CN116049908A - Multi-party privacy calculation method and system based on blockchain - Google Patents
Multi-party privacy calculation method and system based on blockchain Download PDFInfo
- Publication number
- CN116049908A CN116049908A CN202310341988.7A CN202310341988A CN116049908A CN 116049908 A CN116049908 A CN 116049908A CN 202310341988 A CN202310341988 A CN 202310341988A CN 116049908 A CN116049908 A CN 116049908A
- Authority
- CN
- China
- Prior art keywords
- task
- allocation
- data
- block
- calculation
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/70—Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer
- G06F21/71—Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- General Physics & Mathematics (AREA)
- Computer Security & Cryptography (AREA)
- Mathematical Physics (AREA)
- Databases & Information Systems (AREA)
- Health & Medical Sciences (AREA)
- Bioethics (AREA)
- General Health & Medical Sciences (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention belongs to the field of privacy computation, relates to a data processing technology, and aims to solve the problem that an existing multiparty privacy computing system cannot reasonably distribute computing tasks according to the capacity state of an integrated memory core and the volume of a computing data set, in particular to a multiparty privacy computing method and system based on a blockchain, wherein the server is in communication connection with an operation detection module, a task distribution module, a blockchain storage module, a security distribution module, an efficiency distribution module and a storage module; the block chain storage module comprises a plurality of block nodes, and all the block nodes are connected into a block chain network to store data and tasks; the invention can detect and analyze the running state of the block chain storage module, obtain the running coefficient by comprehensively calculating and analyzing the running parameters of each block node, and suspend task allocation when the whole state is abnormal, thereby ensuring that the block chain storage module can normally run.
Description
Technical Field
The invention belongs to the field of privacy computation, relates to a data processing technology, and in particular relates to a multi-party privacy computation method and system based on a blockchain.
Background
The privacy calculation is a technical set for realizing data analysis and calculation on the premise of protecting the data from external leakage, so as to achieve the purpose of being 'available and invisible' for the data; on the premise of fully protecting data and privacy safety, the conversion and release of data value are realized.
The conventional multiparty privacy computing system cannot detect and analyze the capacity state of each integrated memory core, and further cannot perform reasonable computing task allocation according to the capacity state of the integrated memory core and the volume of a computing data set, so that the overall privacy computing efficiency is low.
Aiming at the technical problems, the application provides a solution.
Disclosure of Invention
The invention aims to provide a multi-party privacy computing method and system based on a blockchain, which are used for solving the problem that the existing multi-party privacy computing system cannot reasonably distribute computing tasks according to the capacity state of an integral memory and computing kernel and the volume of a computing data set;
the technical problems to be solved by the invention are as follows: how to provide a multiparty privacy computing system capable of reasonably distributing computing tasks according to the capacity state of a memory and computation integrated kernel and the volume of a computing data set.
The aim of the invention can be achieved by the following technical scheme:
a multiparty privacy computing system based on block chain comprises a server, wherein the server is in communication connection with an operation detection module, a task allocation module, a block chain storage module, a security allocation module, an efficiency allocation module and a storage module;
the block chain storage module comprises a plurality of block nodes, and all the block nodes are connected into a block chain network to store data and tasks;
the operation detection module is used for detecting and analyzing the operation state of the block chain storage module: marking block nodes subjected to detection analysis as detection objects, acquiring random data RC, blank data KC and capacity data YL of the detection objects, performing numerical computation to obtain operation coefficients YX of the block nodes, marking the block nodes as normal nodes or abnormal nodes according to the numerical values of the operation coefficients YX, marking the ratio of the number of the abnormal nodes to the number of the block nodes as the abnormal coefficients of the block chain storage module, and judging whether the operation state of the block chain storage module meets the requirements according to the numerical values of the abnormal coefficients;
the task allocation module is used for allocating and managing the calculation tasks of the block chain storage module: the task allocation mode comprises an efficiency allocation mode and a safety allocation mode; when the efficiency distribution mode is selected, an efficiency distribution signal is sent to the server, and the server sends the efficiency distribution signal to the efficiency distribution module after receiving the efficiency distribution signal; when the safe distribution mode is selected, a safe distribution signal is sent to the server, and the server sends the safe distribution signal to the safe distribution module after receiving the safe distribution signal;
the task allocation module stops task allocation when receiving the abnormal operation signal, and continues task allocation after the block chain storage module operates normally;
the efficiency distribution module is used for performing task distribution on the block chain storage module by adopting an efficiency distribution mode after receiving the efficiency distribution signal;
the safety distribution module is used for distributing tasks to the block chain storage module by adopting a safety distribution mode after receiving the safety distribution signal.
