Data asset DNA differential double-track traceability identification method and device based on block chain

Abstract

The invention discloses a block chain-based data asset DNA differential double-track traceability identification method and device, and relates to the technical field of data assets, and the method comprises the steps: receiving data asset source data, carrying out the secret processing of the data asset source data, and carrying out the storage of part of encrypted data under an IPFS chain, the remaining encrypted data and the public information data are used as identification data, and the data asset source data comprise the encrypted data and the public information data; the identification data is processed based on a DNA differential generation rule algorithm to obtain data asset identity identification DNA, and the data asset identity identification DNA comprises an encrypted ciphertext, a traceability key and a DNA index key; and generating a double-track index structure based on the data asset identity identification DNA, and realizing tracing of the data assets based on the double-track index structure.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data assets, in particular to a data asset DNA differential double-track traceability identification method and device based on a blockchain. BACKGROUND

[0002] The digital economy is undergoing a paradigm shift from resourceization to assetization, and the traceability process of a large number of data assets is facing structural problems. The existing traceability mechanism based on a blockchain relies too much on pure digital identification, which can easily cause serious physical decoupling problems. At the same time, the single-dimensional chain structure of the traceability architecture is prone to systemic efficiency collapse. The traditional request linear queuing processing in traceability causes the concurrent capability to be locked, and the data of high-frequency interaction is prone to congestion. The efficiency bottleneck caused by the traditional single-dimensional traceability architecture cannot support high-frequency circulation scenarios, and the horizontal expansion of its traceability mode is weak. The cross-block query delay of the single-chain structure of the alliance chain increases sharply with the increase of data volume. The vertical depth is shallow, and the smart contract full-chain scanning positioning index traceability cannot search nearby blocks with traceability genetic characteristics. In the digital economy era, applications in various industries often need to process and use data with multiple partners, which undoubtedly brings data ownership problems. SUMMARY

[0003] To solve the problems mentioned in the background, the purpose of the present application is to provide a data asset DNA differential double-track traceability identification method and device based on a blockchain.

[0004] In a first aspect, the purpose of the present application can be achieved by the following technical solution: a data asset DNA differential double-track traceability identification method based on a blockchain, the method comprising the following steps: receiving data asset source data, and performing secret processing on the data asset source data, including storing part of the encrypted data in an IPFS off-chain, and storing the remaining encrypted data and publicly available information data as identification data, wherein the data asset source data includes encrypted data and publicly available information data; processing the identification data based on a DNA differential generation rule algorithm to obtain a data asset identity DNA, wherein the data asset identity DNA includes an encrypted ciphertext, a traceability key, and a DNA index key; generating a double-track index structure based on the data asset identity DNA, and realizing traceability of the data asset based on the double-track index structure, wherein the double-track index structure includes a vertical intra-block chain index and a horizontal cross-block mapping index.

[0005] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: the processing of the identification data based on the DNA differential generation rule algorithm to obtain the data asset identity DNA is performed by Privacy Guard PG algorithm.

[0006] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the PG The algorithm's processing steps include: The privacy data within the identified data is summed and Laplace noise is injected to generate a noise aggregation value that satisfies ε-differential privacy. The noise aggregation value that satisfies ε-differential privacy is combined with the data asset owner's ownership tag to obtain encrypted ciphertext; Based on the unique identifier MAC address and timestamp of the data asset owner, a traceability key and a DNA index key are generated through a cryptographic hash function; By combining the encrypted ciphertext, the traceability key, and the DNA index key, the data asset identity identifier DNA is obtained.

[0007] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the tracing key. as follows: ; The DNA index key as follows: ; In the formula, Hash is a cryptographic hash function, M is the unique MAC address of the data asset owner's hardware, ts is the timestamp data information, and || is the concatenation operation.

[0008] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the construction of the vertical block intra-linked index as follows: Let block B be j Includes m Let D be a set of DNA sequences. j ={DNA1, DNA2, ..., DNA m},according to h The values ​​are divided into t There are equivalence classes, among which t ≤ m For each equivalence class E c The contained DNA is linked sequentially by pointers, forming a unidirectional linked list. L c , where 1 ≤ c ≤ t， The first address of the linked list head ( L c Recorded in block B j Construct a hash index table I jIn, that is .

