CN118229289A - Cross-chain virtual currency anonymous transaction tracking method - Google Patents
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Abstract
The invention belongs to the technical field of blockchains, and particularly relates to a cross-chain virtual currency anonymous transaction tracking method, which comprises the following steps of: collecting transaction data of transaction time, transaction amount and anonymous identifiers of both transaction parties on a plurality of blockchain platforms, and storing the transaction data into a data warehouse; encrypting the transaction data in the data warehouse by applying a homomorphic encryption technology to form encrypted transaction data; accessing and integrating the encrypted transaction data of different blockchain platforms by using a cross-chain technology to form a unified encrypted transaction data set; analyzing the encrypted transaction data set by using an on-chain behavior pattern recognition algorithm to recognize a specific transaction behavior pattern; tracking a target anonymous transaction conforming to the specific transaction behavior mode according to the specific transaction behavior mode; in a query system based on the authority obtained by the supervision mechanism, the supervision mechanism executes query operation on the target anonymous transaction data; and the cross-chain technology is utilized to interconnect, communicate and analyze the encrypted transaction data on different blockchain platforms, so that the tracking efficiency is high.
Description
Technical Field
The invention belongs to the technical field of blockchains, and particularly relates to a cross-chain virtual currency anonymous transaction tracking method.
Background
With the rapid development of blockchain technology, a cross-chain technology has developed, so that the problem of interoperability among different blockchains is solved, and the circulation of information and assets among different blockchain platforms is promoted. However, this advancement also presents new challenges, particularly in terms of ensuring transaction privacy security and tracking anonymous transaction behavior. The prior art has a number of disadvantages in these areas, which are mainly manifested in the following aspects:
First, prior art techniques often require decrypting data for analysis and processing when processing cross-chain transaction data, a process that exposes the user's transaction information, resulting in a risk of privacy disclosure. Privacy protection is particularly important in today's digital age, and users are increasingly demanding protection of their own transaction data and personal information. The traditional decryption analysis method cannot effectively monitor and analyze transaction behaviors while protecting user privacy.
Second, the supervision capability in the cross-link environment is insufficient. As blockchain platforms increase, interoperability between different platforms presents regulatory challenges. The prior art has difficulty in achieving efficient transaction tracking and monitoring in a multi-link environment, particularly in the supervision of anonymous or semi-anonymous transactions. This not only increases the concealment of illegal activities such as money laundering, fraud, etc., but also presents a risk to businesses and individuals who operate in compliance.
Again, existing cross-chain techniques have limitations in the interconnection and analysis of data. While some techniques enable sharing and access of data, there is often a lack of effective tools and methods in fully reviewing and analyzing cross-chain transaction data. This limits the overall knowledge of cross-chain virtual currency transaction behavior, reducing the ability to identify potential risks and abnormal transaction behavior.
Therefore, how to provide a cross-chain virtual currency anonymous transaction tracking method is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In order to solve the technical problems, the invention provides a cross-chain virtual currency anonymous transaction tracking method, which aims to solve the problems of ensuring transaction privacy security and tracking anonymous transaction behaviors among different block chains in the prior art.
The technical scheme adopted by the invention is as follows: a cross-chain virtual currency anonymous transaction tracking method comprises the following steps:
S1, collecting transaction data of transaction time, transaction amount and anonymous identifiers of two transaction parties on a plurality of blockchain platforms, and storing the transaction data into a data warehouse;
S2, encrypting the transaction data in the data warehouse by applying a homomorphic encryption technology to form encrypted transaction data;
s3, accessing and integrating the encrypted transaction data of different blockchain platforms by using a cross-chain technology to form a unified encrypted transaction data set;
s4, analyzing the encrypted transaction data set by using an on-chain behavior pattern recognition algorithm, and recognizing a specific transaction behavior pattern;
S5, tracking target anonymous transactions conforming to the specific transaction behavior mode according to the specific transaction behavior mode, and acquiring target anonymous transaction data of the target anonymous transactions;
The target anonymous transaction data includes transaction origin, participants, and transaction paths;
S6, in a query system based on the authority obtained by the supervision institution, the supervision institution executes query operation on the target anonymous transaction data by encrypting the query key;
S7, utilizing a cross-chain technology to interconnect, communicate and analyze the encrypted transaction data on different blockchain platforms.
Further, the identification of the particular transaction behavior pattern includes:
High frequency micropayment pattern recognition for recognizing activities that frequently conduct micropayment;
A time series analysis for identifying abnormal transaction activity within a specific time period;
Transaction network analysis, namely identifying a highly concentrated transaction network by constructing a transaction relation diagram, and suggesting market control behaviors;
Abnormal transaction amount identification, namely identifying transaction behaviors inconsistent with historical data through statistical analysis of transaction amounts.
Further, the query operation performed on the target anonymous transaction data includes:
Allowing the regulatory body to identify all transaction actions within a specified time based on transaction activity queries within a specified time range;
Querying for a particular transaction behavior pattern, including querying for transaction records conforming to the particular transaction behavior pattern;
the query to the target anonymous transaction participant allows the regulatory agency to review the transaction history of the anonymous identifier of the target anonymous transaction when it has sufficient legal basis.
Further, S1 includes:
S101, defining a transaction data model D:
D＝{T,V,PA,PB}；
Wherein, T represents transaction time, V represents transaction amount, and P A and P B respectively represent anonymous identifiers of both transaction parties;
S102, applying a data monitor L to each blockchain platform, wherein the monitor L is responsible for capturing transaction events on the blockchain in real time and extracting transaction data D;
S103, applying a time stamp marking function f T to the captured transaction data D, and adding global uniform time stamps to each transaction data D:
fT(D)＝D′＝{T′,V,PA,PB}；
wherein T' is a standardized unified time format;
S104, filtering non-target transaction data by using a data filter F, wherein the target transaction data is defined as a transaction meeting a specific condition, the transaction amount exceeds a preset threshold value F (D ')=D', and only D 'meeting the condition is reserved as D';
s105, storing the filtered transaction data D "in a predefined data repository W.