As a preferred embodiment of the present invention, the process of acquiring the optional data RC includes: when block nodes are allocated to the calculation tasks, memory values of the allocated calculation tasks are obtained and marked as any stored data RC; the acquiring process of the empty memory data KC comprises the following steps: before block nodes are distributed to a calculation task, marking the space domain memory value of the block nodes as space memory data KC; the process of acquiring the capacity data YL includes: the CPU occupancy of a block node is marked as capacity data YL before the block node is assigned to a computing task.
As a preferred embodiment of the invention, the specific process of marking the block node as a normal node or an abnormal node comprises the following steps: the operation threshold value YXmax is obtained through the storage module, and the operation coefficient YX of the block node is compared with the operation threshold value YXmax: if the operation coefficient YX is greater than the operation threshold YXmax, judging that the block node meets the calculation requirement of the allocation task, and marking the corresponding block node as a normal node; if the operation coefficient YX is smaller than or equal to the operation threshold YXmax, determining that the block node does not meet the calculation requirement of the allocation task, and marking the corresponding block node as an abnormal node.
As a preferred embodiment of the present invention, the specific process of determining whether the operation state of the blockchain storage module meets the requirement by the magnitude of the value of the anomaly coefficient includes: the storage module acquires an abnormal threshold value, and compares the abnormal coefficient with the abnormal threshold value: if the abnormal coefficient is smaller than the abnormal threshold, judging that the running state of the block chain storage module meets the requirement; if the abnormal coefficient is greater than or equal to the abnormal threshold, the running state of the block chain storage module is judged to be not met, the running detection module sends a running abnormal signal to the server, and the server sends the running abnormal signal to the task allocation module after receiving the running abnormal signal.
As a preferred embodiment of the present invention, the selection process of the efficiency allocation module and the safety allocation module includes: when a server receives a calculation task group, acquiring total memory data ZC, task data RW and periodic data ZQ of the calculation task group; the total memory data ZC of the calculation task group is the sum of the memory values of all tasks in the calculation task group, the task data RW of the calculation task group is the total number of the tasks in the calculation task group, and the period data ZQ of the calculation task group is the calculation period of the calculation task group; the distribution coefficient PF of the calculation task group is obtained by carrying out numerical calculation on the total storage data ZC, the task data RW and the periodic data ZQ; the allocation threshold value FPmax is obtained through the storage module, and the allocation coefficient FP is compared with the allocation threshold value FPmax: if the allocation coefficient FP is smaller than the allocation threshold FPmax, marking the allocation mode of the computing task group as a safe allocation mode; and if the allocation coefficient FP is greater than or equal to the allocation threshold FPmax, marking the allocation mode of the computing task group as an efficiency allocation mode.
As a preferred embodiment of the present invention, the specific process of the efficiency allocation module performing task allocation for the blockchain storage module by adopting the efficiency allocation mode includes: and randomly distributing tasks to the block nodes, after each block node is distributed to the calculation tasks, randomly dividing the rest calculation tasks into a plurality of groups of task sets, wherein the number of the calculation tasks in each group of task sets is the same as that of the block nodes, sequencing the calculation tasks in the task sets according to the sequence from the large memory value to the small memory value, sequencing the block nodes according to the sequence from the large operation coefficient YX, and matching the calculation tasks in the task sets with the block nodes according to the sequence.
As a preferred embodiment of the invention, the specific process of the secure distribution module adopting the secure distribution mode to distribute tasks to the blockchain storage module comprises the following steps: the block nodes are randomly assigned with tasks, and after each block node is assigned with a calculation task, traversing assignment analysis is carried out on the rest calculation tasks: marking a calculation task with the largest task memory value as a first traversal object, marking a block node with the largest running coefficient YX value as a first matching object, and comparing a data provider of the first traversal object with a data provider of the first matching object: if the task memory values are different, matching is successful, the calculation task with the second largest task memory value is marked as a second traversal object, and block node matching is performed; if the data provider of the first traversal object and the data provider of the second matching object are matched, the matching is failed, the block node with the maximum operating coefficient YX value is marked as a second matching object, and the data provider of the first traversal object and the data provider of the second matching object are compared until the matching is successful; and so on until the traversal matching analysis of all the computing tasks is completed.
