An industrial internet of things-oriented data verification method

Abstract

The application discloses an industrial internet of things-oriented data verification method, which comprises the following steps: firstly, an industrial internet of things device generates a secret value and a public parameter for the whole system; then, the industrial internet of things device calculates a file polynomial by using each file block and further calculates a homomorphic label; finally, the industrial internet of things device constructs a Merkle tree for the homomorphic label and generates a root hash; the industrial internet of things device sends the root hash to a verifier and sends the file and the homomorphic label to a cloud server; after receiving the homomorphic label and the file from the industrial internet of things device, the cloud server generates a proof comprising a polynomial commitment and auxiliary authentication information according to a set of verifier challenges; finally, the verifier verifies the auxiliary authentication information and the polynomial commitment simultaneously. The method adopts a homomorphic hash function combined with a polynomial commitment, and solves the problem that the existing data integrity verification on an industrial cloud platform is not applicable to industrial internet of things devices with limited computing and storage capabilities.

Description

Technical Field

[0001] This invention belongs to the field of data security, specifically relating to a data verification method based on the Industrial Internet of Things. Background Technology

[0002] In recent years, with the emergence of new technologies such as edge computing and 5G, the Industrial Internet of Things (IIoT) has developed rapidly. To improve the reliability, flexibility, and efficiency of industrial systems, an increasing number of IoT devices are connecting to smart factories across various sectors. As the number of terminal devices increases, they also generate a large amount of industrial data. Considering the limited computing and storage capacity of IoT devices, the cloud can be introduced to build cloud-assisted IIoT networks, providing powerful computing capabilities and storage space for the IIoT.

[0003] However, the application of cloud storage in the Industrial Internet of Things (IIoT) also brings security challenges. On the one hand, system failures, power outages, or even malicious attacks can lead to data loss in the cloud. On the other hand, when industrial cloud servers are attacked externally, operational or configuration data may be forged by attackers, allowing them to manipulate or disrupt the entire industrial system. Therefore, it is necessary to ensure the integrity of data stored in cloud-assisted IoT networks.

[0004] Based on this need, Atenise et al. first proposed a provable data ownership scheme that can verify whether a remote server owns the data without retrieving the original data. They designed a homomorphic verifiable tag (HVT) and used a sampling strategy to generate a probabilistic proof. However, this scheme does not support dynamic data updates. Shacham and Waters proposed a provable secure verification mechanism, namely provable data extraction under a random oracle. In this scheme, they provided a strict security proof and designed a shorter HVT using BLS signatures. To alleviate the verification overhead for data owners, Wang et al. first introduced third-party auditing into the verification framework to verify data integrity on behalf of the data owner. Third-party auditing has expertise and capabilities that data owners do not possess and can eliminate the data owner's involvement by verifying data integrity. Yang et al. proposed an efficient and secure dynamic cloud data verification scheme that alleviates the burden of third-party auditing by offloading some computational tasks from third-party auditing to cloud servers. Shen et al. proposed an efficient public auditing protocol that supports batch auditing and efficient data updates.

[0005] While existing solutions attempt to improve verification efficiency, the computational overhead for data owners remains significant due to the inefficiency of homomorphic tag computation. Du et al. introduced multinomial commitments to construct homomorphic verifiable tags to reduce tag storage and computation costs. However, the tag computation and verification phases are still inefficient for IIoT devices and sensors with limited computing and storage capabilities, making them unsuitable for industrial IoT devices. Summary of the Invention

[0006] Based on the aforementioned technological status, this invention provides a data verification method for the Industrial Internet of Things (IIoT), which can efficiently generate homomorphic verification tags and verify cloud-stored data while meeting the computing capabilities of IIoT devices.

[0007] To achieve the above objectives, the technical solution adopted by this invention is a data verification method for the Industrial Internet of Things (IIoT). Its system model has four types of entities: IIoT Devices, Data Users, Cloud Servers, and Verifiers, such as... Figure 1 As shown, the specific system model is as follows:

[0008] 1. Industrial Internet of Things (IoT) devices

[0009] Industrial IoT devices collect and transmit real-time information, logs, and location information to cloud servers for further industrial data analysis. They also upload homomorphic verification tags to the cloud server for subsequent verification.

[0010] 2. Data users

[0011] Data users retrieve data from cloud servers to analyze the data, make decisions, and forward configuration data through cloud servers.

[0012] 3. Cloud server

[0013] The cloud server is responsible for storing all the collected data. It generates storage certificates and sends them to verifiers for storage verification.

