, the credibility of the input data of the data demand party is proved, otherwise, the credibility of the input data of the data demand party is confirmed;
(4) The data provider acts retrospectively, takes the input data parameter Args as input, and transmits the input data parameter Args to the data provider again, and the type SerType is calculated according to the privacy provided by the service in the Log:
a. If the privacy calculation type sertype=url, confirming the URL of the privacy calculation service API provided by the data provider when registering, sending the input data parameter Args as input to the URL address registered by the privacy calculation reliability tracing module based on the consensus mechanism, and obtaining the calculation result ReRes of the data provider;
b. if the privacy calculation type SerType=anonymous, confirming that the privacy calculation mode is that a privacy calculation reliability traceability module based on a consensus mechanism refers to an Anonymous calculation node for calculation, taking an incoming data parameter Args as input, submitting a privacy calculation algorithm to a designated calculation node for recalculation, and obtaining a calculation result ReRes;
calculating a hash value HashReRes of the privacy data after encryption by an md5 algorithm, equally calculating a hash value HashRes of the privacy calculation result Res provided by the data provider, and comparing the hash value HashRes, the hash value HashReRes of the privacy data after encryption and a result hash value LogHashRes in a record Log:
a. if the three are the same, proving that the result provided by the data provider is credible;
b. if the HashRes is inconsistent with the HashReRes, proving that an algorithm provided by a data provider is unstable and causes errors;
c. If the HashRes is inconsistent with the LogHasRes, proving that the privacy calculation result provided by the data provider is wrong;
d. if the HashReRes is inconsistent with the LogHashRes, proving that the data provider provides a privacy calculation algorithm with errors;
and returning the service tracing and responsibility determining result FinalRes according to the comparison result, thereby realizing the tracing of the credibility of the privacy data.
6. A block chain consensus mechanism-based privacy data calculation traceability method is characterized by comprising the following steps:
(1) The block chain of the data demand party is used for calling the private data which is not disclosed by the data provider and carrying out preparation work before the private data calculation; the method comprises the steps of calling the unpublished privacy data of the data provider through any one of two methods, wherein one method is to use a calling chain code of a blockchain to call the unpublished privacy data provided by the data provider through a data interface of the data provider; another approach is for the blockchain to provide the private data that is not disclosed by the URL call data provider;
(2) Before calculation starts, firstly, a traceable consensus mechanism algorithm is adopted to select a billing person, the billing person is used for a recording process, and the billing person selected by the traceable consensus mechanism algorithm can trace and record the behavior of the billing person, so that the record credibility of the billing person is ensured; selecting a local node of a data provider or randomly distributing an idle anonymous computing node after selecting a billing person, evaluating the credit of the node by using a cPoW credit model, and transmitting the private data which is not disclosed to the node for computing if the credit of the node is qualified;
(3) Starting calculation, firstly completing calculation of unpublished privacy data of a data provider through a TCCM consensus algorithm based on a threshold password scheme, and completing calculation; the TCCM consensus algorithm based on the threshold password scheme, namely the consensus protocol of the threshold password scheme, adopts a guard metal model based on a threshold group signature theory, and before calculating privacy data, a data provider has a node ID, and pays a guard metal to a guard metal manager set through the node ID to prevent a Bayesian behavior; after the deposit is paid, the data provider signs a signature through the node ID and attaches the signature to the end of the deposit; the node ID completes the calculation of the privacy data; after the calculation is completed, the deposit manager gathers and signs the node ID for paying deposit, returns deposit to the node ID, and the total time of the process is the time for encryption operation and the time after signing, and consumes little resources;
(4) Encrypting the calculation result of the private data which is not disclosed in the step (3) by utilizing a data sharing and data tracing scheme, so as to ensure that the calculation result of the private data which is not disclosed is not revealed and traced in credibility; the data sharing and data tracing scheme encrypts the calculation result of the private data by adopting a block chain data calculation method with a double-chain structure, wherein the double chain comprises two different types of block chains, namely a data block chain and a transaction block chain, and the data block chain encrypts and stores the private data which is not disclosed, so that the private data which is not disclosed is ensured to be stored safely and not to be accessed by other nodes; the transaction blockchain encrypts and stores the calculation result of the private data which is not disclosed and then extracts the calculation result in a transaction link;
(5) The data demand party calls an intelligent contract to acquire the calculation result of the private data which is not disclosed; in order to ensure the credibility of the calculation result of the private data which is not disclosed, carrying out behavior tracing on the data demand side and the data provider side; firstly, finding a billing person through a calculation result of privacy data, and acquiring data records of a data provider and a data demander through the billing person; both sides prove that respective data records are provided as data evidences, and the behavior of the data provider and the data demander is traced through the data evidences; and judging whether the data demand party is credible or not by utilizing the result obtained by tracing the behavior of the data demand party, judging whether the undisclosed privacy data calculation result provided by the data provider is credible or not by utilizing the result obtained by tracing the behavior of the data provider, and finally realizing tracing of the privacy data calculation credibility.
