Multi-layer anonymous data asset privacy protection method and system based on block chain

Abstract

The invention discloses a multi-layer anonymous data asset privacy protection method and system based on a block chain, and relates to the technical field of data asset privacy protection, and the method comprises the steps: receiving data asset original data which comprises asset original data, a hardware identifier, an identity public code, a cryptographic security random number and a timestamp; the original data of the data assets is subjected to secret processing based on a K-anonymous privacy algorithm, an anonymized data set is obtained, the anonymized data set comprises a plurality of generalized quasi-identifier value sequences, and each generalized quasi-identifier value sequence comprises a quasi-identifier value sequence and a quasi-identifier value sequence; each generalized quasi-identifier value sequence comprises a hardware identifier, a hash value of an identity public code, a cryptographic security random number and a generalized timestamp; and generating a unique privacy identifier of the data assets based on the generalized quasi-identifier value sequence, and verifying the data assets based on the unique privacy identifier of the data assets to realize privacy protection of the data assets.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data asset privacy protection, in particular to a multi-layer anonymous data asset privacy protection method and system based on a block chain. BACKGROUND

[0002] The digital economy is experiencing a paradigm shift from resourceization to assetization. However, this process is facing structural shackles: the non-competitive nature of data assets leads to blurred boundaries between data resources and data assets; privacy protection and privacy asset verification needs are naturally mutually exclusive; traditional architecture is difficult to support high-frequency circulation scenarios, and the low cost of malicious behavior further erodes the foundation of market trust. The easy-to-copy nature of data assets allows any holder to commit two types of malicious behavior: merchants can fake data to avoid quality accountability, or competitors can launch impersonation attacks through secondary copying. Existing defense solutions rely on legal recourse, but cross-border evidence collection takes a long time and cannot form effective technical containment, leading to data assets being easily leaked and security cannot be guaranteed. SUMMARY

[0003] To solve the problems mentioned in the background, the purpose of the present application is to provide a multi-layer anonymous data asset privacy protection method and system based on a block chain.

[0004] In a first aspect, the purpose of the present application can be achieved by the following technical solution: a multi-layer anonymous data asset privacy protection method based on a block chain, the method comprising the following steps: receiving data asset original data, wherein the data asset original data includes asset original data, a hardware identifier, an identity public code, a cryptographic security random number, and a timestamp; secretly processing the data asset original data based on a K-anonymous privacy algorithm to obtain an anonymized data set, wherein the anonymized data set contains a plurality of generalized quasi-identifier value sequences, and each generalized quasi-identifier value sequence includes a hardware identifier, a hash value of an identity public code, a cryptographic security random number, and a generalized timestamp; generating a unique privacy identifier for the data asset based on the generalized quasi-identifier value sequence, verifying the data asset based on the unique privacy identifier for the data asset, and achieving privacy protection for the data asset.

[0005] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: the process of secretly processing the data asset original data based on the K-anonymous privacy algorithm comprises: The original data of the data asset is r = (d, M, h, γ, ts), where d is the original data of the asset, M is the MAC address of the hardware unique identifier of the data asset party, h = Hash(id) is the cryptographic hash value of the ownership mark of the data asset party, id is the public unique identity code of the individual or enterprise of the data asset party, γ is a cryptographically secure random number, and ts is timestamp data information. The PDI is distorted using a generalized privacy processing algorithm to generate a generalized set of quasi-identifiers, PDI. A single data item within its set is denoted as pd. i Simultaneously, the original data d is encrypted and stored offline in the off-chain IPFS system. Using T and the anonymity parameter K as input, an anonymized record set T is obtained. The output of the generalization mapping rule G.

[0006] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: generalizing the alignment identifier using the generalization-based privacy processing algorithm, including: Generalize hardware identifiers to a prefix matching pattern; The hash value of the public identity code is generalized to a short hash prefix; Generalize cryptographically secure random numbers to a numerical range; Generalize the timestamp to a time range.