[0009] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the construction of the horizontal cross-block mapping index as follows: For newly generated DNA new ∈ B j In addition to participating in the vertical linked list of its own block, it is also connected to the previous block B. j-1 The system pre-determines a mapping relationship based on DNA. This mapping is not randomly generated, but rather calculated based on the similarity of ownership. new Generalized ownership hash h {new} and B j-1 All DNA in h The similarity is calculated, and the most similar DNA is selected. The keyDNA value is recorded as the DNA. new horizontal pointer .

[0010] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the tracing process of the vertical block intra-linked index is as follows: When the tracing request carries a key key trace Upon arrival, first calculate its corresponding generalized hash prefix. h { key trace}, by querying the index table I j The corresponding linked list can be located in O(1) time. L c It is necessary to traverse the linked list. L c You can find satisfaction key DNAi = key trace Target DNA; The time complexity of the vertical retrieval process is: T vertical =O(1) + O(| L c |), where | L c |For linked lists L c The average length.

[0011] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the tracing process of the horizontal cross-block mapping index is as follows: When tracing across blocks, start from a DNA node in the current block and jump along the similarity path formed by the horizontal pointer Ph. Each jump points to the node with the most similar ownership in the historical block. The expected time complexity is Exp[T]. horizontal Depending on the expected length Exp[Leng] of the path, when the distribution of data asset ownership is not completely random, Exp[Leng] is less than the blockchain length n, making T horizontal ∝Exp[Leng].

[0012] Secondly, in order to achieve the above objectives, this invention discloses a blockchain-based data asset DNA differential dual-track traceability identification device, comprising: The data processing module is used to receive data asset source data and perform covert processing on the data asset source data, including storing some encrypted data off-chain in IPFS and using the remaining encrypted data and publicly available information data as identification data. The data asset source data includes encrypted data and publicly available information data. The privacy processing module is used to process the identification data based on the DNA differential generation rule algorithm to obtain the data asset identity DNA, wherein the data asset identity DNA includes encrypted ciphertext, traceability key and DNA index key; The asset traceability module is used to generate a dual-track index structure based on the data asset identity DNA, and to realize the traceability of data assets based on the dual-track index structure. The dual-track index structure includes a vertical intra-block chain index and a horizontal cross-block mapping index.

[0013] In another aspect of the present invention, in order to achieve the above objectives, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor. When the processor loads and executes the computer program, it employs the blockchain-based data asset DNA differential dual-track traceability identification method as described above.

[0014] The beneficial effects of this invention are: This invention generates a dynamically locked traceability identifier (DNA) on the blockchain based on the physical information of the data asset owner. This identifier combines the data owner's physical information with a timestamp and uses a differential algorithm to generate an immutable unique identifier. This identifier is anchored to the blockchain at the time of asset creation, ensuring a unique code and chain for each item, binding ownership. This achieves secure and highly accurate traceability in both horizontal and vertical parallel processes. The collision-resistant unique DNA identifier and multi-layered traceability architecture proposed in this invention, with its innovative three-layer architecture and DNA identifier, enables secure, reliable, and efficient circulation and traceability of data assets. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the overall framework of the present invention; Figure 3 This is a schematic diagram of the internal process steps of DNA differential stratification in this invention; Figure 4 This is a schematic diagram comparing the traceability requests of this invention with the throughput of the blockchain. Figure 5 This is a schematic diagram comparing the hit rate of source tracing under the uniform load distribution mode of the present invention; Figure 6 This is a schematic diagram comparing the source tracing hit rate under the hotspot skewed load mode of the present invention; Figure 7 This is a schematic diagram comparing the source tracing hit rate under the time-dependent load mode of the present invention; Figure 8 This is a schematic diagram of the device structure of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: like Figure 1 As shown, the data asset DNA differential dual-track traceability identification method based on blockchain includes the following steps: S101: Receive data asset source data and perform covert processing on the data asset source data, including storing part of the encrypted data off-chain in IPFS and using the remaining encrypted data and publicly available information data as identification data, wherein the data asset source data includes encrypted data and publicly available information data; S102: The identification data is processed based on the DNA differential generation rule algorithm to obtain the data asset identity DNA, wherein the data asset identity DNA includes encrypted ciphertext, traceability key and DNA index key; The data asset identity identifier DNA is obtained by processing the identifier data based on the DNA differential generation rule algorithm. Privacy Guard PG The algorithm is executed.