Further, S2 includes:
S201, a homomorphic encryption algorithm H based on Paillier is selected, so that data can be encrypted, and meanwhile, the mathematical structure in the data is reserved, so that the specific operation performed on the encrypted data is the same as the result of encryption after the same operation is performed on the original data:
H(m)＝gm·rnmodn2；
where g and n are large primes, r is a random number, and m is a plaintext message;
S202, taking transaction data D 'stored in a data warehouse W as input of a homomorphic encryption algorithm H, and carrying out encryption processing on each transaction data, wherein the encryption process of the transaction data D' is expressed as follows:
E(D″)＝(E(T″),E(V),E(PA),E(PB))＝(H(T″),H(V),H(PA),H(PB))；
The encryption function E is applied to each element of D ", wherein:
Wherein each r X is a random number relative to T ", V, P A and P B;
S203, the data security is enhanced by periodically updating the secret key K of the homomorphic encryption algorithm H, the secret key updating operation is executed by the secret key management system, and the updating process is ensured not to influence the security and accessibility of the encrypted data:
Knew＝F(Kold,Δt)；
Where F key update function, K old is the old key, K new is the new key, Δt is the time variable representing the key update interval.
Further, S3 includes:
s301, integrating the encrypted transaction data to form an encrypted transaction data set E set, which represents all the encrypted transaction data Merge into one set:
S302, ensuring the safe storage and access of an encrypted transaction data set E set, storing E set in a specially designed encrypted data storage system S enc, wherein the system supports the retrieval and management of homomorphic encrypted data:
Wherein key i represents an index key of the encrypted data set, Is a corresponding encrypted transaction data set;
S304, defining a cross-chain data access interface I, wherein the cross-chain data access interface I is used for unifying access modes of encrypted transaction data of different blockchain platforms, allowing to query and retrieve an encrypted transaction data set E set from the different blockchain platforms, including access to the different blockchain platforms P i, and acquiring the encrypted transaction data set
Where P j represents the jth blockchain platform, j ranges from 1 to m, represents all participating blockchain platforms,An access interface representing a jth blockchain platform for acquiring an encrypted transaction dataset of the platform,/>Expressed by/>The method comprises the steps of obtaining an encrypted transaction data set of a jth blockchain platform;
S305, implementing a cross-chain data integration module C, wherein the cross-chain data integration module C is responsible for integrating the encrypted transaction data E set of a plurality of blockchain platforms obtained through the access of the interface I to form a unified encrypted transaction data set E total:
Wherein m is the total number of blockchain platforms, C is a cross-chain data integration module responsible for integrating encrypted transaction data sets acquired from different blockchain platforms into a unified data set, E total is an integrator of encrypted transaction data sets of all blockchain platforms, and all blockchain platforms are integrated Results of the combining are performed;
S306, a data standardization processing function N is applied to perform format and structure standardization processing on the data in the integrated unified encryption transaction data set E total:
Wherein N represents a data standardization processing function, and performs a unified processing of format and structure on the integrated encrypted transaction data set, the unified encrypted transaction data set E norm represents the standardized encrypted transaction data set after N processing, and α j and β represent coefficients used in the standardization processing, for adjusting the format and structure of the data set;
S307, in the cross-chain data integration process, the integrity and the validity of the encrypted transaction data are verified by using a data verification module V, so that the data integrated into E norm are accurate and reliable, only the encrypted transaction data which pass verification are incorporated into E norm, and the data verification module V checks the encrypted signature of each transaction data, and confirms the authenticity and the non-tampered source of each transaction data:
Wherein VERIFYSIG is a verification function for checking whether the signature of the encrypted transaction data set is valid, Is a signature of the encrypted transaction dataset, and PK source is the public key of the data source;
S308, storing the authenticated and standardized unified encrypted transaction data set E norm in the dedicated encrypted data storage system S enc:
Wherein S enc represents an encrypted data storage system supporting secure storage and indexing of encrypted transaction data sets, key i represents a unique key for indexing an ith encrypted transaction data set, hash represents a Hash function for generating a unique index key for each encrypted transaction data set.
Further, S4 includes:
S401, designing an on-chain behavior pattern recognition algorithm A, performing pattern recognition on a unified encryption transaction data set E norm, and analyzing and recognizing a specific transaction behavior pattern under the condition of not decrypting data;
S402, applying the on-chain behavior pattern recognition algorithm A to the unified encryption transaction data set E norm to recognize the following specific transaction behavior patterns:
High frequency micropayment pattern recognition: by analyzing homomorphic encryption values of the transaction frequency F and the transaction amount v, a mode of frequently carrying out small-amount transactions is identified, and a specific operation is allowed to be carried out on encrypted data by combining homomorphic encryption, so that a measurement function F HFSA is defined:
Where E (v i) is the homomorphic encryption value of the ith transaction amount, f i is its corresponding transaction frequency, An operator representing homomorphic encryption support for combining encryption information of transaction amount and transaction frequency;
Time series analysis: the variation of the encrypted timestamp E (T ") over a given time window is analyzed using a function F TSA based on the time-encrypted value:
where E (T "i) is the encrypted timestamp of the ith transaction, delta i represents the weighting factor for each transaction timestamp within the time window, Another operation representing homomorphic encryption support, γ is a constant for adjusting the sensitivity of time series analysis;
Transaction network analysis: constructing an encrypted transaction relation graph, identifying a highly concentrated transaction network by analyzing the connection density and the structure of nodes in the graph, defining a function F TNA, and calculating homomorphic encryption values of the network density based on the encrypted nodes and the side information of the transaction relation graph:
wherein E (G) represents an encrypted transaction relationship graph, E (n) is the total number of encrypted nodes in the graph, E (adj ij) represents an encryption indicator of whether a transaction relationship exists between the nodes i and j, and x represents a multiplication operation supported by homomorphic encryption;
abnormal transaction amount identification: carrying out statistical analysis on the encrypted transaction quantity E (V) to identify transaction behaviors which are obviously inconsistent with the historical data;
S403, based on the identified specific transaction behavior mode, generating an identification result list L identified containing all relevant encrypted transaction data identifiers for subsequent tracking and analysis;
Wherein lambda k is a weight coefficient of each recognition mode for enhancing the importance of the recognition result;
s404, storing the identification result list L identified in a special result storage system S result, and ensuring the safety and accessibility of the identified transaction behavior mode information:
Wherein, Homomorphic encryption of the hash value of the recognition result is performed to enhance the security and privacy protection of information stored in the result storage system.