[0007] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the algorithm flow of the generalized privacy processing algorithm is as follows: Quasi-identifier extraction and preprocessing: For each record Extract its quasi-identifier vector ; Multidimensional generalization: Define a generalization hierarchy H for each PDI attribute and apply the generalization function. For the internal MAC address M: generalize to a prefix matching pattern, let Keep the first k bytes, set the rest to zero or obfuscate them, and let ; The ownership hash h is generalized to a short hash prefix, let That is, take the first l bits of the hash value, and use a random number (γ): generalize to a numerical range, let ; The timestamp ts is generalized to a time interval, let The generalization result can be expressed as: ; Satisfying the K-anonymity constraint: Finding an optimal generalization scheme G This makes the generalized dataset T Satisfy the K-anonymity constraint while minimizing information loss, the optimization problem is formalized as: , ; Data separation and storage: for each record r i Compute its generalized PDI factor: , encrypt the original data d i and store it to the off-chain IPFS, get the content addressing hash: , construct the anonymized record and add it to the set T ; Output: the algorithm outputs the anonymized dataset T and the generalization rule G , where as the input factor of the privacy difference layer.

[0008] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: the generation of a unique privacy identifier of a data asset based on a sequence of generalized quasi-identifier values adopts a differential privacy algorithm, combined with the hardware identifier based on the sequence of generalized quasi-identifier values and the generalized timestamp, and finally generates the unique privacy identifier of the data asset through a cryptographic hash function.

[0009] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: the verification process of the data asset based on the unique privacy identifier of the data asset, comprising: Registering a management contract, the core method is , input the generalized quasi-identifier , IPFS content pointer , output contains registration index success event; bind and and store persistently; trigger the event of notifying the privacy identifier generation contract; verify the contract, the core method is , input the verification key provided by the user key trace , output the traceability result and verification data : take key trace as input, find the target privacy identifier, and then get the off-chain IPFS data from its associated , perform hash comparison verification operation, and finally return the verification result.

[0010] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: the hardware identifier provides a medium access control address for the data asset.

[0011] In a second aspect, to achieve the above object, the application discloses a multi-layer anonymous data asset privacy protection system based on a blockchain, comprising: a data receiving module configured to receive data asset original data, wherein the data asset original data comprises asset original data, a hardware identifier, an identity public code, a cryptographic security random number, and a timestamp; a data processing module configured to perform secret processing on the data asset original data based on a K-anonymity privacy algorithm to obtain an anonymized data set, wherein the anonymized data set comprises a plurality of generalized quasi-identifier value sequences, and each generalized quasi-identifier value sequence comprises a hardware identifier, a hash value of an identity public code, a cryptographic security random number, and a generalized timestamp; a privacy protection module configured to generate a unique privacy identifier of the data asset based on the generalized quasi-identifier value sequence, perform verification on the data asset based on the unique privacy identifier of the data asset, and achieve privacy protection on the data asset.

[0012] In another aspect of the application, to achieve the above object, a terminal device is disclosed, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, a multi-layer anonymous data asset privacy protection method based on a blockchain is adopted.

[0013] In still another aspect of the application, to achieve the above object, a computer readable storage medium is disclosed, wherein the computer readable storage medium stores a computer program, and when the computer program is loaded and executed by a processor, a multi-layer anonymous data asset privacy protection method based on a blockchain is adopted. The application has the following beneficial effects: According to the frequent circulation characteristics of data assets, the application formulates a differential anonymous privacy policy that adapts to the verification rules of the data assets, separates the privacy identifier of the data party from the source data privacy differential, stores the generated privacy identifier chain, and strongly binds the corresponding data and the off-chain IPFS storage system. The single disadvantage of the traditional architecture is separated into three layers by the multi-layer mechanism inside the blockchain: an application layer supported by a smart contract; a privacy differential layer managed by a unique privacy identifier; and an anonymous layer that separates anonymous information from source data. The three layers are cooperated to optimize the data asset privacy verification method. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings. Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the framework of the present invention; Figure 3 This is a schematic diagram of the internal process steps of the anonymity layer in this invention; Figure 4 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0015] 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.

[0016] Example 1: like Figure 1 As shown, a blockchain-based multi-layered anonymity data asset privacy protection method includes the following steps: S101: Receive raw data of data assets, wherein the raw data of data assets includes raw asset data, hardware identifier, identity public code, cryptographically secure random number and timestamp; It is divided into a three-layer structure, including an anonymity layer, a privacy difference layer, and an application layer. Each layer performs its own function while working together for governance.