A multiparty privacy computing method based on block chains comprises the following steps:
step one: detecting and analyzing the running state of the block chain storage module: marking the block node subjected to detection analysis as a detection object, acquiring any storage data RC, blank storage data KC and capacity data YL of the detection object, performing numerical computation to obtain an abnormal coefficient of the block chain storage module, and judging whether the running state of the block chain storage module meets the requirement or not according to the numerical value of the abnormal coefficient;
step two: the method comprises the steps of carrying out distribution management on computing tasks of a block chain storage module: when a server receives a calculation task group, acquiring total memory data ZC, task data RW and periodic data ZQ of the calculation task group, performing value calculation to obtain a distribution coefficient FP, and marking a task distribution mode as an efficiency distribution mode or a safety distribution mode according to the value of the distribution coefficient FP;
step three: and adopting a safe allocation mode or an efficiency allocation mode to allocate computing tasks to the block nodes in the block chain storage module.
The invention has the following beneficial effects:
1. the running state of the block chain storage module can be detected and analyzed through the running detection module, the running parameters of each block node are comprehensively calculated and analyzed to obtain the running coefficient, the block nodes are marked through the running coefficient, the overall running state of the block chain storage module is fed back through the number proportion of the abnormal nodes in the block nodes, task allocation is suspended when the overall state is abnormal, task allocation is continued after the block chain storage module releases the calculation pressure, and the block chain storage module can be guaranteed to run normally;
2. the computing tasks of the block chain storage module can be distributed and managed through the task distribution module, the distribution coefficient is obtained through numerical computation on each parameter of the computing task group, the task distribution mode is selected through the numerical value of the distribution coefficient, the proper task distribution mode is selected for computing task sets with different computing capacity requirements, and the data safety is ensured while the computing efficiency is ensured;
3. the safe distribution module can be used for distributing tasks to the blockchain storage module in a safe distribution mode, task data provided by the same data provider are distributed to different block nodes for calculation in a traversal distribution analysis mode, and all task data processed in the block nodes come from different data providers at the same time, so that the probability of data tampering is reduced;
4. the efficiency distribution module can be used for distributing tasks to the block chain storage module in an efficiency distribution mode, and when the capacity requirement of task calculation is large, task distribution is carried out according to the running state ordering of the block nodes and the memory value ordering in the task set, so that the block chain storage module can carry out task calculation with the highest efficiency.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a flowchart of a method according to a second embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in FIG. 1, a multi-party privacy computing system based on a blockchain comprises a server, wherein the server is in communication connection with an operation detection module, a task allocation module, a blockchain storage module, a security allocation module, an efficiency allocation module and a storage module.
The block chain storage module comprises a plurality of block nodes, and all the block nodes are connected into a block chain network to store data and tasks; each block node in the blockchain network forms a reconfigurable memory calculation integrated core through a real-time detection and dynamic task allocation algorithm so as to perform near data calculation on data, each block node is connected into a net topology structure so as to perform data and task storage, and task allocation and recombination are supported through a task addressing transmission method.
The operation detection module is used for detecting and analyzing the operation state of the block chain storage module: marking the block node subjected to detection analysis as a detection object, and acquiring any storage data RC, empty storage data KC and capacity data YL of the detection object, wherein the acquiring process of the any storage data RC comprises the following steps: when block nodes are allocated to the calculation tasks, memory values of the allocated calculation tasks are obtained and marked as any stored data RC; the acquiring process of the empty memory data KC comprises the following steps: before block nodes are distributed to a calculation task, marking the space domain memory value of the block nodes as space memory data KC; the process of acquiring the capacity data YL includes: before the block nodes are distributed to the calculation tasks, marking the CPU occupancy rate of the block nodes as capacity data YL; obtaining an operation coefficient YX of the block node according to a formula yx=α1kc/(α2rc+α3yl), wherein the operation coefficient is a value reflecting the calculated saturation degree of the block node, and the smaller the value of the operation coefficient is, the higher the calculated saturation degree of the block node is; wherein, alpha 1, alpha 2 and alpha 3 are all proportional coefficients, and alpha 1 > alpha 2 > alpha 3 > 1; the operation threshold value YXmax is obtained through the storage module, and the operation coefficient YX of the block node is compared with the operation threshold value YXmax: if the operation coefficient YX is greater than the operation threshold YXmax, judging that the block node meets the calculation requirement of the allocation task, and marking the corresponding block node as a normal node; if the operation coefficient YX is smaller than or equal to the operation threshold YXmax, judging that the block node does not meet the calculation requirement of the allocation task, and marking the corresponding block node as an abnormal node; marking the ratio of the number of abnormal nodes to the number of block nodes as an abnormal coefficient of a block chain storage module, acquiring an abnormal threshold value through the storage module, and comparing the abnormal coefficient with the abnormal threshold value: if the abnormal coefficient is smaller than the abnormal threshold, judging that the running state of the block chain storage module meets the requirement; if the abnormal coefficient is greater than or equal to the abnormal threshold value, judging that the running state of the block chain storage module does not meet the requirement, sending a running abnormal signal to a server by the running detection module, and sending the running abnormal signal to the task allocation module after the running abnormal signal is received by the server; detecting and analyzing the running state of the block chain storage module, comprehensively calculating and analyzing the running parameters of each block node to obtain the running coefficient, marking the block node through the running coefficient, feeding back the whole running state of the block chain storage module through the number proportion of the abnormal nodes in the block node, suspending task allocation when the whole state is abnormal, continuing task allocation after the block chain storage module releases the calculation pressure, and ensuring that the block chain storage module can normally run.