[0018] PG The algorithm's processing steps include: The privacy data within the identified data is summed and Laplace noise is injected to generate a noise aggregation value that satisfies ε-differential privacy. The noise aggregation value that satisfies ε-differential privacy is combined with the data asset owner's ownership tag to obtain encrypted ciphertext; Based on the unique identifier MAC address and timestamp of the data asset owner, a traceability key and a DNA index key are generated through a cryptographic hash function; By combining the encrypted ciphertext, the traceability key, and the DNA index key, the data asset identity identifier DNA is obtained.

[0019] Specifically, the PG algorithm is based on the data asset owner. Differential generation is used to generate the DNA information proposed in this paper for data asset traceability. In the early stages of the algorithm, the data asset information transmitted from the upper layer and the information that needs to be publicly displayed are... The algorithm performs PG differential privacy encryption on the mixed data to obtain the desired result. pre DNA Then calculate and separate M This prepares for continued use of encryption. It also involves verifying and tracing the information before handing over the data asset to the owner for safekeeping. key trace Unlike DNA, another important piece of information is key DNA It plays a crucial indexing role in the flow of DNA within and between the local and adjacent DNA strands. The method presented in this paper utilizes formulas... Generate key key trace and key DNA Key and ciphertext combination { key DNA , < pre DNA, M > This is the DNA of data assets. The encryption algorithm steps are as follows: Step 1: Privacy Data Aggregation and Differential Perturbation. This step involves the aggregation and differential perturbation of privacy data. Summation is performed and Laplace noise is injected to ensure that it satisfies ε - Differential privacy. Calculate the true aggregate value: Determine sensitivity: due to arbitrary The biggest change is U The global sensitivity of this summation query φ = UGenerate scale parameters and Laplace noise: ; That is, the noise η is sampled from a Laplace distribution with a scale parameter of μ: ; Calculate the noise aggregate value: .

[0020] Step 2: Generate PreDNA: Aggregate noise values With data asset ownership marking h Combined, an optional encrypted ciphertext is generated to enhance trustworthiness. This step uses ElGamal encryption. Encoding: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] Encoded as an integer message ElGamal encryption: using ownership tokens h and random numbers γ to Encryption yields: , , .

[0021] Step 3: Key Derivation: Use a cryptographic hash function to derive the key from the unique identifier MAC address. M and timestamp ts Two keys are derived from this. A traceability key is generated: Generate DNA index key (keyDNA): .

[0022] Step 4: Data Asset DNA Assembly and Output: Combine the results of the previous steps to form the final data asset DNA structure. Then DNA and Published to the blockchain network as a key factor for traceability. key trace Securely delivered to data asset owners. key DNA As DNA flows through the blockchain.

[0023] S103: Generate a dual-track index structure based on the DNA of data asset identity, and realize the traceability of data assets based on the dual-track index structure. The dual-track index structure includes a vertical intra-block chain index and a horizontal cross-block mapping index.

[0024] The vertical cross-block mapping index is constructed as follows: Intra-block chained structure: DNA within the same block is not stored in an unordered manner. To improve retrieval efficiency, DNA is linked according to its generalized ownership hash. h Perform clustering.

[0025] Structure definition: Let block B be... j Includes m Let D be a set of DNA sequences. j ={DNA1, DNA2, ..., DNA m}. We based on h The values ​​of are divided into . t equivalence classes ( t ≤ m For each equivalence class E c (1 ≤ c ≤ t The DNA contained therein is linked sequentially by pointers, forming a unidirectional linked list. L c The address of the first element of the linked list. head ( L c Recorded in block B j Construct a hash index table I j In, that is .

[0026] The tracing process for a vertical cross-block mapping index is as follows: When the tracing request carries a key... key trace Upon arrival, the scheme first calculates its corresponding generalized hash prefix. h { key trace}. Then, by querying the index table. I j The corresponding linked list can be located in O(1) time. L c After that, you only need to traverse the linked list. L c You can find satisfaction key DNAi = key trace The target DNA.

[0027] The time complexity of this vertical retrieval process is: T vertical =O(1) + O(| L c |), where | L c |For linked lists L c The average length. Due to h After generalization, its value space is much smaller than m Therefore | Lc |significantly smaller than m This achieves local optimization of retrieval efficiency.