Further, S5 includes:
s501, utilizing a recognition result list Accessing a corresponding unified encrypted transaction data set E norm to track the anonymous transactions involved;
s502, an application tracking algorithm B for analyzing a transaction chain in a unified encrypted transaction data set E norm, wherein the application tracking algorithm B comprises a transaction origin, a transaction participant and a transaction path, and can track the encrypted transaction data set without decryption by combining data in an encrypted state, and analyze the encrypted transaction data set iteratively to analyze encrypted transaction chain information:
Wherein α, β and γ ij are weighting factors used to emphasize the importance of the transaction start point, end point and specific nodes in the path; Representing a specific iterative operation performed on the homomorphic encryption dataset for analyzing the encryption path information;
s503, by analyzing the encryption information of the transaction Path i, introducing an aggregation function C path, and evaluating and determining a transaction link while keeping data encryption:
Wherein m i is the number of nodes in the transaction path, delta k and epsilon k are weighting coefficients set for each node, lambda is a constant for adjusting the sensitivity of the whole path analysis, and alpha represents a special homomorphic operator for path aggregation analysis;
S504, generating a tracking report R i for each tracked target anonymous transaction, wherein the tracking report R i comprises encryption information of transaction origins, participants and paths and detailed information of identified specific transaction behavior modes, and the generation of the tracking report R i involves the comprehensive representation of the encryption information and the identification modes:
Where Info ij represents encrypted information of transaction origin, destination and path, ζ j is corresponding weighting factor; mode im represents the encrypted transaction behavior pattern identification, η m is the corresponding weight coefficient, Representing a specific homomorphic multiplication for synthesizing report content;
s505, storing the generated tracking report R i in a tracking result storage system S track, ensuring the security and privacy of the tracking result, and simultaneously facilitating authorized access:
Where σ is a weighting factor for enhancing storage security, q i is the number of elements in the ith trace report, Representing a specific homomorphic encryption operation performed on the trace report content to ensure security and privacy protection of the trace information during storage.
Further, S7 includes:
S701, constructing a cross-chain technical framework F cross, wherein the cross-chain technical framework F cross supports interconnection, intercommunication and analysis of unified encrypted transaction data sets E norm on different blockchain platforms:
Wherein N represents the number of participating blockchain platforms, M i represents the number of transaction data in the ith blockchain platform, λ ij and k ij represent a weighting factor and an exponential factor, respectively, for adjusting the influence of each transaction data, and Θ cross is a complexity adjustment factor for enhancing the flexibility and adaptability of the framework;
S702, defining a cross-link data exchange protocol P exchange, wherein the protocol prescribes a unified encryption transaction data set Format, security requirements, and access control mechanisms for transmissions between different blockchain platforms:
Wherein, delta ij and gamma ij respectively represent a security adjustment factor and a weighting index of each encrypted transaction data, and ψ security is an overall security weighting factor for enhancing security in the data exchange process;
S703, executing interconnection and interworking of the unified encryption transaction dataset E norm through a cross-chain technology framework F cross, and using The protocol handles data exchange between different platforms, access and analysis of global encrypted transaction data:
Wherein, Representing the execution result of the ith data exchange protocol, Φ integration is a complexity adjustment factor of cross-chain integration, and is used for optimizing the integration process of data;
S704, performing comprehensive data examination and behavior pattern recognition on the encrypted transaction data set after cross-chain integration by using the comprehensive analysis module A analysis, and performing deep behavior pattern analysis by using the cross-chain data set to recognize potential risks and abnormal transaction behaviors:
Wherein, Representing an analysis function of the ith block chain platform jth transaction data, wherein rho ij and sigma ij are complexity adjustment factors and weighting indexes introduced in the analysis process respectively, so that the accuracy and depth of analysis are improved;
S705, generating a cross-chain analysis report R cross, summarizing all transaction behavior patterns and risks identified by a cross-chain technical framework and a comprehensive analysis module, wherein the report contains comprehensive analysis results and recommended measures for a global encrypted transaction data set:
Wherein Analysis ij represents the Analysis content of the j transaction data of the i-th blockchain platform, omega i is the weighting factor of the whole report, v ij and tau ij represent the complexity adjustment factor and the weighting index of each Analysis result respectively, and are used for strengthening the detail and the depth of the report content.
The invention has the beneficial effects that:
1. By applying homomorphic encryption technology, the encrypted transaction data is analyzed and processed on the premise of not decrypting the data, so that the privacy protection level of the user transaction data is improved, the transaction information of the user cannot be revealed even in the cross-chain analysis and monitoring process, and the problem that the supervision and management institution is difficult to trace and trace sources is effectively solved.
2. According to the invention, through the fusion of the on-chain behavior pattern recognition technology and the cross-chain technology, anonymous transactions can be tracked across different blockchain platforms, and comprehensive behavior pattern analysis can be performed on the transactions, so that the supervision capability and flexibility of a supervision mechanism under a multi-chain environment are enhanced, and a powerful tool is provided for the attack of illegal behaviors such as money laundering and fraud.
3. The invention combines the comprehensive analysis module and the cross-chain technology framework, can realize the access, integration and comprehensive examination of global encrypted transaction data, so that a supervision organization and an analyst can more deeply understand the behavior mode of cross-chain virtual currency transaction, and timely identify and respond potential risks and abnormal transaction behaviors.
Drawings
FIG. 1 is a block flow diagram of a cross-chain virtual currency anonymous transaction tracking method of the present invention.
Detailed Description
Embodiments of the present invention are described in further detail below with reference to the accompanying drawings and examples. The following examples are illustrative of the invention but are not intended to limit the scope of the invention.
In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front," "rear," "head," "tail," and the like are used as an orientation or positional relationship based on that shown in the drawings, merely to facilitate description of the invention and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore should not be construed as limiting the invention. Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "connected," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Embodiment one:
As shown in fig. 1, a cross-chain virtual currency anonymous transaction tracking method includes the following steps:
S1, collecting transaction data of transaction time, transaction amount and anonymous identifiers of two transaction parties on a plurality of blockchain platforms, and storing the transaction data into a data warehouse;
S2, encrypting the transaction data in the data warehouse by applying a homomorphic encryption technology to form encrypted transaction data;
s3, accessing and integrating the encrypted transaction data of different blockchain platforms by using a cross-chain technology to form a unified encrypted transaction data set;
s4, analyzing the encrypted transaction data set by using an on-chain behavior pattern recognition algorithm, and recognizing a specific transaction behavior pattern;
S5, tracking target anonymous transactions conforming to the specific transaction behavior mode according to the specific transaction behavior mode, and acquiring target anonymous transaction data of the target anonymous transactions;
The target anonymous transaction data includes transaction origin, participants, and transaction paths;
S6, in a query system based on the authority obtained by the supervision institution, the supervision institution executes query operation on the target anonymous transaction data by encrypting the query key;
S7, utilizing a cross-chain technology to interconnect, communicate and analyze the encrypted transaction data on different blockchain platforms.