[0017] This invention discloses a multi-layered anonymity data asset privacy protection method based on blockchain. The method is characterized by a hierarchical governance structure for block nodes. In the anonymity layer, entirely governed by anonymous nodes, the source data of the data asset is anonymously processed using the K-anonymity privacy algorithm. Most of the encrypted data is stored off-chain via IPFS, while a small amount of publicly available identifier data is passed down to the next layer. This approach aims to protect data asset privacy while alleviating block storage pressure. In the privacy differential layer, governance is entirely handled by privacy nodes. After receiving information processed by the K-anonymity algorithm, this layer uses a privacy differential generation rule algorithm to obtain a unique identity privacy identifier specific to the data asset. The top-level application layer is managed by application nodes, which respond to privacy verification requests and information feedback. The overall architecture is shown in the attached figure. Figure 2 As shown.

[0018] The anonymous layer in the scheme aims to protect privacy by generating a unique identifier that is different from the physical information of the data asset. This layer can solve the weak anchoring of data asset ownership and save storage space by combining off-chain IPFS system, ensure high-fidelity data asset privacy requirements, and establish a privacy protection strategy that adapts to data asset rules. The anonymous layer process steps are shown in FIG. Figure 3 .

[0019] S102: Perform secret processing on the data asset original data based on the K-anonymity privacy algorithm to obtain an anonymized data set, wherein the anonymized data set contains a plurality of generalized quasi-identifier value sequences, wherein each generalized quasi-identifier value sequence includes a hardware identifier, a hash value of an identity public code, a cryptographically secure random number, and a generalized timestamp; The process of performing secret processing on the data asset original data based on the K-anonymity privacy algorithm includes: The data asset original data is r =(d, M, h, γ, ts), wherein d is the asset original data, M is the hardware unique identifier MAC address of the data asset party, h = Hash(id) is the cryptographic hash value of the ownership mark of the data asset party, id is the unique identity public code of the data asset party, γ is a cryptographically secure random number, and ts is the timestamp data information; By performing distortion processing on the PDI based on the generalized privacy processing algorithm, a set of generalized quasi-identifiers PDI is generated, and each data in the set is denoted as pd i ; at the same time, the original data d is encrypted and stored offline in the off-chain IPFS system, and T, the anonymous parameter K are input to obtain an anonymized record set T , the output of the generalized mapping rule G.

[0020] The generalized privacy processing algorithm performs generalized processing on the quasi-identifier, including: The hardware identifier is generalized to a prefix matching mode; The hash value of the identity public code is generalized to a short hash prefix; The cryptographically secure random number is generalized to a numerical interval; The timestamp is generalized to a time interval.

[0021] The K-anonymity algorithm in the method anonymous layer is a privacy protection model method realized by generalization technology. The core goal is to ensure that any data record in the data asset that needs to be connected cannot be distinguished from other K-1 records according to the quasi-privacy identifier PDI. At this point, the anonymous scheme we designed is based on the original data submitted by the data asset provider and the physical information it has at that time, which is generated as a record r by algorithm, which is composed of a five-tuple containing the physical information of the data asset party, denoted as r =(d, M, h, γ, ts), wherein d is the original data of the data asset, M is the MAC address of the hardware unique identifier of the data asset party, h = Hash(id) is the cryptographic hash value of the ownership mark of the data asset party, id is the public code of the unique identity of the data asset party or enterprise, γ is a cryptographic secure random number, and ts is the time stamp data information. It should be noted that, in order to effectively anchor the asset ownership in the data asset verification process while protecting the privacy of the data party, the mechanism hashes the id, which aims to achieve the following goals: privacy protection, using the one-wayness of the hash function to ensure the confidentiality of the original identifier id, realizing irreversible pseudo-anonymization, meeting the data desensitization regulation requirements; stable association, ensuring that the assets provided by the same data provider at different times can be reliably associated based on the stable h value, providing a consistent basis for subsequent formation of K-anonymous equivalence classes; controllable verification, the hash value h is used as a factor to generate the data asset privacy identifier, which can support the ownership declaration verification operation without completely exposing the identity.