The task allocation module is used for allocating and managing the calculation tasks of the block chain storage module: the task allocation mode comprises an efficiency allocation mode and a safety allocation mode; the selection process of the efficiency distribution module and the safety distribution module comprises the following steps: when a server receives a calculation task group, acquiring total memory data ZC, task data RW and periodic data ZQ of the calculation task group; the total memory data ZC of the calculation task group is the sum of the memory values of all tasks in the calculation task group, the task data RW of the calculation task group is the total number of the tasks in the calculation task group, and the period data ZQ of the calculation task group is the calculation period of the calculation task group; obtaining a distribution coefficient PF of the calculation task group through a formula FP= (beta 1 x ZC+beta 2 x RW)/(beta 3 x ZQ), wherein the distribution coefficient is a numerical value reflecting the processing difficulty degree of the calculation task group, and the larger the numerical value of the distribution coefficient is, the higher the processing difficulty degree of the calculation task group is; wherein β1, β2 and β3 are proportionality coefficients, and β1 > β2 > β3 > 1; the allocation threshold value FPmax is obtained through the storage module, and the allocation coefficient FP is compared with the allocation threshold value FPmax: if the allocation coefficient FP is smaller than the allocation threshold FPmax, marking an allocation mode of the computing task group as a safe allocation mode, sending a safe allocation signal to a server by the task allocation module, and sending the safe allocation signal to the safe allocation module after the server receives the safe allocation signal; if the allocation coefficient FP is greater than or equal to the allocation threshold FPmax, marking an allocation mode of the calculation task group as an efficiency allocation mode, sending an efficiency allocation signal to a server by the task allocation module, and sending the efficiency allocation signal to the efficiency allocation module after the efficiency allocation signal is received by the server; the task allocation module stops task allocation when receiving the abnormal operation signal, and continues task allocation after the block chain storage module operates normally; the method comprises the steps of carrying out distribution management on computing tasks of a block chain storage module, carrying out numerical computation on each parameter of a computing task group to obtain distribution coefficients, carrying out task distribution mode selection according to the numerical value of the distribution coefficients, selecting proper task distribution modes for computing task sets with different computing capacity requirements, and guaranteeing data safety while guaranteeing computing efficiency.
The secure distribution module is used for performing task distribution on the block chain storage module by adopting a secure distribution mode after receiving the secure distribution signal: the block nodes are randomly assigned with tasks, and after each block node is assigned with a calculation task, traversing assignment analysis is carried out on the rest calculation tasks: marking a calculation task with the largest task memory value as a first traversal object, marking a block node with the largest running coefficient YX value as a first matching object, and comparing a data provider of the first traversal object with a data provider of the first matching object: if the task memory values are different, matching is successful, the calculation task with the second largest task memory value is marked as a second traversal object, and block node matching is performed; if the data provider of the first traversal object and the data provider of the second matching object are matched, the matching is failed, the block node with the maximum operating coefficient YX value is marked as a second matching object, and the data provider of the first traversal object and the data provider of the second matching object are compared until the matching is successful; and the like, until the traversal matching analysis of all the calculation tasks is completed; and task distribution is carried out on the block chain storage module by adopting a safe distribution mode, task data provided by the same data provider are distributed to different block nodes for calculation in a traversing distribution analysis mode, and all task data processed in the block nodes come from different data providers at the same time, so that the probability of data tampering is reduced.