In the embodiment, the scheme processes the cross-link data from S1 to S3, and analyzes and processes the encrypted transaction data on the premise of not decrypting the data by applying the homomorphic encryption technology, so that the privacy protection level of the user transaction data is improved, the transaction information of the user cannot be leaked even in the cross-link analysis and monitoring process, and the problem of difficult tracing by a supervision mechanism is effectively solved; according to the scheme, data are analyzed and tracked from S4 to S5, anonymous transactions can be tracked across different blockchain platforms through a fused chain behavior pattern recognition technology and a cross-chain technology, comprehensive behavior pattern analysis can be performed on the transactions, the supervision capability and flexibility of a supervision mechanism under a multi-chain environment are enhanced, and powerful tools are provided for striking illegal behaviors such as money laundering and fraud; according to the scheme, the data are queried and inspected from S6 to S7, and the comprehensive analysis module and the cross-chain technology framework are combined, so that the access, integration and comprehensive inspection of global encrypted transaction data can be realized, and a supervision organization and an analyst can know the behavior mode of the cross-chain virtual currency transaction more deeply, and identify and respond potential risks and abnormal transaction behaviors in time.
Preferably, the identifying of the specific transaction behavior pattern includes:
High frequency micropayment pattern recognition for recognizing activities that frequently conduct micropayment;
A time series analysis for identifying abnormal transaction activity within a specific time period;
Transaction network analysis, namely identifying a highly concentrated transaction network by constructing a transaction relation diagram, and suggesting market control behaviors;
Abnormal transaction amount identification, namely identifying transaction behaviors inconsistent with historical data through statistical analysis of transaction amounts. The transaction behavior patterns are identified and analyzed through different dimensions of high-frequency small-amount transaction pattern identification, time sequence analysis, transaction network analysis and abnormal transaction amount identification, so that anonymous transactions can be tracked across different blockchain platforms, comprehensive behavior pattern analysis can be performed on the transactions, and the supervision capability and flexibility of a supervision mechanism in a multi-chain environment are enhanced.
Preferably, the query operation performed on the target anonymous transaction data includes:
Allowing the regulatory body to identify all transaction actions within a specified time based on transaction activity queries within a specified time range;
Querying for a particular transaction behavior pattern, including querying for transaction records conforming to the particular transaction behavior pattern;
The query to the target anonymous transaction participant allows the regulatory agency to review the transaction history of the anonymous identifier of the target anonymous transaction when it has sufficient legal basis. Under the condition of having enough legal basis, the supervision organization inquires and examines the tracked target anonymous transaction, so that the access, integration and comprehensive examination of global encrypted transaction data can be realized, and the supervision organization and an analyst can know the behavior mode of the cross-chain virtual currency transaction more deeply, and identify and respond potential risks and abnormal transaction behaviors in time.
Preferably, S1 includes:
S101, defining a transaction data model D:
D＝{T,V,PA,PB}；
Wherein, T represents transaction time, V represents transaction amount, and P A and P B respectively represent anonymous identifiers of both transaction parties;
S102, applying a data monitor L to each blockchain platform, wherein the monitor L is responsible for capturing transaction events on the blockchain in real time and extracting transaction data D;
S103, applying a time stamp marking function f T to the captured transaction data D, and adding global uniform time stamps to each transaction data D:
fT(D)＝D′＝{T′,V,PA,PB}；
wherein T' is a standardized unified time format;
S104, filtering non-target transaction data by using a data filter F, wherein the target transaction data is defined as a transaction meeting a specific condition, the transaction amount exceeds a preset threshold value F (D ')=D', and only D 'meeting the condition is reserved as D';
s105, storing the filtered transaction data D "in a predefined data repository W.
Preferably, S2 includes:
S201, a homomorphic encryption algorithm H based on Paillier is selected, so that data can be encrypted, and meanwhile, the mathematical structure in the data is reserved, so that the specific operation performed on the encrypted data is the same as the result of encryption after the same operation is performed on the original data:
H(m)＝gm·rnmodn2；
where g and n are large primes, r is a random number, and m is a plaintext message;
S202, taking transaction data D 'stored in a data warehouse W as input of a homomorphic encryption algorithm H, and carrying out encryption processing on each transaction data, wherein the encryption process of the transaction data D' is expressed as follows:
E(D″)＝(E(T″),E(V),E(PA),E(PB))＝(H(T″),H(V),H(PA),H(PB))；
The encryption function E is applied to each element of D ", wherein:
wherein each rX is a random number relative to T ", V, PA and PB;
S203, the data security is enhanced by periodically updating the secret key K of the homomorphic encryption algorithm H, the secret key updating operation is executed by the secret key management system, and the updating process is ensured not to influence the security and accessibility of the encrypted data:
Knew＝F(Kold,Δt)；
Where F key update function, K old is the old key, K new is the new key, Δt is the time variable representing the key update interval.
Preferably, S3 includes:
s301, integrating the encrypted transaction data to form an encrypted transaction data set E set, which represents all the encrypted transaction data Merge into one set:
S302, ensuring the safe storage and access of an encrypted transaction data set E set, storing E set in a specially designed encrypted data storage system S enc, wherein the system supports the retrieval and management of homomorphic encrypted data:
Wherein key i represents an index key of the encrypted data set, Is a corresponding encrypted transaction data set;
S304, defining a cross-chain data access interface I, wherein the cross-chain data access interface I is used for unifying access modes of encrypted transaction data of different blockchain platforms, allowing to query and retrieve an encrypted transaction data set E set from the different blockchain platforms, including access to the different blockchain platforms P i, and acquiring the encrypted transaction data set
Where P j represents the jth blockchain platform, j ranges from 1 to m, represents all participating blockchain platforms,An access interface representing a jth blockchain platform for acquiring an encrypted transaction dataset of the platform,/>Expressed by/>The method comprises the steps of obtaining an encrypted transaction data set of a jth blockchain platform;
S305, implementing a cross-chain data integration module C, wherein the cross-chain data integration module C is responsible for integrating the encrypted transaction data E set of a plurality of blockchain platforms obtained through the access of the interface I to form a unified encrypted transaction data set E total:
Wherein m is the total number of blockchain platforms, C is a cross-chain data integration module responsible for integrating encrypted transaction data sets acquired from different blockchain platforms into a unified data set, E total is an integrator of encrypted transaction data sets of all blockchain platforms, and all blockchain platforms are integrated Results of the combining are performed;
S306, a data standardization processing function N is applied to perform format and structure standardization processing on the data in the integrated unified encryption transaction data set E total:
Wherein N represents a data standardization processing function, and performs a unified processing of format and structure on the integrated encrypted transaction data set, the unified encrypted transaction data set E norm represents the standardized encrypted transaction data set after N processing, and α j and β represent coefficients used in the standardization processing, for adjusting the format and structure of the data set;
S307, in the cross-chain data integration process, the integrity and the validity of the encrypted transaction data are verified by using a data verification module V, so that the data integrated into E norm are accurate and reliable, only the encrypted transaction data which pass verification are incorporated into E norm, and the data verification module V checks the encrypted signature of each transaction data, and confirms the authenticity and the non-tampered source of each transaction data:
Wherein VERIFYSIG is a verification function for checking whether the signature of the encrypted transaction data set is valid, Is a signature of the encrypted transaction dataset, and PK source is the public key of the data source;
S308, storing the authenticated and standardized unified encrypted transaction data set E norm in the dedicated encrypted data storage system S enc:
Wherein S enc represents an encrypted data storage system supporting secure storage and indexing of encrypted transaction data sets, key i represents a unique key for indexing an ith encrypted transaction data set, hash represents a Hash function for generating a unique index key for each encrypted transaction data set.