[0014] 4. Verifier

[0015] Verifiers check the integrity of data stored on cloud servers on behalf of industrial IoT devices and data users by verifying evidence sent by cloud servers.

[0016] like Figure 1As shown, each industrial IoT device transmits all collected data to a cloud server. Simultaneously, it generates and uploads homomorphic verification tags to the cloud server and transmits verification parameters to a verifier. The verifier issues a challenge to randomly selected data from the cloud server. The cloud server calculates corresponding evidence based on the challenge and returns the evidence to the verifier. The verifier verifies the received evidence and sends the verification result to the data user and the industrial IoT device. Finally, the data user can confirm the integrity of the cloud-stored data and perform data analysis, analytics, or industrial applications on it.

[0017] This plan mainly consists of 5 steps, described in detail below:

[0018] 1. Parameter setting stage

[0019] Based on the input security parameter λ, a parameter algorithm is set to generate the secret value ψ and the common parameter P. m .

[0020] 2. File storage stage

[0021] The storage algorithm takes a secret value ψ and a file F as input and outputs a homomorphic label. and root hash value h r .

[0022] 3. Challenge Generation Phase

[0023] The challenge generation algorithm uses common parameters P m The input is k1, k2, and the output is the challenge parameter cp, which includes the keys k1 and k2 and the number of file blocks to be challenged c.

[0024] 4. Evidence generation stage

[0025] The challenge parameters are cp, file F, and homomorphic label. As input, the evidence generation algorithm outputs the corresponding evidence prof.

[0026] 5. Evidence Verification Stage

[0027] The evidence verification algorithm combines the evidence PRF, challenge parameter CP, and root hash value H. r and common parameter P m As input. If the evidence from the cloud server can be verified, the output Rst is 1; otherwise, it is 0.

[0028] This invention addresses the problem of existing verification methods imposing a heavy computational burden on data owners, reduces the overhead of IoT devices, and proposes an efficient data verification scheme for the Industrial Internet of Things (IIoT). Compared with existing advanced technologies, this invention has lower computational overhead in tag calculation and verification algorithms, and can meet the resource constraints of IIoT devices. Attached Figure Description

[0029] Figure 1 It is a system model diagram

[0030] Figure 2 This is the flowchart of the method. Detailed Implementation

[0031] like Figure 2 The diagram shown is a flowchart of the data verification method of this invention, which includes three participants: an Industrial Internet of Things (IIoT) device, a verifier, and a cloud server, as detailed below:

[0032] I. Industrial Internet of Things (IoT) Equipment

[0033] (1) Setup phase

[0034] Suppose P(x) is a polynomial of order n-1. Let g denote the multiplicative cyclic group of order p. The generator. Industrial IoT devices generate their secret random parameter values ​​ψ, and calculate the common parameter {Ψ}. i} i∈[0，n] , here

[0035] (2) Storage stage

[0036] Suppose an Industrial Internet of Things (IIoT) device has a file F that needs to be stored, and this file is divided into f file chunks. The IIoT device first divides each file chunk {F} into f file chunks. i} i∈[0，f-1] Divide into n data blocks, i.e., F i =F i，0 F i，1 F i，n-1 For each file chunk, the corresponding file polynomial is: The calculated polynomial commitment value is denoted as Here, ψ is a secret parameter generated during the parameter setting phase. Assume a1, a2, ..., a n-1 Let be the coefficients of a polynomial, and s be a random value. Then the polynomial commitment value of a polynomial of degree n-1 at point s is P(s) = a0 + a1·s + a2·s 2 +...+a n-1 ·s n -1 mod p, where mod p means performing a modulo operation, and p is the multiplication cyclic group. The prime order p. that is The value at point ψ.

[0037] Assume the input message x∈Z of the homomorphic hash function HHF q Z here q If x is a prime number field with characteristic q, then the output homomorphic hash value of x is HHF: H←g x Industrial IoT devices use homomorphic hash functions. Calculate the homomorphic label, and then further calculate the hash commitment value. Here, h is the cryptographic hash function, i.e.

[0038] Finally, it will be based on the hash commitment value of all file blocks. Create an indexed Merkle hash tree (IMHT)T, where the root hash value is h. r Unlike previous work that constructed IMHTs for verifying files, this method constructs a tag-indexed Merkle tree (Tag-IMHT) for authenticating homomorphic tags. The leaf nodes of the Tag-IMHT store the hash values ​​of the tags. Here, h represents the cryptographic hash function. Each non-leaf node is represented as h(n||h). lc ||h rc ), where n represents the number of leaf nodes reachable from this node. h lc and h rc These represent the hash values ​​of the left and right child nodes of the node, respectively. By constructing Tag-IMHT for homomorphic tags, the integrity of the homomorphic tags can be guaranteed. Validators only need to store the root hash value to efficiently validate any homomorphic tag using the corresponding Auxiliary Authentication Information (AAI) generated by the cloud server.

[0039] Industrial IoT devices will store all file blocks {F i} and homomorphic tags It is sent to the cloud for storage and sharing. It also sends the root hash value h. r Send to the verifier.

[0040] II. Verifier

[0041] (1) Challenge generation stage

[0042] The validator is responsible for checking whether the shared data is stored intact in the cloud. Therefore, it will send the challenge parameters cp = (k1, k2, c) to the cloud server, where k1 and k2 are randomly generated numbers, and c is the number of file blocks to be challenged.

[0043] (2) Evidence verification stage

[0044] The verifier first retrieves the challenge set S = (id, a) using the same method as in the evidence generation algorithm. id ) and z. Then, it uses Generate a new root hash value h′ r Compare h′ r Is it equal to h? r If they are not equal, the verifier will terminate the verification and reply with a verification failure message.

[0045] The verifier calculates Z prf The homomorphic hash value of (ψ) is Here It is Z prf The coefficient of (x). It further generates Then, the verifier calculates the following equation:

[0046]

[0047] If the equation is true, the verification passes; otherwise, it fails.

[0048] III. Cloud Server

[0049] (1) Evidence generation stage

[0050] After receiving the challenge parameters from the validator, the cloud server calculates the challenge set S = (id, a... id Here, id is the index of the randomly generated challenge block. And a id This is a random number used to generate the coefficients of the evidence. The verifier also generates a random value z for the multinomial commitment. Then, it selects a random value... And calculate B←HHF(β).

[0051] The server also generates a coefficient η←H(B), where Subsequently, the cloud server concealed the evidence polynomial:

[0052]

[0053] Finally, the cloud server transmitted all the evidence. For the verifier, the polynomial quotient here is:

[0054]

[0055] The Ω here i It is the auxiliary authentication information of the i-th file block, that is, in the Merkle tree, it is the leaf node h of the i-th file block. ci Reaching the root node h r The required non-leaf nodes. The cloud server can generate the root node value based on the auxiliary authentication information and the hash value of the leaf node.