[0028] The horizontal cross-block mapping index is constructed as follows: Cross-block mapping structure: When the target DNA is not in the current block, cross-block tracing is required. Traditional solutions require linear backtracking along the block height, with a time complexity of O(block number). n This mechanism optimizes this process to approximately O( ) by establishing a lateral mapping between DNA molecules. log n ) Structural definition: For newly generated DNA new ∈ B j In addition to participating in the vertical linked list of its own block, it will also be linked with the previous block B. j-1 A mapping relationship is established for a specific DNA sequence. This mapping is not randomly established, but based on the similarity of ownership. The system calculates the DNA sequence... new Generalized ownership hash h {new} and B j-1 All DNA in h The similarity is calculated, and the most similar DNA is selected, with its keyDNA value recorded as the DNA. new horizontal pointer This creates a network of DNA that covers the entire chain and is based on ownership similarity, rather than a single chain.

[0029] The tracing process for horizontal cross-block mapping indexes is as follows: During cross-block tracing, the system no longer scans each block sequentially, but starts from a certain DNA node in the current block and follows the horizontal pointer. P h The "similar path" is used to jump around, and each jump points to the node with the most similar ownership in the historical block, thereby quickly approaching the target area.

[0030] The expected time complexity of this process Exp [T horizontal [Depends on the expected length of the path] Exp [ Leng In situations where the distribution of data asset ownership is not completely random, Exp [ Leng Much smaller than the length of a blockchain n This makes T horizontal ∝ Exp [ Leng This achieves sublinear and even logarithmic level improvements in retrieval efficiency.

[0031] The advantages of DNA dual-track effectiveness can be summarized as follows: A leap in traceability efficiency: The dual-track model reduces the complexity of traditional full-chain scanning to O(n log n). n m ) optimized to T total =T vertical +T horizontal ≈ O(| L c |) + Exp [ Leng Through theoretical analysis, | L c | and Exp [ Leng All are much smaller than n and m This theoretically proves its high efficiency.

[0032] Effective defense against malicious activity: Preventing merchant forgery, the DNA is bound to its corresponding off-chain IPFS storage data via cryptographic hashes. Any tampering with off-chain data will result in hash mismatches during verification. Preventing peer duplication, copied data assets... key trace Identical to the original DNA. In the dual-track index, key trace It is a unique key. The system can detect it quickly. key trace Conflict or its related M This points to different physical sources, thus triggering audit alerts.

[0033] Continuity of privacy protection: The entire construction and retrieval process of the dual-track index is based entirely on the generalized identifier. h and M and cryptographic hashes key DNA It operates without ever using any precise original identity information, thus inheriting the K-anonymity privacy protection provided by the anonymity layer.

[0034] This invention aims to explore the effectiveness of a data asset DNA differential dual-track traceability identification method in a blockchain context. To determine the beneficial effects of the scheme described in this invention, 1500 test nodes were deployed in the experimental system, with 500 nodes allocated to each layer. The experimental data consisted of a dataset of information from 2,000 companies worldwide. This dataset included fine-grained attributes of the companies, such as: legal name, legal forms and identifiers (EIN, LEI), address and geographic coordinates, standardized industry classifications (NACE, NAICS, SIC), and key financial data for 2024 (revenue, profit, assets, market capitalization). The financial data was treated as private data in the experiment. To ensure accuracy, all experiments were run 100 times, and the results are expressed as an average.

[0035] This example demonstrates blockchain throughput. Under the same data conditions, traceability requests are performed at intervals of 100 data entries, and the comparison is shown in the attached figure. Figure 4 As shown, the proposed solution significantly outperforms the control group in throughput across 20 test groups, maintaining a relatively stable and reasonable decline trend. In contrast, the other control groups exhibit lower throughput due to limitations in single-layer tracing logic, and show a significant attenuation tendency when tracing requests reach 1600. This is related to the experimental condition of 1500 nodes. Traditional solutions do not use a layered approach for divide-and-conquer, and when a large number of tracing requests are concurrent, a single chain easily reaches its performance bottleneck. On one hand, the smart contracts deployed on nodes during the tracing process require numerous mutual calls, leading to frequent communication between on-chain nodes. On the other hand, the performance overhead of on-chain signature and encryption operations is substantial. Therefore, when tracing attempts are distributed to each node, overflow requests can cause a significant decrease in system throughput. The proposed solution effectively avoids this situation.

[0036] This example demonstrates the performance of the invention under high-frequency interaction in real-world data asset environments. The experiment simulates a high-concurrency scenario and stress-tests the system's dynamic performance metrics. The experiment focuses on evaluating the efficiency of the proposed dual-track index structure under different load modes and exploring the regulatory effect of the anonymity layer generalization parameters on system performance.