[0022] The quasi-identifier PDI generated by anonymity is a set of key attributes in the original data of the data asset that can be used to link with the privacy difference layer information for traceability operation. In this anonymity mechanism, it is defined as: PDI = {M, h, γ, ts}, and the time stamp ts will be generalized to a time window to avoid being accurately marked to destroy the anonymity privacy protection. Let T = {r1, r2,...,rn}, the anonymization process is a mapping function Anony, which converts T into an anonymous data set T It satisfies K-anonymity if and only if for T Each quasi-identifier value sequence appears at least K times in T , K is the core parameter that quantifies the strength of privacy protection, which defines the degree of anonymity, that is, each data asset needs to be hidden in at least K-1 data groups, and its value is determined by the data publishing strategy, which needs to be balanced between privacy protection strength and data utility. This property can be formally defined as: , , wherein t[PDI] represents the table T The projection of the records t on the quasi-identifier attribute set, | · | represents the cardinality of the set, and the subset of records satisfying the above conditions is called an equivalence class E. K-anonymity provides identity privacy from a probabilistic perspective. Assuming that the attacker has quasi-identifier information pdtarget of the target individual, even if it successfully locates the corresponding equivalence class E, the probability of successfully identifying a specific individual will not exceed : This anonymous scheme is a key bridge between "unique identification" and "privacy protection", which balances the identification of distinction and stability, and strips its ability to directly point to the real identity, laying a solid foundation for the next level of privacy differential algorithm and the privacy security of the entire multi-level mechanism.

[0023] The algorithm flow of the privacy processing algorithm based on generalization is as follows: To realize the K-anonymity privacy algorithm in the present example, the mechanism designs a privacy processing algorithm based on generalization. The algorithm distorts the PDI to generate a set of generalized quasi-identifiers PDI , and the single data in the set is denoted as pdi, which is processed as a key factor and passed to the privacy differential layer to generate a unique privacy identifier for verification; at the same time, the original data d is encrypted and stored offline in the off-chain IPFS system. The algorithm flow is as follows, taking T, the anonymity parameter K as input, to get the anonymized record set T , the output of the generalization mapping rule G.

[0024] Quasi-identifier extraction and preprocessing: for each record , extract its quasi-identifier vector .

[0025] Multi-dimensional generalization: define the generalization hierarchy H for each PDI attribute and apply the generalization function . For internal MAC address (M): generalize to prefix matching mode, let keep the first k bytes, and the rest are zeroed or blurred, let . Ownership hash (h): generalize to a short hash prefix, let , that is, take the first l bits of the hash value. Random number (γ): generalize to a numerical interval, let . Timestamp (ts): generalize to a time interval, let , in the above generalization and are consistent with K, and the data privacy policy is used to determine the value, and the experimental part will explore the optimal solution under various conditions. Finally, the generalization result of a record can be expressed as: .

[0026] Satisfy the K-anonymity constraint: find an optimal generalization scheme G such that the generalized dataset T satisfies the K-anonymity constraint while minimizing the information loss. This optimization problem is formalized as: , .

[0027] Data separation and storage: For each record ri, compute its generalized PDI factor: encrypt the original data di and store it to the off-chain IPFS, get the content addressing hash: construct the anonymized record and add it to the set T .

[0028] Output: The algorithm outputs the anonymized dataset T and the generalization rule G , where is the input factor of the privacy differential layer, used to generate a unique privacy identifier.

[0029] The privacy differential layer aims to create a unique identifier for data assets through differential privacy algorithms in a cryptographic method to innovate verification patterns. This layer breaks the limitations of traditional single-dimensional inefficient verification solutions. The multi-layer verification mechanism based on the characteristics of the privacy identifier can effectively resist malicious behavior from data asset parties or peers.

[0030] The topmost application layer aims to quickly respond to verification requests from verification parties by setting up smart contracts. This layer is mainly based on smart contracts, and numerous application nodes are responsible for traceability operations. Based on the privacy identifier information processed by the privacy differential layer, the verification algorithm is automatically executed, and the data asset verification request is efficiently processed under high security conditions.

[0031] S103: Generate a unique privacy identifier for the data asset based on the sequence of generalized quasi-identifier values, verify the data asset based on the unique privacy identifier of the data asset, and achieve privacy protection for the data asset.