Preferably, S4 includes:
S401, designing an on-chain behavior pattern recognition algorithm A, performing pattern recognition on a unified encryption transaction data set E norm, and analyzing and recognizing a specific transaction behavior pattern under the condition of not decrypting data;
S402, applying the on-chain behavior pattern recognition algorithm A to the unified encryption transaction data set E norm to recognize the following specific transaction behavior patterns:
High frequency micropayment pattern recognition: by analyzing homomorphic encryption values of the transaction frequency F and the transaction amount v, a mode of frequently carrying out small-amount transactions is identified, and a specific operation is allowed to be carried out on encrypted data by combining homomorphic encryption, so that a measurement function F HFSA is defined:
Where E (v i) is the homomorphic encryption value of the ith transaction amount, f i is its corresponding transaction frequency, An operator representing homomorphic encryption support for combining encryption information of transaction amount and transaction frequency;
Time series analysis: the variation of the encrypted timestamp E (T ") over a given time window is analyzed using a function F TSA based on the time-encrypted value:
where E (T "i) is the encrypted timestamp of the ith transaction, delta i represents the weighting factor for each transaction timestamp within the time window, Another operation representing homomorphic encryption support, γ is a constant for adjusting the sensitivity of time series analysis;
Transaction network analysis: constructing an encrypted transaction relation graph, identifying a highly concentrated transaction network by analyzing the connection density and the structure of nodes in the graph, defining a function F TNA, and calculating homomorphic encryption values of the network density based on the encrypted nodes and the side information of the transaction relation graph:
wherein E (G) represents an encrypted transaction relationship graph, E (n) is the total number of encrypted nodes in the graph, E (adj ij) represents an encryption indicator of whether a transaction relationship exists between the nodes i and j, and x represents a multiplication operation supported by homomorphic encryption;
abnormal transaction amount identification: carrying out statistical analysis on the encrypted transaction quantity E (V) to identify transaction behaviors which are obviously inconsistent with the historical data;
S403, based on the identified specific transaction behavior mode, generating an identification result list L identified containing all relevant encrypted transaction data identifiers for subsequent tracking and analysis;
Wherein lambda k is a weight coefficient of each recognition mode for enhancing the importance of the recognition result;
s404, storing the identification result list L identified in a special result storage system S result, and ensuring the safety and accessibility of the identified transaction behavior mode information:
Wherein, Homomorphic encryption of the hash value of the recognition result is performed to enhance the security and privacy protection of information stored in the result storage system.
Preferably, S5 includes:
s501, utilizing a recognition result list Accessing a corresponding unified encrypted transaction data set E norm to track the anonymous transactions involved;
s502, an application tracking algorithm B for analyzing a transaction chain in a unified encrypted transaction data set E norm, wherein the application tracking algorithm B comprises a transaction origin, a transaction participant and a transaction path, and can track the encrypted transaction data set without decryption by combining data in an encrypted state, and analyze the encrypted transaction data set iteratively to analyze encrypted transaction chain information:
Wherein α, β and γ ij are weighting factors used to emphasize the importance of the transaction start point, end point and specific nodes in the path; Representing a specific iterative operation performed on the homomorphic encryption dataset for analyzing the encryption path information;
s503, by analyzing the encryption information of the transaction Path i, introducing an aggregation function C path, and evaluating and determining a transaction link while keeping data encryption:
Wherein m i is the number of nodes in the transaction path, delta k and epsilon k are weighting coefficients set for each node, lambda is a constant for adjusting the sensitivity of the whole path analysis, and alpha represents a special homomorphic operator for path aggregation analysis;
S504, generating a tracking report R i for each tracked target anonymous transaction, wherein the tracking report R i comprises encryption information of transaction origins, participants and paths and detailed information of identified specific transaction behavior modes, and the generation of the tracking report R i involves the comprehensive representation of the encryption information and the identification modes:
Where Info ij represents encrypted information of transaction origin, destination and path, ζ j is corresponding weighting factor; mode im represents the encrypted transaction behavior pattern identification, η m is the corresponding weight coefficient, Representing a specific homomorphic multiplication for synthesizing report content;
s505, storing the generated tracking report R i in a tracking result storage system S track, ensuring the security and privacy of the tracking result, and simultaneously facilitating authorized access:
Where σ is a weighting factor for enhancing storage security, q i is the number of elements in the ith trace report, Representing a specific homomorphic encryption operation performed on the trace report content to ensure security and privacy protection of the trace information during storage.