The efficiency distribution module is used for performing task distribution on the block chain storage module by adopting an efficiency distribution mode after receiving the efficiency distribution signal: randomly distributing tasks to the block nodes, after each block node is distributed to the calculation tasks, randomly dividing the rest calculation tasks into a plurality of groups of task sets, wherein the number of the calculation tasks in each group of task sets is the same as that of the block nodes, sequencing the calculation tasks in the task sets according to the sequence from the large memory value to the small memory value, sequencing the block nodes according to the sequence from the large operation coefficient YX, and matching the calculation tasks in the task sets with the block nodes according to the sequence; and performing task allocation for the block chain storage module by adopting an efficiency allocation mode, and performing task allocation according to the running state ordering of the block nodes and the memory value ordering in the task set when the capacity requirement of task calculation is large, so that the block chain storage module performs task calculation with the highest efficiency.
Example two
As shown in fig. 2, a multi-party privacy calculating method based on block chains comprises the following steps:
step one: detecting and analyzing the running state of the block chain storage module: marking the block node subjected to detection analysis as a detection object, acquiring any storage data RC, blank storage data KC and capacity data YL of the detection object, performing numerical computation to obtain an abnormal coefficient of the block chain storage module, and judging whether the running state of the block chain storage module meets the requirement or not according to the numerical value of the abnormal coefficient;
step two: the method comprises the steps of carrying out distribution management on computing tasks of a block chain storage module: when a server receives a calculation task group, acquiring total memory data ZC, task data RW and periodic data ZQ of the calculation task group, performing value calculation to obtain a distribution coefficient FP, and marking a task distribution mode as an efficiency distribution mode or a safety distribution mode according to the value of the distribution coefficient FP;
step three: and adopting a safe allocation mode or an efficiency allocation mode to allocate computing tasks to the block nodes in the block chain storage module.
In the working process, block nodes subjected to detection and analysis are marked as detection objects, any storage data RC, blank storage data KC and capacity data YL of the detection objects are obtained, numerical value calculation is carried out to obtain abnormal coefficients of the block chain storage module, and whether the running state of the block chain storage module meets the requirement is judged according to the numerical value of the abnormal coefficients; when a server receives a calculation task group, acquiring total memory data ZC, task data RW and periodic data ZQ of the calculation task group, performing value calculation to obtain a distribution coefficient FP, and marking a task distribution mode as an efficiency distribution mode or a safety distribution mode according to the value of the distribution coefficient FP; and adopting a safe allocation mode or an efficiency allocation mode to allocate computing tasks to the block nodes in the block chain storage module.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: the formula yx=α1×kc/(α2×rc+α3×yl); collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding operation coefficient for each group of sample data; substituting the set operation coefficient and the collected sample data into a formula, forming a ternary one-time equation set by any three formulas, screening the calculated coefficient, and taking an average value to obtain values of alpha 1, alpha 2 and alpha 3 which are 3.74, 2.97 and 2.65 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding operation coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected, for example, the operation coefficient is in direct proportion to the value of the empty data.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The multi-party privacy computing system based on the blockchain is characterized by comprising a server, wherein the server is in communication connection with an operation detection module, a task allocation module, a blockchain storage module, a security allocation module, an efficiency allocation module and a storage module;
the block chain storage module comprises a plurality of block nodes, and all the block nodes are connected into a block chain network to store data and tasks;
the operation detection module is used for detecting and analyzing the operation state of the block chain storage module: marking block nodes subjected to detection analysis as detection objects, acquiring random data RC, blank data KC and capacity data YL of the detection objects, performing numerical computation to obtain operation coefficients YX of the block nodes, marking the block nodes as normal nodes or abnormal nodes according to the numerical values of the operation coefficients YX, marking the ratio of the number of the abnormal nodes to the number of the block nodes as the abnormal coefficients of the block chain storage module, and judging whether the operation state of the block chain storage module meets the requirements according to the numerical values of the abnormal coefficients;
the task allocation module is used for allocating and managing the calculation tasks of the block chain storage module: the task allocation mode comprises an efficiency allocation mode and a safety allocation mode; when the efficiency distribution mode is selected, an efficiency distribution signal is sent to the server, and the server sends the efficiency distribution signal to the efficiency distribution module after receiving the efficiency distribution signal; when the safe distribution mode is selected, a safe distribution signal is sent to the server, and the server sends the safe distribution signal to the safe distribution module after receiving the safe distribution signal;
the task allocation module stops task allocation when receiving the abnormal operation signal, and continues task allocation after the block chain storage module operates normally;
the efficiency distribution module is used for performing task distribution on the block chain storage module by adopting an efficiency distribution mode after receiving the efficiency distribution signal;
the safety distribution module is used for distributing tasks to the block chain storage module by adopting a safety distribution mode after receiving the safety distribution signal.