[0032] The process of verifying the data asset based on the unique privacy identifier of the data asset includes: The smart contract is the core execution unit of the application layer of the present mechanism, which solidifies the algorithm logic of the anonymization layer and the privacy differential layer on the blockchain in a decentralized and verifiable manner, ensuring the automation and credibility of the verification business. Among them: the core method of the registration management contract is , the input generalized quasi-identifier , the IPFS content pointer , and the output contains a registration index success event. Its function is to verify the data party's signature; With Binding and persisting storage; triggering events of the notification privacy identifier generation contract. Verifying contract, whose core method is , input user-provided verification key keytrace, output traceability result and verification data , whose function is to execute the traceability algorithm: take keytrace as input, find the target privacy identifier, and then from its associated Get off-chain IPFS data, perform hash comparison and other verification operations, and finally return the verification result.

[0033] Specifically, the following embodiments will further illustrate the present application: This example sets the privacy leakage threat: the attacker is a malicious node or verifier on the blockchain, has access to all on-chain public information (including DNA, generalized identifiers, etc.), and its goal is to use this information to infer the original privacy data of a specific data asset or the real identity of the data provider. Theorem: resist privacy leakage, under this threat, assuming that the attacker has a quasi-identifier pdtarget, the probability Psuccess of successfully inferring the real identity id corresponding to the target data record r is ≤ 1 / K. This probability is independent of the blockchain computing power and is determined only by the anonymity layer parameter K. Proof: according to the formal definition of K-anonymity, for the anonymized data set T , any quasi-identifier sequence appears in at least K records. Therefore, for any pdtarget owned by the attacker, the size of the equivalence class E that it can locate . This upper bound on the probability is strictly guaranteed by the K-anonymity algorithm. In addition, the privacy differential layer further processes the generalized data with differential privacy. Even if the attacker can partially weaken the protection of K-anonymity, its inference of the aggregate statistic S will become extremely inaccurate due to the injection of Laplace noise The attacker cannot distinguish with high confidence whether the output from the real data set S or its adjacent data set S' is, which satisfies the definition of ε-differential privacy, so it cannot be sure of its inference result. Therefore, under the superposition of the two layers of privacy protection, the risk of privacy leakage under this threat is controlled within the acceptable theoretical lower limit.

[0034] This example sets the data tampering threat: malicious data providers (business evil) try to tamper with the original data di stored in the off-chain IPFS after registering the data asset on the chain, and deny their tampering behavior to escape quality accountability. Theorem: resist data tampering, under this threat, any tampering behavior (di→di') of the original data di stored in the off-chain IPFS will be detected by the smart contract of the application layer with a high probability. Proof: the security of this mechanism is rooted in the collision resistance of the cryptographic hash function and the tamper resistance of the blockchain. When the data is stored separately in the anonymous layer, the system has calculated and stored the cryptographic hash value Hash(di) of the original data on the chain. Suppose the malicious provider tampers the data to di'. When verifying, the smart contract will perform the following operations: ① Get the previously stored and trusted hash value Hash(di) from the chain. ② According to the pointer ci stored in the privacy identifier, get the current data di' from IPFS. ③ Calculate the hash Hash(di') of the current data. ④ Compare and verify: Hash(di') and Hash(di) are consistent. According to the properties of the cryptographic hash function: di≠di'⇒Hash(di)≠Hash(di'), therefore, as long as di'≠di, the equation is almost certainly not true, and the smart contract will return a verification failure and mark the data as tampered. The entire verification process does not involve decrypting the original data, protecting privacy, while efficiently and reliably completing integrity verification. The tamper resistance of the on-chain hash Hash(di) ensures the credibility of the verification basis, effectively resisting this threat.

[0035] This example sets the counterfeit copy threat: malicious peer attackers try to generate the same verification key keytrace by copying the original data asset, thereby impersonating the original data provider and illegally claiming ownership of the data asset. Theorem: resist counterfeit copying, under this threat, after malicious peers copy the data asset, they cannot successfully pass the ownership verification of the traceability verification contract without triggering system alerts. Proof: the resistance of this threat relies on the unique binding mechanism of the privacy identifier. The privacy identifier keytrace and keyDNA of the data asset are derived from the original physical identifier M and the precise timestamp ts through the hash function: , The determinacy of the hash function ensures that the same (M, ts) always generates the same keytrace. When the attacker copies the data, there are two cases as follows.