Preferably, S7 includes:
S701, constructing a cross-chain technical framework F cross, wherein the cross-chain technical framework E cross supports interconnection, intercommunication and analysis of unified encrypted transaction data sets E norm on different blockchain platforms:
Wherein N represents the number of participating blockchain platforms, M i represents the number of transaction data in the ith blockchain platform, λ ij and k ij represent a weighting factor and an exponential factor, respectively, for adjusting the influence of each transaction data, and Θ cross is a complexity adjustment factor for enhancing the flexibility and adaptability of the framework;
S702, defining a cross-link data exchange protocol P exchange, wherein the protocol prescribes a unified encryption transaction data set Format, security requirements, and access control mechanisms for transmissions between different blockchain platforms:
Wherein, delta ij and gamma ij respectively represent a security adjustment factor and a weighting index of each encrypted transaction data, and ψ security is an overall security weighting factor for enhancing security in the data exchange process;
S703, executing interconnection and interworking of the unified encryption transaction dataset E norm through a cross-chain technology framework F cross, and using The protocol handles data exchange between different platforms, access and analysis of global encrypted transaction data:
Wherein, Representing the execution result of the ith data exchange protocol, Φ integration is a complexity adjustment factor of cross-chain integration, and is used for optimizing the integration process of data;
S704, performing comprehensive data examination and behavior pattern recognition on the encrypted transaction data set after cross-chain integration by using the comprehensive analysis module A analysis, and performing deep behavior pattern analysis by using the cross-chain data set to recognize potential risks and abnormal transaction behaviors:
Wherein, Representing an analysis function of the ith block chain platform jth transaction data, wherein rho ij and sigma ij are complexity adjustment factors and weighting indexes introduced in the analysis process respectively, so that the accuracy and depth of analysis are improved;
S705, generating a cross-chain analysis report R cross, summarizing all transaction behavior patterns and risks identified by a cross-chain technical framework and a comprehensive analysis module, wherein the report contains comprehensive analysis results and recommended measures for a global encrypted transaction data set:
Wherein ANALLYSIS ij represents the analysis content of the jth transaction data of the ith blockchain platform, ω i is a weighting factor of the overall report, and ν ij and τ ij represent complexity adjustment factors and weighting indexes of each analysis result, respectively, for enhancing the detail and depth of the report content.
In this embodiment, the method further includes:
Fund flow direction query for specific address: a function is added in the monitoring inquiry system, so that the supervision authorities can execute inquiry aiming at specific encrypted addresses, and the fund inflow and outflow conditions of the addresses are displayed. Including ordering the flow of funds to enable the transaction amount to be displayed from large to small so that the maximum funds transfer activity can be quickly identified and the final movement of funds tracked. The supervision authorities are helped to effectively identify the flow condition of large funds, and further analyze the potential risk behind the supervision authorities or the unfair behavior;
Visual presentation of multi-currency and cross-chain transactions: the system is improved so that the system can visually display the conversion and the cross-chain transaction between different currencies carried out by one address. By visually exhibiting such transactions, the regulatory authorities track and analyze the flow path of funds, identifying potential money laundering or other illegal activities;
Transaction association query: allowing the regulatory agency to query all transaction records between two specific addresses. Including the amount of the transaction, time and associated encryption information. Through such associative queries, the regulatory body can learn in detail the flow of funds between two or more related addresses;
Entity labeling of mass transaction addresses: and adding a function of entity labeling for mass transaction addresses into the system. Entity tagging refers to associating a transaction address with an entity in the real world (e.g., a person, company, or other organization). The recognition capability of the supervision authorities on entities behind the transaction is improved, and particularly when large-scale or complex cases are investigated, the case-related entities and the transaction network thereof can be rapidly positioned.
The working mode of the invention is as follows:
In use, in this embodiment, a virtual currency transaction platform named "CrossBridge" is used as a background, and the main challenge faced by this platform is how to effectively track anonymous transactions that are performed in cross-chain operations and currency shuffling.
"CrossBridge" is a transaction platform supporting multiple cryptocurrencies and blockchains that allows users to conduct cross-chain transactions. While this provides great convenience to the user, it also provides a multiplicative opportunity for illegal activities. Some lawbreakers utilize this for coin shuffling, i.e. by continuous cross-chain transfer, making tracking the flow of funds extremely difficult. Without effective tools, conventional tracking methods are not only time consuming, but often fail to achieve the desired tracking effect.
In order to solve the problem, a cross-chain virtual currency anonymous transaction tracking method in the scheme is adopted based on a 'CrossBridge' platform.
First, a data collection module is deployed on the platform "CrossBridge" to capture all relevant information for cross-chain transactions in real-time, including time, amount of transactions and anonymous accounts involved. Then, the transaction data are encrypted through homomorphic encryption technology, so that the privacy of the user is ensured to be protected in the subsequent processing process.
Next, encrypted transaction data from the different blockchains are integrated using cross-chain techniques and analyzed using on-chain behavior pattern recognition algorithms to identify possible coin shuffling behaviors. Once a particular transaction pattern, such as a high frequency micropayment pattern, is identified, a tracking procedure is initiated to track the anonymous transaction involved by analyzing the transaction path and the participants.
The tracking work on the CrossBridge platform using the prior art takes an average of 5 hours to complete and has a low success rate. After the method is adopted, the tracking is finished on a CrossBridge platform only by 5 to 10 minutes on average, and the tracking efficiency is improved by nearly 30 times.
Specifically, by adopting the cross-chain virtual currency anonymous transaction tracking method, an account suspected of being subjected to currency shuffling is monitored through a CrossBridge platform, and in a short period of 2 hours, small transactions of more than 50 times are performed through 5 different blockchains. By applying the method of the invention, the abnormal behavior pattern is rapidly identified, the final receiving account is tracked by the on-chain behavior pattern identification algorithm, and all involved transaction paths are locked.
The present invention has been described in detail above. The description of the specific embodiments is only intended to aid in understanding the method of the present invention and its core ideas. It should be noted that it will be apparent to those skilled in the art that various modifications and adaptations of the invention can be made without departing from the principles of the invention and these modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.
Claims (9)
1. The cross-chain virtual currency anonymous transaction tracking method is characterized by comprising the following steps of:
S1, collecting transaction data of transaction time, transaction amount and anonymous identifiers of two transaction parties on a plurality of blockchain platforms, and storing the transaction data into a data warehouse;
S2, encrypting the transaction data in the data warehouse by applying a homomorphic encryption technology to form encrypted transaction data;
s3, accessing and integrating the encrypted transaction data of different blockchain platforms by using a cross-chain technology to form a unified encrypted transaction data set;
s4, analyzing the encrypted transaction data set by using an on-chain behavior pattern recognition algorithm, and recognizing a specific transaction behavior pattern;
S5, tracking target anonymous transactions conforming to the specific transaction behavior mode according to the specific transaction behavior mode, and acquiring target anonymous transaction data of the target anonymous transactions;
The target anonymous transaction data includes transaction origin, participants, and transaction paths;
S6, in a query system based on the authority obtained by the supervision institution, the supervision institution executes query operation on the target anonymous transaction data by encrypting the query key;
S7, utilizing a cross-chain technology to interconnect, communicate and analyze the encrypted transaction data on different blockchain platforms.
