Cross-device collaborative verification method and system based on tee and ima, device and storage medium

Abstract

The application provides a cross-device cooperative verification method and system based on TEE and IMA, a device and a storage medium, and belongs to the technical field of distributed system IMA verification. The method comprises the following steps: a host device verifies the credibility of a slave device TEE; the host device assigns IMA verification tasks according to the weighted scores of the computing capacity of the slave device; the slave device verifies the IMA in the TEE, signs and returns the result; a secure communication channel is established between the devices, and data is transmitted through an encryption protocol; the host device summarizes the result through majority voting or a PBFT distributed consensus algorithm; wherein the slave device verifies the IMA in batches in the TEE and performs abnormality detection, and the host device performs dynamic distributed consensus confirmation. The application is used for optimizing the verification efficiency and trusted demand of edge devices, blockchains and other distributed scenes.

Description

Technical Field

[0001] This invention belongs to the field of distributed system IMA verification technology, and particularly relates to cross-device collaborative verification methods, systems, devices and storage media based on TEE and IMA. Background Technology

[0002] The rapid development of distributed systems has placed higher demands on integrity verification. IMA verifies file integrity through hash value comparison and is often used in conjunction with TPM. TEE provides a secure and trusted environment based on hardware isolation mechanisms, and the combination of the two has existing applications. For example: Single-device TEE + IMA: such as TZ-IMA, to verify the integrity of a single device; Containerized IMA: Used in container environments, not applicable to multi-device scenarios; Runtime TEE verification: TDX supports runtime checks; Mobile device protection: TIMA protects mobile terminal devices.

[0003] The technical problems existing in the current technical solutions include: limitations of single devices: low efficiency and no coverage of multi-device scenarios; lack of coordination mechanism, making it impossible to apply in multi-node scenarios; insufficient fault tolerance: no fault handling is provided; lack of trust guarantee: there is no consensus mechanism among multiple nodes, and trusting the verification result of only a single node poses a risk; in the distributed system, the master device needs to verify the integrity of a large number of files, including firmware, configuration files, key components, etc., and the computing power of a single device is limited.

[0004] Therefore, there is an urgent need for a cross-device collaborative verification solution to improve efficiency, reliability, and fault tolerance, especially to meet the needs of distributed scenarios such as edge devices and blockchain. Summary of the Invention

[0005] To address the limitations of existing technologies in distributed systems, this invention provides a cross-device collaborative verification method, system, device, and storage medium based on TEE and IMA, thereby optimizing verification efficiency and trust requirements in distributed scenarios such as edge devices and blockchain.

[0006] The first aspect of this invention discloses a cross-device collaborative verification method based on TEE and IMA, the method comprising: S1: a master device verifies the trustworthiness of the slave device's TEE; S2: the master device weights and allocates IMA verification tasks according to the computing power scores of the slave devices; S3: the slave device verifies the IMA in the TEE, signs it, and returns the result; S4: a secure communication channel is established between the devices, and data is transmitted through an encryption protocol; S5: the master device summarizes the results through majority voting or the PBFT distributed consensus algorithm; wherein, in S2, the slave device performs batch verification of IMA and anomaly detection in the TEE, and in S5, the master device performs dynamic distributed consensus confirmation.

[0007] In S2, the weighted allocation IMA verification task specifically includes: The computing device's capability score is weighted differently based on the number of CPU cores, CPU clock speed, memory, and network connectivity. Score=t1 cpu_num+t2 cpu_freq+t3 memory+t4 network; Where t1, t2, t3, and t4 represent weights; Files are segmented based on device capability scores: Task[i]=N (score[i] / ∑score) Where N is the total file size; A dynamic real-time adjustment mechanism is implemented so that when a slave device's load exceeds a threshold or it goes offline, the master device reallocates tasks. On edge devices, the firmware and data streams of edge nodes are verified, and devices with higher capability scores are processed first for critical firmware or other large files.

[0008] In S5, dynamic distributed consensus is used to improve the credibility of verification results, where: When the number of devices is less than or equal to the discrimination value, a consensus is reached through majority voting. When the number of devices exceeds the discrimination value, the PBFT distributed consensus algorithm is used to tolerate L(n-1) / 3 malicious nodes; If the connection times out, it will be reassigned.