[0037] Hit rate and blocking rate of source tracing index under different load modes in high-pressure scenarios This scenario will verify the performance under high-pressure request conditions, gradually increasing the number of concurrent requests from 100 to 2000 for 5 minutes, recording the average values ​​of various performance metrics, and verifying the advantages of the dual-track tracing mechanism compared to the traditional linear scanning scheme and the control group in terms of retrieval hit rate and system blocking rate. Three typical request load modes simulate different business scenarios. The performance metrics are set as follows: Tracing Vertical Hit Rate (Hvertical): the proportion of requests that successfully locate the target DNA within a single block using a vertical chain index; Tracing Horizontal Hit Rate (Hhorizontal): the proportion of requests that require a horizontal pointer jump to other blocks to complete the tracing; System Blocking Rate (Br): the proportion of requests that cannot be processed immediately due to system resource saturation and must enter the queue to wait. Experimental results are compared in the appendix. Figure 5 , 6 As shown in Figures 7 and 8, the present invention maintains a 100% overall hit rate in tracing data under various loads. In the hotspot skew mode, Hvertical achieves a hit rate as high as 90.66%, demonstrating that vertical indexing can optimize the retrieval efficiency of hotspot data to the extreme. In the time-series correlated mode, Hhorizontal achieves a hit rate of only 14.42%, indicating that most requests can be completed without crossing blocks, resulting in extremely high efficiency. Regarding blocking rate, the blocking rate Br of 3DKMT is significantly lower than the comparative scheme in all modes, averaging about 12-17 percentage points lower. This is due to its sublinear time complexity retrieval algorithm, which can quickly process requests and release resources. Traditional full-chain benchmark scanning schemes and control group schemes consume a large amount of computation time for each request, resulting in significantly higher blocking rates than the proposed scheme. In particular, the benchmark scheme's blocking rate of nearly 70% can easily paralyze the tracing system.

[0038] At the ownership anchoring level, this example innovatively introduces the concept of data asset DNA. By integrating the physical device information of the data producer with the precise timestamp, and through cryptographic hashing and differential privacy processing, the generated dynamic identifier achieves a strong binding between digital assets and physical entities from the source, effectively curbing ownership drift and physical decoupling. At the traceability architecture level, this paper breaks the limitations of the traditional single-dimensional chain structure of blockchain and proposes a horizontal and vertical dual-track indexing mechanism, which exhibits excellent throughput and low latency characteristics, especially in high-concurrency scenarios.

[0039] Example 2: To achieve the above objective, such as Figure 8 As shown, based on Embodiment 1, this invention discloses a blockchain-based data asset DNA differential dual-track traceability identification device, comprising: Data processing module 11 is used to receive data asset source data and perform covert processing on the data asset source data, including storing part of the encrypted data off-chain in IPFS and using the remaining encrypted data and publicly available information data as identification data. The data asset source data includes encrypted data and publicly available information data. The privacy processing module 12 is used to process the identification data based on the DNA differential generation rule algorithm to obtain the data asset identity DNA, wherein the data asset identity DNA includes encrypted ciphertext, traceability key and DNA index key; The asset traceability module 13 is used to generate a dual-track index structure based on the data asset identity DNA, and to realize the traceability of data assets based on the dual-track index structure. The dual-track index structure includes a vertical intra-block chain index and a horizontal cross-block mapping index.

[0040] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0041] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0042] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0043] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

Claims

1. A blockchain-based data asset DNA differential dual-track traceability and identification method, characterized in that, The method includes the following steps: Receive data asset source data and perform covert processing on the data asset source data, including storing some encrypted data off-chain in IPFS and using the remaining encrypted data and publicly available information data as identification data. The data asset source data includes encrypted data and publicly available information data. The identification data is processed based on the DNA differential generation rule algorithm to obtain the data asset identity DNA, wherein the data asset identity DNA includes encrypted ciphertext, traceability key and DNA index key; A dual-track index structure is generated based on the DNA of data asset identity, and the traceability of data assets is realized based on the dual-track index structure. The dual-track index structure includes a vertical intra-block chain index and a horizontal cross-block mapping index.

2. The blockchain-based data asset DNA differential dual-track traceability identification method according to claim 1, characterized in that, The data asset identity identifier DNA is obtained by processing the identifier data based on the DNA differential generation rule algorithm. PrivacyGuardPG The algorithm is executed.