2. The method of claim 1, wherein the identifying of the particular transaction behavior pattern comprises:
High frequency micropayment pattern recognition for recognizing activities that frequently conduct micropayment;
A time series analysis for identifying abnormal transaction activity within a specific time period;
Transaction network analysis, namely identifying a highly concentrated transaction network by constructing a transaction relation diagram, and suggesting market control behaviors;
Abnormal transaction amount identification, namely identifying transaction behaviors inconsistent with historical data through statistical analysis of transaction amounts.
3. The method of claim 1, wherein the query performed on the target anonymous transaction data comprises:
Allowing the regulatory body to identify all transaction actions within a specified time based on transaction activity queries within a specified time range;
Querying for a particular transaction behavior pattern, including querying for transaction records conforming to the particular transaction behavior pattern;
the query to the target anonymous transaction participant allows the regulatory agency to review the transaction history of the anonymous identifier of the target anonymous transaction when it has sufficient legal basis.
4. The method for tracking anonymous transactions across a chain of virtual currencies of claim 1, wherein S1 comprises:
S101, defining a transaction data model D:
D＝{T,V,PA,PB}；
Wherein, T represents transaction time, V represents transaction amount, and P A and P B respectively represent anonymous identifiers of both transaction parties;
S102, applying a data monitor L to each blockchain platform, wherein the monitor L is responsible for capturing transaction events on the blockchain in real time and extracting transaction data D;
S103, applying a time stamp marking function f T to the captured transaction data D, and adding global uniform time stamps to each transaction data D:
fT(D)＝D′＝{T′,V,PA,PB}；
wherein T' is a standardized unified time format;
S104, filtering non-target transaction data by using a data filter F, wherein the target transaction data is defined as a transaction meeting a specific condition, the transaction amount exceeds a preset threshold value F (D ')=D', and only D 'meeting the condition is reserved as D';
s105, storing the filtered transaction data D "in a predefined data repository W.
5. The method for tracking anonymous transactions across a chain of virtual currencies of claim 4, wherein S2 comprises:
S201, a homomorphic encryption algorithm H based on Paillier is selected, so that data can be encrypted, and meanwhile, the mathematical structure in the data is reserved, so that the specific operation performed on the encrypted data is the same as the result of encryption after the same operation is performed on the original data:
H(m)＝gm·rnmodn2；
where g and n are large primes, r is a random number, and m is a plaintext message;
S202, taking transaction data D 'stored in a data warehouse W as input of a homomorphic encryption algorithm H, and carrying out encryption processing on each transaction data, wherein the encryption process of the transaction data D' is expressed as follows:
E(D″)＝(E(T″),E(V),E(PA),E(PB))＝(H(T″),H(V),H(PA),H(PB));
The encryption function E is applied to each element of D ", wherein:
Wherein each r X is a random number relative to T ", V, P A and P B;
S203, the data security is enhanced by periodically updating the secret key K of the homomorphic encryption algorithm H, the secret key updating operation is executed by the secret key management system, and the updating process is ensured not to influence the security and accessibility of the encrypted data:
Knew＝F(Kold,Δt)；
Where F key update function, K old is the old key, K new is the new key, Δt is the time variable representing the key update interval.
6. The method for tracking anonymous transactions across a chain of virtual currencies of claim 5, wherein S3 comprises:
s301, integrating the encrypted transaction data to form an encrypted transaction data set E set, which represents all the encrypted transaction data Merge into one set:
S302, ensuring the safe storage and access of an encrypted transaction data set E set, storing E set in a specially designed encrypted data storage system S enc, wherein the system supports the retrieval and management of homomorphic encrypted data:
Wherein key i represents an index key of the encrypted data set, Is a corresponding encrypted transaction data set;
S304, defining a cross-chain data access interface I, wherein the cross-chain data access interface I is used for unifying access modes of encrypted transaction data of different blockchain platforms, allowing to query and retrieve an encrypted transaction data set E set from the different blockchain platforms, including access to the different blockchain platforms P i, and acquiring the encrypted transaction data set
Where P j represents the jth blockchain platform, j ranges from 1 to m, represents all participating blockchain platforms,An access interface representing a jth blockchain platform for acquiring an encrypted transaction dataset of the platform,/>Representation byThe method comprises the steps of obtaining an encrypted transaction data set of a jth blockchain platform;
S305, implementing a cross-chain data integration module C, wherein the cross-chain data integration module C is responsible for integrating the encrypted transaction data E set of a plurality of blockchain platforms obtained through the access of the interface I to form a unified encrypted transaction data set E total:
Wherein m is the total number of blockchain platforms, C is a cross-chain data integration module responsible for integrating encrypted transaction data sets acquired from different blockchain platforms into a unified data set, E total is an integrator of encrypted transaction data sets of all blockchain platforms, and all blockchain platforms are integrated Results of the combining are performed;
S306, a data standardization processing function N is applied to perform format and structure standardization processing on the data in the integrated unified encryption transaction data set E total:
Wherein N represents a data standardization processing function, and performs a unified processing of format and structure on the integrated encrypted transaction data set, the unified encrypted transaction data set E norm represents the standardized encrypted transaction data set after N processing, and α j and β represent coefficients used in the standardization processing, for adjusting the format and structure of the data set;
S307, in the cross-chain data integration process, the integrity and the validity of the encrypted transaction data are verified by using a data verification module V, so that the data integrated into E norm are accurate and reliable, only the encrypted transaction data which pass verification are incorporated into E norm, and the data verification module V checks the encrypted signature of each transaction data, and confirms the authenticity and the non-tampered source of each transaction data:
Wherein VERIFYSIG is a verification function for checking whether the signature of the encrypted transaction data set is valid, Is a signature of the encrypted transaction dataset, and PK source is the public key of the data source;
S308, storing the authenticated and standardized unified encrypted transaction data set E norm in the dedicated encrypted data storage system S enc:
Wherein S enc represents an encrypted data storage system supporting secure storage and indexing of encrypted transaction data sets, key i represents a unique key for indexing an ith encrypted transaction data set, hash represents a Hash function for generating a unique index key for each encrypted transaction data set.
7. The method of cross-chain virtual currency anonymous transaction tracking as defined in claim 6, wherein S4 comprises:
S401, designing an on-chain behavior pattern recognition algorithm A, performing pattern recognition on a unified encryption transaction data set E norm, and analyzing and recognizing a specific transaction behavior pattern under the condition of not decrypting data;
S402, applying the on-chain behavior pattern recognition algorithm A to the unified encryption transaction data set E norm to recognize the following specific transaction behavior patterns:
High frequency micropayment pattern recognition: by analyzing homomorphic encryption values of the transaction frequency F and the transaction amount v, a mode of frequently carrying out small-amount transactions is identified, and a specific operation is allowed to be carried out on encrypted data by combining homomorphic encryption, so that a measurement function F HFSA is defined:
Where E (v i) is the homomorphic encryption value of the ith transaction amount, f i is its corresponding transaction frequency, An operator representing homomorphic encryption support for combining encryption information of transaction amount and transaction frequency;
Time series analysis: the variation of the encrypted timestamp E (T ") over a given time window is analyzed using a function F TSA based on the time-encrypted value:
where E (T "i) is the encrypted timestamp of the ith transaction, delta i represents the weighting factor for each transaction timestamp within the time window, Another operation representing homomorphic encryption support, γ is a constant for adjusting the sensitivity of time series analysis;
Transaction network analysis: constructing an encrypted transaction relation graph, identifying a highly concentrated transaction network by analyzing the connection density and the structure of nodes in the graph, defining a function F TNA, and calculating homomorphic encryption values of the network density based on the encrypted nodes and the side information of the transaction relation graph:
wherein E (G) represents an encrypted transaction relationship graph, E (n) is the total number of encrypted nodes in the graph, E (adj ij) represents an encryption indicator of whether a transaction relationship exists between the nodes i and j, and x represents a multiplication operation supported by homomorphic encryption;
abnormal transaction amount identification: carrying out statistical analysis on the encrypted transaction quantity E (V) to identify transaction behaviors which are obviously inconsistent with the historical data;
S403, based on the identified specific transaction behavior mode, generating an identification result list L identified containing all relevant encrypted transaction data identifiers for subsequent tracking and analysis;
Wherein lambda k is a weight coefficient of each recognition mode for enhancing the importance of the recognition result;
s404, storing the identification result list L identified in a special result storage system S result, and ensuring the safety and accessibility of the identified transaction behavior mode information:
Wherein, Homomorphic encryption of the hash value of the recognition result is performed to enhance the security and privacy protection of information stored in the result storage system.
8. The method for tracking anonymous transactions across a chain of virtual currencies of claim 7, wherein S5 comprises:
s501, utilizing a recognition result list Accessing a corresponding unified encrypted transaction data set E norm to track the anonymous transactions involved;
s502, an application tracking algorithm B for analyzing a transaction chain in a unified encrypted transaction data set E norm, wherein the application tracking algorithm B comprises a transaction origin, a transaction participant and a transaction path, and can track the encrypted transaction data set without decryption by combining data in an encrypted state, and analyze the encrypted transaction data set iteratively to analyze encrypted transaction chain information:
Wherein α, β and γ ij are weighting factors used to emphasize the importance of the transaction start point, end point and specific nodes in the path; Representing a specific iterative operation performed on the homomorphic encryption dataset for analyzing the encryption path information;
s503, by analyzing the encryption information of the transaction Path i, introducing an aggregation function C path, and evaluating and determining a transaction link while keeping data encryption:
Wherein m i is the number of nodes in the transaction path, delta k and epsilon k are weighting coefficients set for each node, lambda is a constant for adjusting the sensitivity of the whole path analysis, and alpha represents a special homomorphic operator for path aggregation analysis;
S504, generating a tracking report R i for each tracked target anonymous transaction, wherein the tracking report R i comprises encryption information of transaction origins, participants and paths and detailed information of identified specific transaction behavior modes, and the generation of the tracking report R i involves the comprehensive representation of the encryption information and the identification modes:
Where Info ij represents encrypted information of transaction origin, destination and path, ζ j is corresponding weighting factor; mode im represents the encrypted transaction behavior pattern identification, η m is the corresponding weight coefficient, Representing a specific homomorphic multiplication for synthesizing report content;
s505, storing the generated tracking report R i in a tracking result storage system S track, ensuring the security and privacy of the tracking result, and simultaneously facilitating authorized access:
Where σ is a weighting factor for enhancing storage security, q i is the number of elements in the ith trace report, Representing a specific homomorphic encryption operation performed on the trace report content to ensure security and privacy protection of the trace information during storage.
9. The method for tracking anonymous transactions across a chain of virtual currencies of claim 8, wherein S7 comprises:
S701, constructing a cross-chain technical framework F cross, wherein the cross-chain technical framework F cross supports interconnection, intercommunication and analysis of unified encrypted transaction data sets E norm on different blockchain platforms:
Wherein N represents the number of participating blockchain platforms, M i represents the number of transaction data in the ith blockchain platform, λ ij and k ij represent a weighting factor and an exponential factor, respectively, for adjusting the influence of each transaction data, and Θ cross is a complexity adjustment factor for enhancing the flexibility and adaptability of the framework;
S702, defining a cross-link data exchange protocol P exchange, wherein the protocol prescribes a unified encryption transaction data set Format, security requirements, and access control mechanisms for transmissions between different blockchain platforms:
Wherein, delta ij and gamma ij respectively represent a security adjustment factor and a weighting index of each encrypted transaction data, and ψ security is an overall security weighting factor for enhancing security in the data exchange process;
S703, executing interconnection and interworking of the unified encryption transaction dataset E norm through a cross-chain technology framework F corss, and using The protocol handles data exchange between different platforms, access and analysis of global encrypted transaction data:
Wherein, Representing the execution result of the ith data exchange protocol, Φ integration is a complexity adjustment factor of cross-chain integration, and is used for optimizing the integration process of data;
S704, performing comprehensive data examination and behavior pattern recognition on the encrypted transaction data set after cross-chain integration by using the comprehensive analysis module A analysis, and performing deep behavior pattern analysis by using the cross-chain data set to recognize potential risks and abnormal transaction behaviors:
Wherein, Representing an analysis function of the ith block chain platform jth transaction data, wherein rho ij and sigma ij are complexity adjustment factors and weighting indexes introduced in the analysis process respectively, so that the accuracy and depth of analysis are improved;
S705, generating a cross-chain analysis report R cross, summarizing all transaction behavior patterns and risks identified by a cross-chain technical framework and a comprehensive analysis module, wherein the report contains comprehensive analysis results and recommended measures for a global encrypted transaction data set:
Wherein Analysis ij represents Analysis content of the j transaction data of the i-th blockchain platform, ω i is a weighting factor of the overall report, and v ij and τ ij represent complexity adjustment factors and weighting indexes of each Analysis result respectively, so as to strengthen the detail and the depth of the report content.
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