[0009] In S2, anomaly detection from the device specifically includes: Task integrity check: HMAC is used to verify the task package; Consistency check: Mark an anomaly when the result deviation exceeds a threshold; Test response: Send a pseudo-file for testing; Follow-up processing: Remove abnormal devices and documents.

[0010] In the method: During the initialization phase, the master device broadcasts a challenge code, and the slave device responds with a TEE signature. If the verification is trustworthy, the task allocation phase begins; if the task is untrustworthy, the untrustworthy device is removed. During the task allocation phase, the master device calculates the capability score of the slave device, segments the file, and generates a task package by weighted allocation, and broadcasts the task through an encrypted channel. During the collaborative verification phase, the device decrypts the task in the TEE, executes the IMA task in batches, and signs the verification result. During the aggregation and consensus phase, the master device collects the signature results and determines whether a timeout has occurred. If so, the task is reassigned; otherwise, the dynamic consensus algorithm is executed. In the post-processing stage, an integrity report is generated and signed.

[0011] In S2, the master device allocates IMA verification tasks based on the weighted scores of the slave devices' computing capabilities, specifically including: Enter the list of files to be verified and set the capability parameters; Calculate the capability score, allocate tasks to file slices based on the capability score, determine whether critical files should be processed first, adjust the slice order if so, otherwise generate a task package including file, hash, and HMAC information. The task package is compressed and encrypted, and then broadcast to the slave device TEE.

[0012] In S2, the master device verifies the trustworthiness of the slave device TEE through remote authentication and generates a trust list.

[0013] The second aspect of this invention discloses a cross-device collaborative verification system based on TEE and IMA. The system includes a master device, slave devices, and a communication channel establishment unit, wherein: the master device verifies the trustworthiness of the slave device's TEE; the master device weights and allocates IMA verification tasks according to the slave devices' computing power scores; the slave devices verify the IMA in the TEE, sign it, and return the result; the communication channel establishment unit establishes a secure communication channel between the devices and transmits data through an encryption protocol; the master device summarizes the results through majority voting or the PBFT distributed consensus algorithm; wherein, the slave devices perform batch verification of IMA and anomaly detection in the TEE, and the master device performs dynamic distributed consensus confirmation.

[0014] A third aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect of the present invention.

[0015] The fourth aspect of the present invention discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the first aspect of the present invention.

[0016] In summary, this invention addresses the issues of low efficiency and insufficient reliability in integrity verification within distributed systems by proposing a cross-device collaborative verification method, system, device, and storage medium based on TEE (Trusted Execution Environment) and IMA (Integrity Measurement Framework). The master device distributes verification tasks through weighted task allocation, while slave device TEEs perform IMA checks in batches and detect anomalies. The master device then confirms the results using a dynamic consensus algorithm. This invention leverages the collaborative execution of IMA integrity checks across multiple devices' TEEs, sharing computational tasks and enhancing trust, thereby optimizing resource utilization and robustness, and improving the reliability of the results. It is particularly suitable for scenarios such as edge devices, blockchain, IoT, and IoV (Internet of Vehicles). Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the main equipment structure according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the slave device structure according to an embodiment of the present invention; Figure 3 This is a schematic diagram of master-slave device communication according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a cross-device collaborative verification process based on TEE and IMA according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the task allocation process according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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.

[0020] This invention discloses a cross-device collaborative verification method, system, device, and storage medium based on TEE and IMA, wherein the method includes (e.g.) Figure 1As shown): S1: The master device verifies the trustworthiness of the slave device's TEE; S2: The master device assigns IMA verification tasks based on the slave device's computing power score; S3: The slave device verifies the IMA in the TEE, signs it, and returns the result; S4: A secure communication channel is established between the devices, and data is transmitted through an encrypted protocol; S5: The master device summarizes the results through majority voting or the PBFT distributed consensus algorithm; In S2, the slave device performs batch verification of IMA and anomaly detection in the TEE, and in S5, the master device performs dynamic distributed consensus confirmation.

[0021] Communication scenarios between master devices, slave devices, and master-slave devices, such as Figure 1-3 As shown.