3. The blockchain-based data asset DNA differential dual-track traceability identification method according to claim 2, characterized in that, The PG The algorithm's processing steps include: The privacy data within the identified data is summed and Laplace noise is injected to generate a noise aggregation value that satisfies ε-differential privacy. The noise aggregation value that satisfies ε-differential privacy is combined with the data asset owner's ownership tag to obtain encrypted ciphertext; Based on the unique identifier MAC address and timestamp of the data asset owner, a traceability key and a DNA index key are generated through a cryptographic hash function; By combining the encrypted ciphertext, the traceability key, and the DNA index key, the data asset identity identifier DNA is obtained.

4. The blockchain-based data asset DNA differential dual-track traceability identification method according to claim 3, characterized in that, The traceability key as follows: ； The DNA index key as follows: ； In the formula, Hash is a cryptographic hash function, M is the unique MAC address of the data asset owner's hardware, ts is the timestamp data information, and || is the concatenation operation.

5. The blockchain-based data asset DNA differential dual-track traceability identification method according to claim 1, characterized in that, The construction of the vertical block-based chained index is as follows: Let block B be j Includes m Let D be a set of DNA sequences. j ={DNA1, DNA2, ..., DNA m },according to h The values ​​are divided into t There are equivalence classes, among which t ≤ m For each equivalence class E c The contained DNA is linked sequentially by pointers, forming a unidirectional linked list. L c , where 1 ≤ c ≤ t， The first address of the linked list head ( L c Recorded in block B j Construct a hash index table I j In, that is .

6. The blockchain-based data asset DNA differential dual-track traceability identification method according to claim 1, characterized in that, The horizontal cross-block mapping index is constructed as follows: For newly generated DNA new ∈ B j In addition to participating in the vertical linked list of its own block, it is also connected to the previous block B. j-1 The system pre-determines a mapping relationship based on DNA. This mapping is not randomly generated, but rather calculated based on the similarity of ownership. new Generalized ownership hash h {new} and B j-1 All DNA in h The similarity is calculated, and the most similar DNA is selected. The keyDNA value is recorded as the DNA. new horizontal pointer .

7. The blockchain-based data asset DNA differential dual-track traceability identification method according to claim 5, characterized in that, The tracing process of the vertical block intra-linked index is as follows: When the tracing request carries a key key trace Upon arrival, first calculate its corresponding generalized hash prefix. h { key trace }, by querying the index table I j The corresponding linked list can be located in O(1) time. L c It is necessary to traverse the linked list. L c You can find satisfaction key DNAi = key trace Target DNA; The time complexity of the vertical retrieval process is: T vertical =O(1) + O(| L c |), where | L c |For linked lists L c The average length.

8. The blockchain-based data asset DNA differential dual-track traceability identification method according to claim 6, characterized in that, The tracing process of the horizontal cross-block mapping index is as follows: When tracing across blocks, start from a DNA node in the current block and jump along the similarity path formed by the horizontal pointer Ph. Each jump points to the node with the most similar ownership in the historical block. The expected time complexity is Exp[T]. horizontal Depending on the expected length Exp[Leng] of the path, when the distribution of data asset ownership is not completely random, Exp[Leng] is less than the blockchain length n, making T horizontal ∝ Exp[Leng].

9. A blockchain-based data asset DNA differential dual-track traceability identification device, employing the blockchain-based data asset DNA differential dual-track traceability identification method as described in any one of claims 1 to 8, characterized in that, include: The data processing module is used to receive data asset source data and perform covert processing on the data asset source data, including storing some encrypted data off-chain in IPFS and using the remaining encrypted data and publicly available information data as identification data. The data asset source data includes encrypted data and publicly available information data. The privacy processing module is used to process the identification data based on the DNA differential generation rule algorithm to obtain the data asset identity DNA, wherein the data asset identity DNA includes encrypted ciphertext, traceability key and DNA index key; The asset traceability module is used to generate a dual-track index structure based on the data asset identity DNA, and to realize the traceability of data assets based on the dual-track index structure. The dual-track index structure includes a vertical intra-block chain index and a horizontal cross-block mapping index.

10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The memory stores a computer program that can run on a processor. When the processor loads and executes the computer program, it employs the blockchain-based data asset DNA differential dual-track traceability identification method as described in any one of claims 1 to 8.

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