7. The method for calculating traceability of private data based on a blockchain consensus mechanism according to claim 6, wherein: in the step (2), the cPoW credit model evaluation consists of a node credit model and a fragmentation rotation model; the node credit model and the fragmentation rotation model carry out credit evaluation on the nodes at the same time; the node credit model is used for evaluating the performance of each index of the node, in order to make the influence degree of the evaluation indexes of the credit equal, firstly, the attribute is uniformly quantized, a three-layer neural network credit evaluation model is designed, the input layer node is a secondary index x for credit evaluation, the hidden layer node is a primary index y, the output layer node z range is [0,1], the weight from the input layer to the hidden layer is v, and the weight from the hidden layer to the output layer is w; the time slicing rotation model is designed for making the influence degree of the evaluation index of the reliability fair, and the lucky degree of the nodes is evaluated by selecting different nodes according to different time.
8. The method for calculating traceability of private data based on a blockchain consensus mechanism according to claim 6, wherein: the step (5) is specifically implemented as follows:
(1) According to the calculation result of the private data which is not disclosed by the private data calculation module based on the intelligent contract, a billing person is found, and the data records of the data provider and the data demander are obtained through the billing person;
(2) The privacy calculation reliability tracing module based on the consensus mechanism receives data records of a data demand side and a data provider as data evidence, wherein the data records comprise data parameters Args which are provided by the data demand side and are transmitted by the service at the time, a privacy calculation result Res which is provided by the data provider, a service record number LogID, service application time LogTime, data demand side ID and data provider ID, the transmitted data parameters Args, the privacy calculation result Res and the service record number LogID are necessary parameters, and the record Log of the service is extracted by inquiring the LogID on a blockchain;
(3) The behavior of the data demand party is traced, the service content of the time is traced through records obtained in the proving stage of the two parties, the HashArgs of the hash value after the encryption of the private data is calculated by using an md5 algorithm according to the input data parameter Args provided by the data demand party, the HashArgs of the hash value is compared with the hash value of the input data parameter recorded in the record Log, if the hash value is the same, the credibility of the input data of the data demand party is proved, otherwise, the credibility of the input data of the data demand party is confirmed;
(4) The data provider acts retrospectively, takes the input data parameter Args as input, and transmits the input data parameter Args to the data provider again, and the type SerType is calculated according to the privacy provided by the service in the Log:
a. if sertype=url, confirming the URL of the available privacy calculation service API registered by the data provider in the mode of privacy calculation, sending the input data parameter Args as input to the URL address registered by the privacy calculation reliability tracing module based on the consensus mechanism, and obtaining a calculation result ReRes of the data provider;
b. if sertype=anonymous, confirming that the privacy calculation mode is that a privacy calculation reliability traceability module based on a consensus mechanism refers to an Anonymous calculation node for calculation, taking an incoming data parameter Args as input, submitting a privacy calculation algorithm to a designated calculation node for recalculation, and obtaining a calculation result ReRes;
calculating a hash value HashReRes of the privacy data after encryption by an md5 algorithm, equally calculating a hash value HashRes of the privacy calculation result Res provided by the data provider, and comparing the hash value HashRes, the hash value HashReRes of the privacy data after encryption and a result hash value LogHashRes in a record Log:
a. If the three are the same, proving that the result provided by the data provider is credible;
b. if the HashRes is inconsistent with the HashReRes, proving that an algorithm provided by a data provider is unstable and causes errors;
c. if the HashRes is inconsistent with the LogHasRes, proving that the privacy calculation result provided by the data provider is wrong;
d. if the HashReRes is inconsistent with the LogHashRes, proving that the data provider provides a privacy calculation algorithm with errors;
and returning the service tracing and responsibility determining result FinalRes according to the comparison result, thereby realizing the tracing of the credibility of the privacy data.