2. The blockchain-based multiparty privacy computing system of claim 1, wherein the process of obtaining the stored data RC comprises: when block nodes are allocated to the calculation tasks, memory values of the allocated calculation tasks are obtained and marked as any stored data RC; the acquiring process of the empty memory data KC comprises the following steps: before block nodes are distributed to a calculation task, marking the space domain memory value of the block nodes as space memory data KC; the process of acquiring the capacity data YL includes: the CPU occupancy of a block node is marked as capacity data YL before the block node is assigned to a computing task.
3. The multi-party blockchain-based privacy computing system of claim 2, wherein the specific process of marking a blocknode as either a normal node or an abnormal node comprises: the operation threshold value YXmax is obtained through the storage module, and the operation coefficient YX of the block node is compared with the operation threshold value YXmax: if the operation coefficient YX is greater than the operation threshold YXmax, judging that the block node meets the calculation requirement of the allocation task, and marking the corresponding block node as a normal node; if the operation coefficient YX is smaller than or equal to the operation threshold YXmax, determining that the block node does not meet the calculation requirement of the allocation task, and marking the corresponding block node as an abnormal node.
4. The multi-party privacy computing system as claimed in claim 3, wherein the determining whether the operating state of the blockchain storage module meets the requirement by the magnitude of the value of the anomaly coefficient comprises: the storage module acquires an abnormal threshold value, and compares the abnormal coefficient with the abnormal threshold value: if the abnormal coefficient is smaller than the abnormal threshold, judging that the running state of the block chain storage module meets the requirement; if the abnormal coefficient is greater than or equal to the abnormal threshold, the running state of the block chain storage module is judged to be not met, the running detection module sends a running abnormal signal to the server, and the server sends the running abnormal signal to the task allocation module after receiving the running abnormal signal.
5. The blockchain-based multiparty privacy computing system of claim 4, wherein the selection process of the efficiency allocation module and the security allocation module comprises: when a server receives a calculation task group, acquiring total memory data ZC, task data RW and periodic data ZQ of the calculation task group; the total memory data ZC of the calculation task group is the sum of the memory values of all tasks in the calculation task group, the task data RW of the calculation task group is the total number of the tasks in the calculation task group, and the period data ZQ of the calculation task group is the calculation period of the calculation task group; the distribution coefficient PF of the calculation task group is obtained by carrying out numerical calculation on the total storage data ZC, the task data RW and the periodic data ZQ; the allocation threshold value FPmax is obtained through the storage module, and the allocation coefficient FP is compared with the allocation threshold value FPmax: if the allocation coefficient FP is smaller than the allocation threshold FPmax, marking the allocation mode of the computing task group as a safe allocation mode; and if the allocation coefficient FP is greater than or equal to the allocation threshold FPmax, marking the allocation mode of the computing task group as an efficiency allocation mode.
6. The multi-party privacy computing system as defined in claim 5, wherein the efficiency allocation module performs task allocation for the blockchain storage module using an efficiency allocation mode comprising: and randomly distributing tasks to the block nodes, after each block node is distributed to the calculation tasks, randomly dividing the rest calculation tasks into a plurality of groups of task sets, wherein the number of the calculation tasks in each group of task sets is the same as that of the block nodes, sequencing the calculation tasks in the task sets according to the sequence from the large memory value to the small memory value, sequencing the block nodes according to the sequence from the large operation coefficient YX, and matching the calculation tasks in the task sets with the block nodes according to the sequence.
7. The multi-party privacy computing system as in claim 6, wherein the secure distribution module performs task distribution for the blockchain storage module using a secure distribution mode comprising: the block nodes are randomly assigned with tasks, and after each block node is assigned with a calculation task, traversing assignment analysis is carried out on the rest calculation tasks: marking a calculation task with the largest task memory value as a first traversal object, marking a block node with the largest running coefficient YX value as a first matching object, and comparing a data provider of the first traversal object with a data provider of the first matching object: if the task memory values are different, matching is successful, the calculation task with the second largest task memory value is marked as a second traversal object, and block node matching is performed; if the data provider of the first traversal object and the data provider of the second matching object are matched, the matching is failed, the block node with the maximum operating coefficient YX value is marked as a second matching object, and the data provider of the first traversal object and the data provider of the second matching object are compared until the matching is successful; and so on until the traversal matching analysis of all the computing tasks is completed.
8. The multi-party privacy calculating method based on the blockchain is characterized by comprising the following steps of:
step one: detecting and analyzing the running state of the block chain storage module: marking the block node subjected to detection analysis as a detection object, acquiring any storage data RC, blank storage data KC and capacity data YL of the detection object, performing numerical computation to obtain an abnormal coefficient of the block chain storage module, and judging whether the running state of the block chain storage module meets the requirement or not according to the numerical value of the abnormal coefficient;
step two: the method comprises the steps of carrying out distribution management on computing tasks of a block chain storage module: when a server receives a calculation task group, acquiring total memory data ZC, task data RW and periodic data ZQ of the calculation task group, performing value calculation to obtain a distribution coefficient FP, and marking a task distribution mode as an efficiency distribution mode or a safety distribution mode according to the value of the distribution coefficient FP;
step three: and adopting a safe allocation mode or an efficiency allocation mode to allocate computing tasks to the block nodes in the block chain storage module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310341988.7A CN116049908B (en) | 2023-04-03 | 2023-04-03 | Multi-party privacy calculation method and system based on blockchain |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310341988.7A CN116049908B (en) | 2023-04-03 | 2023-04-03 | Multi-party privacy calculation method and system based on blockchain |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116049908A true CN116049908A (en) | 2023-05-02 |
| CN116049908B CN116049908B (en) | 2023-06-06 |
Family
ID=86118639
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310341988.7A Active CN116049908B (en) | 2023-04-03 | 2023-04-03 | Multi-party privacy calculation method and system based on blockchain |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116049908B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116226096A (en) * | 2023-05-09 | 2023-06-06 | 北京惠朗时代科技有限公司 | Electronic signature data maintenance management system based on data processing |
| CN117667602A (en) * | 2024-01-31 | 2024-03-08 | 山东航天九通车联网有限公司 | Cloud computing-based online service computing power optimization method and device |
| CN118708430A (en) * | 2024-06-04 | 2024-09-27 | 广东科技学院 | Computer data security protection system based on blockchain |
| CN118982125A (en) * | 2024-07-22 | 2024-11-19 | 北京科杰科技有限公司 | A task management method based on time estimation |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109120704A (en) * | 2018-08-24 | 2019-01-01 | 郑州云海信息技术有限公司 | A kind of resource monitoring method of cloud platform, device and equipment |
| CN110022299A (en) * | 2019-03-06 | 2019-07-16 | 浙江天脉领域科技有限公司 | A kind of method of ultra-large distributed network computing |
| CN111600967A (en) * | 2020-07-24 | 2020-08-28 | 江苏荣泽信息科技股份有限公司 | Access pressure-based load balancing system among block chain nodes |
| US20200356454A1 (en) * | 2019-05-10 | 2020-11-12 | Silicon Motion Technology (Hong Kong) Limited | Method and computer storage node of shared storage system for abnormal behavior detection/analysis |
| US20210117220A1 (en) * | 2018-11-23 | 2021-04-22 | China Unionpay Co., Ltd. | Unified resource scheduling coordinator, method for creating a virtual machine and/or container, and unified resource scheduling system |
| US20220138179A1 (en) * | 2020-11-02 | 2022-05-05 | Blue Tech Inc. | Private blockchain system and method |
| WO2022160886A1 (en) * | 2021-01-29 | 2022-08-04 | Zhejiang Dahua Technology Co., Ltd. | Task allocation method, apparatus, storage medium, and electronic device |
| WO2022171082A1 (en) * | 2021-02-10 | 2022-08-18 | 中国移动通信有限公司研究院 | Information processing method, apparatus, system, electronic device and storage medium |
| CN115827256A (en) * | 2023-02-17 | 2023-03-21 | 上海亿铸智能科技有限公司 | Task transmission scheduling management system for multi-core storage and computation integrated accelerator network |
-
2023
- 2023-04-03 CN CN202310341988.7A patent/CN116049908B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109120704A (en) * | 2018-08-24 | 2019-01-01 | 郑州云海信息技术有限公司 | A kind of resource monitoring method of cloud platform, device and equipment |
| US20210117220A1 (en) * | 2018-11-23 | 2021-04-22 | China Unionpay Co., Ltd. | Unified resource scheduling coordinator, method for creating a virtual machine and/or container, and unified resource scheduling system |
| CN110022299A (en) * | 2019-03-06 | 2019-07-16 | 浙江天脉领域科技有限公司 | A kind of method of ultra-large distributed network computing |
| US20200356454A1 (en) * | 2019-05-10 | 2020-11-12 | Silicon Motion Technology (Hong Kong) Limited | Method and computer storage node of shared storage system for abnormal behavior detection/analysis |
| CN111600967A (en) * | 2020-07-24 | 2020-08-28 | 江苏荣泽信息科技股份有限公司 | Access pressure-based load balancing system among block chain nodes |
| US20220138179A1 (en) * | 2020-11-02 | 2022-05-05 | Blue Tech Inc. | Private blockchain system and method |
| WO2022160886A1 (en) * | 2021-01-29 | 2022-08-04 | Zhejiang Dahua Technology Co., Ltd. | Task allocation method, apparatus, storage medium, and electronic device |
| WO2022171082A1 (en) * | 2021-02-10 | 2022-08-18 | 中国移动通信有限公司研究院 | Information processing method, apparatus, system, electronic device and storage medium |
| CN115827256A (en) * | 2023-02-17 | 2023-03-21 | 上海亿铸智能科技有限公司 | Task transmission scheduling management system for multi-core storage and computation integrated accelerator network |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116226096A (en) * | 2023-05-09 | 2023-06-06 | 北京惠朗时代科技有限公司 | Electronic signature data maintenance management system based on data processing |
| CN116226096B (en) * | 2023-05-09 | 2023-07-14 | 北京惠朗时代科技有限公司 | Electronic signature data maintenance management system based on data processing |
| CN117667602A (en) * | 2024-01-31 | 2024-03-08 | 山东航天九通车联网有限公司 | Cloud computing-based online service computing power optimization method and device |
| CN117667602B (en) * | 2024-01-31 | 2024-04-26 | 山东航天九通车联网有限公司 | Cloud computing-based online service computing power optimization method and device |
| CN118708430A (en) * | 2024-06-04 | 2024-09-27 | 广东科技学院 | Computer data security protection system based on blockchain |
| CN118708430B (en) * | 2024-06-04 | 2025-01-24 | 广东科技学院 | Computer data security protection system based on blockchain |
| CN118982125A (en) * | 2024-07-22 | 2024-11-19 | 北京科杰科技有限公司 | A task management method based on time estimation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116049908B (en) | 2023-06-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116049908B (en) | Multi-party privacy calculation method and system based on blockchain | |
| CN103346912B (en) | Method, device and system for conducting warning correlation analysis | |
| CN108123849A (en) | Detect threshold value determination method, device, equipment and the storage medium of network traffics | |
| US10884805B2 (en) | Dynamically configurable operation information collection | |
| CN115412371B (en) | Big data security protection method and system based on Internet of things and cloud platform | |
| US20150006459A1 (en) | Alarm Correlation Analysis Method, Apparatus and System | |
| CN116594857A (en) | Office software intelligent interaction management platform based on artificial intelligence | |
| CN116226096B (en) | Electronic signature data maintenance management system based on data processing | |
| CN113672475A (en) | Alarm processing method and device, computer equipment and storage medium | |
| CN117478679A (en) | Dynamic load balancing method based on data analysis | |
| CN115827256B (en) | Task transmission scheduling management system for multi-core memory and calculation integrated accelerator network | |
| CN109634808A (en) | A kind of chain type monitor event root cause analysis method based on association analysis | |
| CN114124773B (en) | Port block address conversion test system and method | |
| CN110716986A (en) | Big data analysis system and application method thereof | |
| CN115085948B (en) | Network security situation assessment method based on improved D-S evidence theory | |
| CN105812280A (en) | Classification method and electronic equipment | |
| CN105872082A (en) | Fine-grained resource response system based on load balancing algorithm of container cluster | |
| CN117667602B (en) | Cloud computing-based online service computing power optimization method and device | |
| CN117255134A (en) | Data transmission method based on cloud edge cooperation | |
| CN118200024A (en) | Intrusion detection system facing distributed cloud server based on decentralization federation learning | |
| CN117271177B (en) | Root cause positioning method and device based on link data, electronic equipment and storage medium | |
| CN106161339B (en) | Obtain the method and device of IP access relations | |
| Luizelli et al. | Characterizing the impact of network substrate topologies on virtual network embedding | |
| CN109522915B (en) | Virus file clustering method and device and readable medium | |
| CN106301889B (en) | A kind of port numbers rapid-setting method and system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: B567, 4th Floor, Building 14, Cuiwei Zhongli, Haidian District, Beijing, 100036 Patentee after: Shuliju (Beijing) Technology Co.,Ltd. Address before: Room A12, Room 501, 5th Floor, No. 20 Gongti East Road, Chaoyang District, Beijing, 100020 Patentee before: Beijing Shuliju Technology Co.,Ltd. |
|
| CP03 | Change of name, title or address |