[0036] Copy data but cannot copy the physical source: the attacker uses a different physical device (M'≠M) or submits the copied data at a different time (ts'≠ts). According to the above formula, the keytrace' it generates will be different from the keytrace of the original version. Therefore, when using the original keytrace to verify, it cannot find a matching keyDNA in the dual-track index, the verification fails, and the impersonation fails.

[0037] Perfect copy (including physical source and time): this is a very strong assumption, difficult to achieve in practice. Even if it is true, the system will detect keyDNA conflicts when the privacy identifier is registered, because the same input will produce the same keyDNA, or find the same keytrace but associated with different off-chain data hashes Hash(di) or different generalized device identifiers M due to MAC address generalization results , thus triggering the fraud alert mechanism of the smart contract.

[0038] Assume that the probability of the attacker successfully forging and passing the verification is Pforge. This probability is limited by its ability to copy the physical environment and the exact time, and is zero when a conflict is detected: Pforge≤P(copy M and ts exactly)≈0 Therefore, the impersonation attack described by this threat is extremely difficult to succeed in practice, and this mechanism provides effective protection against it.

[0039] Embodiment two: in order to achieve the above purpose, as shown in Figure 4 based on the basis of embodiment one, the application discloses a multi-layer anonymous data asset privacy protection system based on block chain, comprising: The data receiving module 11 is used for receiving data asset original data, wherein the data asset original data includes asset original data, hardware identifier, identity public code, cryptographic security random number and timestamp; The data processing module 12 is used for secretly processing the data asset original data based on the K-anonymous privacy algorithm to obtain an anonymized data set, wherein the anonymized data set contains a plurality of generalized quasi-identifier value sequences, and each generalized quasi-identifier value sequence includes a hardware identifier, a hash value of an identity public code, a cryptographic security random number and a generalized timestamp; The privacy protection module 13 is used for generating a unique privacy identifier of the data asset based on the generalized quasi-identifier value sequence, verifying the data asset based on the unique privacy identifier of the data asset, and realizing privacy protection of the data asset.

[0040] Based on the same inventive concept, the present application further provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the program comprises program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are configured to implement one or more instructions, and are specifically configured to load and execute one or more instructions in the computer storage medium to implement the above method.

[0041] It needs to be further explained that, based on the same inventive concept, the present application further provides a computer storage medium, which stores a computer program, and the computer program is executed by the processor to perform the above method. The storage medium can adopt 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 may, for example, be but is not limited to an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, 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, device or component.

[0042] In the description of the present application, the description of the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in one or more embodiments or examples.

[0043] The foregoing presents and describes the basic principles, main features and advantages of the present disclosure. It should be understood by those skilled in the art that the present disclosure is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, various changes and improvements can be made to the present disclosure, and all these changes and improvements fall within the scope of the present disclosure.

Claims

1. A blockchain-based method for protecting the privacy of multi-layered anonymous data assets, characterized in that: The method includes the following steps: Receive raw data of data assets, wherein the raw data of data assets includes raw asset data, hardware identifier, identity public code, cryptographically secure random number and timestamp; The original data of the data asset is anonymized using the K-anonymity privacy algorithm to obtain an anonymized dataset. The anonymized dataset contains multiple generalized quasi-identifier value sequences. Each generalized quasi-identifier value sequence includes a hardware identifier, a hash value of the identity public code, a cryptographically secure random number, and a generalized timestamp. A unique privacy identifier for data assets is generated based on a generalized sequence of quasi-identifier values. The data assets are then verified based on this unique privacy identifier, thus achieving privacy protection for the data assets.

2. The method for protecting privacy of multi-layered anonymous data assets based on blockchain according to claim 1, characterized in that, The process of anonymously processing the raw data of data assets based on the K-anonymity privacy algorithm includes: The original data of the data asset is r = (d, M, h, γ, ts), where d is the original data of the asset, M is the MAC address of the hardware unique identifier of the data asset party, h = Hash(id) is the cryptographic hash value of the ownership mark of the data asset party, id is the public unique identity code of the individual or enterprise of the data asset party, γ is a cryptographically secure random number, and ts is timestamp data information. The PDI is distorted using a generalized privacy processing algorithm to generate a generalized set of quasi-identifiers, PDI. A single data item within its set is denoted as pd. i Simultaneously, the original data d is encrypted and stored offline in the off-chain IPFS system. Using T and the anonymity parameter K as input, an anonymized record set T is obtained. The output of the generalization mapping rule G.