9. The electronic equipment is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
a memory for storing a computer program;
a processor for executing a computer program stored on a memory, the execution of which implements the system of any one of claims 1-5 or the method of any one of claims 6-8.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the system of any of claims 1-5 or the method of any of claims 6-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310603310.1A CN116821952A (en) | 2023-05-25 | 2023-05-25 | Privacy data calculation traceability system and method based on block chain consensus mechanism |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310603310.1A CN116821952A (en) | 2023-05-25 | 2023-05-25 | Privacy data calculation traceability system and method based on block chain consensus mechanism |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116821952A true CN116821952A (en) | 2023-09-29 |
Family
ID=88121263
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310603310.1A Pending CN116821952A (en) | 2023-05-25 | 2023-05-25 | Privacy data calculation traceability system and method based on block chain consensus mechanism |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116821952A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117194359A (en) * | 2023-11-07 | 2023-12-08 | 国网信息通信产业集团有限公司 | Data sharing method, device, equipment and medium supporting privacy protection |
-
2023
- 2023-05-25 CN CN202310603310.1A patent/CN116821952A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117194359A (en) * | 2023-11-07 | 2023-12-08 | 国网信息通信产业集团有限公司 | Data sharing method, device, equipment and medium supporting privacy protection |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Baza et al. | B-ride: Ride sharing with privacy-preservation, trust and fair payment atop public blockchain | |
| CN110135844B (en) | Credit recording and inquiring method and device based on block chain and electronic equipment | |
| Pasdar et al. | Connect API with blockchain: A survey on blockchain oracle implementation | |
| CN109523683B (en) | Anonymous electronic voting method based on block chain technology | |
| CN110175913A (en) | Data processing system, method, calculating equipment and storage medium based on block chain | |
| CN110414270B (en) | Personal data protection system and method based on block chain | |
| CN112073484B (en) | GDPR compliance supervision method and system based on alliance chain | |
| CN110275891B (en) | Artificial intelligence software market | |
| CN105763547B (en) | Third party's authorization method and third party's authoring system | |
| CN112465627B (en) | Financial loan auditing method and system based on block chain and machine learning | |
| US11886423B2 (en) | Blockchain-based recording and querying operations | |
| Pasdar et al. | Blockchain oracle design patterns | |
| CN109636610B (en) | Decentralized data transaction system and method | |
| CN112862303B (en) | Crowdsourcing quality evaluation system and method based on block chain | |
| WO2022156594A1 (en) | Federated model training method and apparatus, electronic device, computer program product, and computer-readable storage medium | |
| US20220278845A1 (en) | Honest behavior enforcement via blockchain | |
| CN112801778A (en) | Federated bad asset blockchain | |
| Abubaker et al. | Trustful data trading through monetizing IoT data using BlockChain based review system | |
| CN116821952A (en) | Privacy data calculation traceability system and method based on block chain consensus mechanism | |
| CN115735212A (en) | Distributed machine learning via secure multi-party computing and ensemble learning | |
| Gao et al. | BFR‐SE: A Blockchain‐Based Fair and Reliable Searchable Encryption Scheme for IoT with Fine‐Grained Access Control in Cloud Environment | |
| CN111476640A (en) | Authentication method, system, storage medium and big data authentication platform | |
| Liu et al. | Blockchain-empowered lifecycle management for AI-generated content products in edge networks | |
| CN110033367A (en) | Based on the contract record method and device of block chain, electronic equipment | |
| CN108540335A (en) | The management method and managing device of device analysis report |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |