CN116208358A - Large-scale PBFT (physical Bifiduciary Point) improvement technology based on reputation evaluation model - Google Patents
Large-scale PBFT (physical Bifiduciary Point) improvement technology based on reputation evaluation model Download PDFInfo
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- CN116208358A CN116208358A CN202211437047.5A CN202211437047A CN116208358A CN 116208358 A CN116208358 A CN 116208358A CN 202211437047 A CN202211437047 A CN 202211437047A CN 116208358 A CN116208358 A CN 116208358A
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- 238000013210 evaluation model Methods 0.000 title claims abstract description 21
- 238000005516 engineering process Methods 0.000 title claims abstract description 7
- 238000011156 evaluation Methods 0.000 claims abstract description 6
- 108091035707 Consensus sequence Proteins 0.000 claims description 2
- 238000000034 method Methods 0.000 abstract description 7
- 238000011161 development Methods 0.000 abstract description 2
- 238000004088 simulation Methods 0.000 abstract 1
- 239000010410 layer Substances 0.000 description 18
- 238000012795 verification Methods 0.000 description 10
- 230000006399 behavior Effects 0.000 description 8
- 238000004891 communication Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 238000005315 distribution function Methods 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/12—Applying verification of the received information
- H04L63/126—Applying verification of the received information the source of the received data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1441—Countermeasures against malicious traffic
- H04L63/145—Countermeasures against malicious traffic the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
- H04L9/3247—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving digital signatures
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
- H04L9/3297—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving time stamps, e.g. generation of time stamps
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L2209/00—Additional information or applications relating to cryptographic mechanisms or cryptographic arrangements for secret or secure communication H04L9/00
- H04L2209/46—Secure multiparty computation, e.g. millionaire problem
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Abstract
The PBFT is used as a consensus algorithm for achieving information consistency in a blockchain, and is more suitable for being applied to a alliance chain with partial decentralization and anti-Bayesian node characteristics. However, the performance of the existing federated chain consensus algorithm PBFT is significantly reduced as the regional node size increases, greatly limiting the development of large-scale federated chains. In view of the above, a reputation evaluation model-based sliced PBFT consensus algorithm is presented herein. The method comprises the steps of dividing a large-scale node cluster into high-efficiency small-scale node clusters by using a slicing technology, and realizing overall consensus by using local consensus. And secondly, giving different reputation grades to the nodes through a reputation evaluation model, selecting a high reputation evaluation node to preferentially select a main node, firstly providing a gray area concept, and improving the stability of a consensus scheme through integrating the node types. Finally, simulation experiment results show that the invention has the advantage of high performance and provides higher consensus stability under a large-scale node environment.
Description
Technical Field
The invention belongs to the field of block chains, and relates to a practical Bayesian fault tolerance technology.
Background
In a blockchain, a consensus mechanism is used as a core part of the blockchain. How to ensure the consistency of data among a plurality of nodes is a problem that needs to be considered by a consensus mechanism, and the performance of a consensus system is obviously improved by an excellent consensus mechanism. The main characteristic of the Bayesian fault-tolerant algorithm is that the transmission of messages on unreliable channels can reach consensus within a certain fault-tolerant range. PBFT is widely used in federated chain applications, but PBFT also has a problem of dramatic performance degradation in the case of large-scale consensus networks, which greatly limits the development and landing of federated chain applications. After the PBFT consensus model structure is changed from a single layer to a double layer, the communication overhead for realizing consensus can be greatly reduced, and the communication efficiency is improved. On the basis, the invention introduces a reputation evaluation model and provides a concept of a gray area, and possible Bayesian nodes are placed in the gray area through the output result of the reputation evaluation model, so that the influence of the Bayesian nodes on the consensus model is reduced, and the stability of the consensus model is improved.
Disclosure of Invention
The invention aims to provide a large-scale PBFT (physical random access memory) improvement technology based on a reputation evaluation model, and the invention introduces a double-layer consensus model in order to improve the consensus efficiency of PBFT in a large-scale consensus network. In order to improve the stability of the model, the invention provides a concept of gray area, introduces a reputation evaluation model, inputs the consensus behavior of the node into the reputation evaluation model, preliminarily judges whether the node is a Bayesian node according to the output result of the consensus model to the node, and improves the stability of the consensus model by using the possible Bayesian node scheme gray area. The invention adopts the following technical scheme:
step one: the bottom layer area in the double-layer consensus model achieves consensus;
step two: the bottom layer area uploads the message to the top layer area according to the consensus sequence, and consensus is achieved in the top layer area;
step three: inputting the node consensus behavior into a reputation evaluation model, and outputting reputation evaluation scores of the nodes by the reputation evaluation model;
step four: the possible bayer pattern nodes are placed in gray areas according to the output reputation scores.
Drawings
FIG. 1 depicts in detail a specific consensus model structure of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that only some, but not all embodiments of the invention are described below.
The invention provides a large-scale PBFT (physical property added-quality improvement) technology based on a reputation evaluation model, which comprises the following specific steps:
step one: underlying region consensus phase
1) The underlying region agrees with requests from clients:
step 1 (Request) client c issues a Request message m to the underlying region. After receiving sr of the secondary node in the bottom layer area, the secondary node will
The message is signed and packed by the private key of the message and then is sent to the master node in the current area, m represents the request message sent by c, and ts represents the timestamp when the client sends the request message.
The current area master node is receiving
After the message, it is necessary to determine the +.>
If the private key signature in the message is correct, the Pre-preparation phase is entered if so, and if so, the message is discarded.
Step 2 (Pre-preparation) the master node p receives the transmission from the slave node
Message, generating +.A. signed by self private key>
A message and broadcast in the current region, while recording the request into the local log, v representing the view number,m represents the message content, d represents the message digest of the message content, n is the current view that the master node assigns to after receiving the message is unique and within the range H, H]The number in the server is mainly used for sequencing all client requests.
Here, the Pre-preparation message reduces the communication resources occupied by the Pre-preparation message by placing the m content of the message itself outside the Pre-preparation message. The Pre-prepare message serves as an evidence to determine that the request is given a sequence number n in View v, so that it can be traced back in View-Change.
The secondary node in the current area receives the primary node
The message requires the following checks:
whether the signature is correct in the Request and Pre-prepare messages.
Whether the current view number is v.
The node never receives messages m in view v with sequence number n but digest d not identical.
Whether the message digest of m is consistent with d in the message.
Judging whether n is within the interval H, H.
If all the requirements are met, passing verification, entering a preparation stage, and if any of the requirements are not met, discarding the message.
Step 3 (preparation) the current region secondary node i receives
After the message and after verification passes, enter the preparation phase, multicast one at all other secondary nodes of the current area
Messages, and adds both messages to the log of i.
The primary node and the secondary node receive
After the message, the following verification is performed:
whether the message signature is correct.
Whether n is in [ H, H ].
D is the sum of
D in the message is the same.
If the verification is not passed, the message is discarded directly. If the verification is passed, the message is accepted. The current node I writes the prepared (m, v, n, I, ts) into the message log, receives the message from 2f different nodes
Message-consistent +.>
After the message, the Verify phase is entered.
Step 4 (Verify) when the Verify phase is entered, node i sends a bar to the current region master node p after the prepended (m, v, n, i, ts) is correct
Accepting +.>
After the message and insert it into log:
whether the message signature is correct.
Whether the current node has received n under the same view v.
Calculate the information digest of m, if d can agree with it.
Judging whether n is between H, H.
The prepended (m, v, n, i, ts) in any f+1 node set is true, ensuring that verified (m, v, n, ts) is true. The prepred (m, v, n, i, ts) is true and the master node p accepts 2f+1 AND including itself
M, v, n in the message are identical +.>
A message. Confirm that verified-local (m, v, n, i, ts) is true. After the two conditions are met, if the region is the bottom layer region, the sub stage is entered, and if the region is the top layer region, the Reply stage is entered.
Step 5 (sub) entering sub phase, the current region master node bp sequentially transmits according to the order of n
The top-level region continues to execute the consensus mechanism for the top-level region.
Step two: top layer region consensus phase
2) The top layer region is uploaded according to the bottom layer region to carry out top layer region consensus through the bottom layer consensus information:
step 1 (Request) client c issues a Request message m to the underlying region. After receiving sr of the secondary node in the bottom layer area, the secondary node will
The message is signed and packed by the private key of the message and then is sent to the master node in the current area, m represents the request message sent by c, and ts represents the timestamp when the client sends the request message.
The current area master node is receiving
After the message, it is necessary to determine the +.>
If the private key signature in the message is correct, the Pre-preparation phase is entered if so, and if so, the message is discarded.
Step 2 (Pre-preparation) the master node p receives the transmission from the slave node
Message, generating +.A. signed by self private key>
The message is broadcast in the current area, and the request is recorded in the local log, v represents the view number, m represents the message content, d represents the information abstract of the message content, n is the unique and range [ H, H ] of the current view allocated by the master node after receiving the message]The number in the server is mainly used for sequencing all client requests.
Here, the Pre-preparation message reduces the communication resources occupied by the Pre-preparation message by placing the m content of the message itself outside the Pre-preparation message. The Pre-prepare message serves as an evidence to determine that the request is given a sequence number n in View v, so that it can be traced back in View-Change.
The secondary node in the current area receives the primary node
The message requires the following checks:
whether the signature is correct in the Request and Pre-prepare messages.
Whether the current view number is v.
The node never receives messages m in view v with sequence number n but digest d not identical.
Whether the message digest of m is consistent with d in the message.
Judging whether n is within the interval H, H.
If all the requirements are met, passing verification, entering a preparation stage, and if any of the requirements are not met, discarding the message.
Step 3 (preparation) the current region secondary node i receives
After the message and after verification passes, enter the preparation phase, multicast one at all other secondary nodes of the current area
Messages, and adds both messages to the log of i.
The primary node and the secondary node receive
After the message, the following verification is performed:
whether the message signature is correct.
Whether n is in [ H, H ].
D is the sum of
D in the message is the same.
If the verification is not passed, the message is discarded directly. If the verification is passed, the message is accepted. The current node i writes the prepared (m, v, n, i, ts) into the message log, receives the and from 2f different nodes
Message-consistent +.>
After the message, the Verify phase is entered.
Step 4 (Verify) when the Verify phase is entered, node i sends a bar to the current region master node p after the prepended (m, v, n, i, ts) is correct
Accepting +.>
After the message and insert it into log:
whether the message signature is correct.
Whether the current node has received n under the same view v.
Calculate the information digest of m, if d can agree with it.
Judging whether n is between H, H.
The prepended (m, v, n, i, ts) in any f+1 node set is true, ensuring that verified (m, s, n, ts) is true. The prepred (m, v, n, i, ts) is true and the master node p accepts 2f+1 AND including itself
M, v, n in the message are identical +.>
A message. Confirm that verified-local (m, v, n, i, ts) is true. After the two conditions are met, if the region is the bottom layer region, the sub stage is entered, and if the region is the top layer region, the Reply stage is entered.
Step 5 (Reply) (top-level region) entering the Reply phase, indicating that the top-level region has reached consensus. Transmitting
For client c, c receives f+1 different signatures<reply,v,d,k,ts> Sig A message representing that the top-level region has reached consensus.
Step three: reputation update stage
The reputation evaluation model comprehensively evaluates the consensus behaviors of all nodes in the consensus process according to the participation consensus frequency of all nodes, the regional node scale, the historical influence degree and the reward and punishment factor:
3) The reputation value of each node is one hundred initially, and the reputation value is updated according to the behaviors of each participant:
definition of 1 node participation consensus frequency: the participation frequency of the node is evaluated according to the participation frequency of the node i in a period of time, and the node i is used for
To express: />
Wherein n is the number of times a node participates in consensus, and alpha and beta are used as adjustable parameters to control
Duty cycle in the reputation evaluation model. It can be seen that the more times n that nodes participate in consensus, the more i nodes are participating in consensus frequencyThe higher the score in terms.
Wherein mu 1 <μ 2 <μ 3 。
in the method, in the process of the invention,
used for representing the consensus situation of the i node in the N round of consensus, m represents the number of rounds of the last round, and +.>
Represents the attenuation factor:
as the consensus advances, the older data has less impact and only the data of the nearest k-cycles can be used for reference, k being an adjustable decay factor-cycle number parameter.
Where λ is used to distinguish the reputation evaluation model results of the primary and secondary nodes, the behavior score of the primary node in the reputation evaluation model score for a successful or failed consensus should be higher for the primary node because the primary node has a more important role in the consensus process.
Definition of 4 reward and punishment factors: reputation evaluation is carried out on the aspect of the round of consensus behavior of the nodes by using reward and punishment factors, and the reputation value of the i node of the nth round rewards and punishment factors
Expressed as:
in the method, in the process of the invention,
representing the reputation value of the node at the n-1 th round of the i node, λ represents an adjustable punishment factor parameter, where g (x) is a normal distribution function, expressed as: />
The normal distribution function is used for successfully achieving consensus that the nodes with high reputation values have lower rewards than the nodes with low reputation values. Meanwhile, the punishment strength of failure consensus of the nodes with high reputation value is larger than that of the nodes with low reputation value. This approach encourages nodes to successfully participate in consensus while avoiding PoS-like harmless attacks.
Reputation value of node i on nth round
Expressed as:
wherein, eta, theta,
lambda is an adjustable parameter of each factor, and can adjust the proportion of each influencing factor in the node reputation evaluation model
Step four: gray region switching stage
4) And inputting reputation evaluation scores obtained by the reputation evaluation model according to the consensus behaviors of the nodes, and replacing the nodes with scores lower than a threshold value and the nodes with the highest reputation evaluation scores in the gray area.
Claims (1)
1. A large-scale PBFT improvement technology based on a reputation evaluation model comprises the following specific steps:
step one: the bottom layer area in the double-layer consensus model achieves consensus;
step two: the bottom layer area uploads the message to the top layer area according to the consensus sequence, and consensus is achieved in the top layer area;
step three: inputting the node consensus behavior into a reputation evaluation model, and outputting reputation evaluation scores of the nodes by the reputation evaluation model;
step four: the possible bayer pattern nodes are placed in gray areas according to the output reputation scores.
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