Claims

1. A data verification method for the Industrial Internet of Things (IIoT), comprising participants including IIoT devices, data users, cloud servers, and verifiers, characterized in that, Includes the following steps: 1) Parameter settings Suppose P(x) is a polynomial of order n-1, and x represents the unknowns of the polynomial; assume It is a multiplicative cyclic group of order p, and g is called this multiplicative cyclic group. The generator; the industrial IoT device first randomly selects a secret parameter value ψ from a field where the feature number is a prime number p, and then the industrial IoT device performs an exponential operation on the secret parameter value ψ, with the formula as follows: To calculate the common parameter {Ψ i } i∈[0，n-1] Here, i is an integer from 1 to n-1; 2) File storage Suppose an industrial IoT device has a file F that needs to be stored, and this file is divided into f file chunks F. f Industrial IoT devices first divide each file chunk {F i } i∈[0，f-1] The file is divided into n data blocks, assuming the i-th data block is F. i , then {F i =F i，0 F i，1 F i，n-1 }; Then, for the i-th file block, its corresponding file polynomial is The calculated polynomial commitment value is denoted as Here, ψ is a secret parameter value generated during the parameter generation stage; Suppose a1, a2, ..., a n-1 Let be the coefficients of a polynomial, and s be a random value; then the polynomial commitment value of a polynomial with degree n-1 at point s is P(s) = a0 + a1·s + a2·s 2 +...+a n-1 ·s n-1 mod p, where mod p is the modulo p operation on the result of the formula, and p is the prime number field. The characteristic number p in the middle; that is The polynomial commitment value at point ψ; Then, the industrial IoT device calculates the homomorphic tag based on the polynomial commitment value. and hash commitment value Here, h is the cryptographic hash function, i.e., h: This involves performing a cryptographic hash operation on the commitment value of the i-th file block; finally, generating an indexed Merkle tree T based on all homomorphic labels; and then the industrial IoT device processes all file blocks (filechunk) {F i } i∈[0，f-1] and homomorphic tags Send it to the cloud server for storage and sharing, and index the root hash value h of the Merkle hash tree T. r Send to the verifier; 3) Generate Challenges The verifier is responsible for checking whether the shared data is stored completely in the cloud; Therefore, it sends the challenge parameter cp = (k1, k2, c) to the cloud server, where k1 and k2 are randomly generated parameters, and c is the number of file chunks to be challenged; 4) Generating evidence Once the cloud server receives the challenge parameters cp = {k1, k2, c} from the validator, the cloud server will calculate the challenge set S = {(id, a} id )}, and generate a random value z for the multinomial commitment; here id is the index of the randomly generated challenge file block, a id It generates a random value based on the file block ID; then, the cloud server selects a random value. And calculate the homomorphic hash value B←HHF(β) of the random value, where HHF is the homomorphic hash function; assume It is a multiplicative cyclic group of order p, and g is called this multiplicative cyclic group. If the generator is a homomorphic hash function (HHF), then the homomorphic hash function HHF is used to generate a message. Mapped to A hash value H is given by the formula: HHF: The cloud server also generates a coefficient η←H(B), where H is a variable. A point on the map is mapped to a hash function in a field with a prime number p, and its formula is H: Then, the cloud server added the product of two random numbers, β. This conceals the evidence polynomial: Then the cloud server calculates P. prf (z) and the polynomial quotient Z Prf (x), where P prf (z) is P prf The value of (x) at point z; and the polynomial quotient is calculated using the following formula: Here, x is an unknown variable, and z is a random value generated by the cloud server for multinomial commitment; finally, the cloud server transmits all the evidence. For the verifier, here is Ω i It is the auxiliary authentication information about the i-th file chunk in the Merkle tree T; 5) Verify evidence The validator first retrieves the challenge set S = {(id, a}} using the same method as in the evidence generation algorithm. id )} and z; then, it uses Generate a new hash value h′ r The verifier's homomorphic label for the i-th block Perform a cryptographic hash operation to obtain the hash value h of the node. i And use this in conjunction with auxiliary authentication information Ω i All nodes in the array are subjected to cryptographic hashing operations until a new hash value h′ is obtained by hashing the second-to-last value. r Then compare h′ r Is it equal to auxiliary authentication information Ω? i The last value h in r If they are not equal, the verifier will terminate the verification and reply that the verification failed. If they are equal, continue with the next verification step; The verifier calculates the polynomial quotient Z. prf The homomorphic hash value H of (x) Q The formula is Here {ρ i } i∈[0，n-1] It is a polynomial Z prf The coefficients of (x), where n is the polynomial Z. prf (x) order, {Ψ i } i∈[0，n-1] These are common parameters generated during the parameter setting phase; then the verifier calculates H. Q -z power Here, z is a random number generated by the cloud server; the validator further generates it. Then, the verifier calculates the following equation: If the equation is true, the verification passes; otherwise, it fails.

2. The data verification method for the Industrial Internet of Things according to claim 1, further characterized in that, Homomorphic tags in step 2) Calculated as follows: Industrial IoT devices use homomorphic hash functions Calculate the homomorphic label; here, HHF is the homomorphic hash function, assuming the input message is... A multiplication commutative group g is the generator of the group, and the homomorphic hash function outputs the hash value H of x, i.e., HHF:

3. The data verification method for the Industrial Internet of Things according to claim 1, further characterized in that, In step 2), the indexed Merkle tree T is calculated as follows: Industrial IoT devices are based on the hash commitment value of all file blocks. Create an indexed Merkle tree (IMHT)T where the root hash value is h. r A tag-indexed Merkle tree (Tag-IMHT) was constructed for authenticating homomorphic tags; the leaf nodes of the Tag-IMHT store the hash value of the tag. Here, h represents the cryptographic hash function. This represents the homomorphic label of the node; each non-leaf node is represented as h(n||h). lc ||h rc ), where n represents the number of leaf nodes reachable from this node; h lc and h rc These represent the hash values ​​of the left and right child nodes of the node, respectively.

4. The data verification method for the Industrial Internet of Things according to claim 1, further characterized in that, The auxiliary authentication information Ω of the i-th file chunk in step (4) i Calculated as follows: To prove ownership of the i-th file chunk, a cloud server uses auxiliary authentication information including the sibling nodes of the node's parent node and the sibling nodes of the sibling nodes' parent nodes, repeating this process until the root node is included. Assume a Merkle tree with four leaf nodes D1, D2, D3, and D4, whose parent nodes are their hash values ​​N1, N2, N3, and N4; where N1 and N2's parent node is N5; N3 and N4's parent node is N6; and N5 and N6's parent node is the root node N. root Therefore, the auxiliary authentication information Ω1 of D1 includes its parent node N1's sibling node N2, the sibling node N6 of the parent node N5 of sibling node N2, and the root node N. root .

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