3. The method for protecting privacy of multi-layered anonymous data assets based on blockchain according to claim 2, characterized in that, The generalization-based privacy processing algorithm performs generalization processing on the alignment identifier, including: Generalize hardware identifiers to a prefix matching pattern; The hash value of the public identity code is generalized to a short hash prefix; Generalize cryptographically secure random numbers to a numerical range; Generalize the timestamp to a time range.

4. The method for protecting privacy of multi-layered anonymous data assets based on blockchain according to claim 3, characterized in that, The algorithm flow of the generalization-based privacy processing algorithm is as follows: Quasi-identifier extraction and preprocessing: For each record Extract its quasi-identifier vector ; Multidimensional generalization: Define a generalization hierarchy H for each PDI attribute and apply the generalization function. For the internal MAC address M: generalize to a prefix matching pattern, let Keep the first k bytes, set the rest to zero or obfuscate them, and let ; The ownership hash h is generalized to a short hash prefix, let That is, take the first l bits of the hash value, and use a random number (γ): generalize to a numerical range, let ; The timestamp ts is generalized to a time interval, let The generalization result can be expressed as: ; Satisfying the K-anonymity constraint: Finding an optimal generalization scheme G This makes the generalized dataset T To satisfy the K-anonymity constraint and minimize information loss, the optimization problem can be formalized as follows: , ; Data separation and storage: For each record r i Calculate its generalized PDI factor: encrypt the original data d i And store it in off-chain IPFS, and obtain the content addressing hash: Constructing anonymized records Add it to set T ; Output: The algorithm outputs anonymized dataset T. and generalization rule G ,in As an input factor for privacy differential stratification.

5. A blockchain-based multi-layered anonymity data asset privacy protection method according to claim 1, characterized in that, The method for generating unique privacy identifiers for data assets based on generalized quasi-identifier value sequences employs a differential privacy algorithm. This algorithm combines a hardware identifier based on the generalized quasi-identifier value sequence with a generalized timestamp, and finally generates the unique privacy identifier for the data asset through a cryptographic hash function.

6. A method for protecting privacy of multi-layered anonymous data assets based on blockchain according to claim 1, characterized in that, The data asset verification process based on the unique privacy identifier of the data asset includes: Register and manage contracts; the core method is... Input generalized quasi-identifier IPFS content pointer The output includes a successful index registration event; and Bind and persist storage; trigger the event that notifies the privacy identifier generation contract; verify the contract; the core method is... Enter the verification key provided by the user. key trace Output the source tracing results and verification data. :by key trace For input, after finding the target privacy identifier, from its associated... Retrieve off-chain IPFS data, perform hash comparison verification, and finally return the verification result.

7. A blockchain-based multi-layered anonymity data asset privacy protection method according to claim 1, characterized in that, The hardware identifier provides a media access control address for the data asset.

8. A blockchain-based multi-layer anonymized data asset privacy protection system, employing the blockchain-based multi-layer anonymized data asset privacy protection method as described in any one of claims 1 to 7, characterized in that, include: The data receiving module is used to receive raw data of data assets, wherein the raw data of data assets includes raw asset data, hardware identifier, identity public code, cryptographically secure random number and timestamp; The data processing module is used to perform covert processing on the original data asset data based on the K-anonymity privacy algorithm to obtain an anonymized dataset. The anonymized dataset contains multiple generalized quasi-identifier value sequences, wherein each generalized quasi-identifier value sequence includes a hardware identifier, a hash value of the identity public code, a cryptographically secure random number, and a generalized timestamp. The privacy protection module is used to generate unique privacy identifiers for data assets based on a generalized sequence of quasi-identifier values, and to verify data assets based on these unique privacy identifiers, thereby achieving privacy protection for data assets.

9. 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 the processor. When the processor loads and executes the computer program, it employs a blockchain-based multi-layered anonymous data asset privacy protection method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it employs a blockchain-based multi-layered anonymous data asset privacy protection method as described in any one of claims 1 to 7.

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