[0022] In one embodiment, weighted task allocation primarily addresses the limitations of single devices and insufficient dynamic flexibility, and can be divided into three aspects: 1) Calculate the device's capability score, assigning different weights based on the number of CPU cores, CPU frequency, memory, and network. Score = t1 cpu_num + t2 cpu_freq + t3 memory + t4 network (e.g., t1=0.3, t2=0.2, t3=0.3, t4=0.2, the weight values ​​can be adjusted according to the specific situation). 2) Based on the device capability score, large files are split into chunks, Task[i] = N (score[i] / ∑score), where N is the total file size; 3) Dynamic real-time adjustment mechanism to avoid task backlog. When the slave device load exceeds 80% or goes offline, the master device will reallocate tasks.

[0023] On edge devices, the firmware and data streams of edge nodes are verified. Devices with high capability scores are given priority in processing critical firmware or other large files, while devices with low capability scores are verified for some data such as sensor data.

[0024] In one embodiment, dynamic distributed consensus primarily addresses the problem of insufficient reliability of verification results, as follows: 1) When the number of devices is ≤ 5, consensus shall be reached by majority voting; 2) When the number of devices > 5, PBFT is adopted to tolerate L(n-1) / 3” malicious nodes; 3) Connection timed out, will be reassigned in 2 seconds.

[0025] In one embodiment, anomaly detection and handling primarily improve fault tolerance and reliability, as follows: 1) Task integrity check: HMAC is used to verify the task package; 2) Consistency check: Results with a deviation exceeding 20% ​​are marked as abnormal; 3) Test response: Send a pseudo-file for testing; 4) Processing: Remove abnormal devices and documents.

[0026] In one embodiment (e.g.) Figure 4 As shown in the diagram): During the initialization phase, the master device broadcasts a challenge code, and the slave devices respond with a TEE signature. If the response is trustworthy, the task allocation phase begins; otherwise, the untrustworthy device is removed. During the task allocation phase, the master device calculates the capability score of the slave devices, segments the file, and generates a task package with weighted allocation. The task is then broadcast through an encrypted channel. During the collaborative verification phase, the slave devices decrypt the task in the TEE, execute the IMA task in batches, and sign the verification result. During the aggregation and consensus phase, the master device collects the signature results, determines if a timeout has occurred, and if so, reassigns the task; otherwise, it executes the dynamic consensus algorithm. During the post-processing phase, an integrity report is generated and signed.

[0027] In one embodiment (e.g.) Figure 5 As shown in Figure S2, the master device assigns IMA verification tasks based on the weighted computing capability scores of the slave devices. Specifically, this includes: inputting a list of files to be verified and setting capability parameters; calculating capability scores, allocating tasks to files by fragmenting them according to their capability scores, determining whether critical files should be processed first, adjusting the fragmentation order if so, otherwise generating a task package including file, hash, and HMAC information; compressing and encrypting the task package, and broadcasting it to the slave device's TEE.

[0028] In one embodiment: the master device weights and allocates tasks, the slave device TEE performs batch verification and detects anomalies, and finally the master device confirms the results through dynamic distributed consensus; the master device verifies the trustworthiness of the slave device TEE through remote proof, generates a trusted list, and weights and allocates tasks according to the slave device's capability score, file fragmentation, etc., to form verification tasks; the master device distributes tasks to the slave devices through a secure channel, and the slave devices verify the tasks according to HMAC; the slave device TEE performs batch execution of IMA verification tasks, detects anomalies, and signs the results; the master device collects the results and confirms their integrity through dynamic distributed consensus and anomaly detection. This invention is applied to distributed application scenarios to perform dynamic weighted task allocation, anomaly detection and handling, and dynamic consensus.

[0029] The present invention also provides a system comprising: a master device responsible for distributing tasks, summarizing results, and making final decisions, including a task distribution module, a consensus module, and a remote proof module; a slave device running a TEE to execute assigned verification tasks, including a TEE verifier module and a cache module; and a storage medium storing instructions and data for executing the above methods.

[0030] This invention improves the verification efficiency of large-scale systems, enhances trust in distributed environments, and dynamically weights task allocation to maximize resource utilization. In the case of unstable edge networks, this invention makes timely adjustments to avoid task backlog and maintains verification integrity even after removing problematic nodes.

[0031] In one embodiment (edge ​​computing cluster (basic workload)): Scenario: Verifying firmware and logs (200 files) with an edge server and 4 edge nodes. Configuration: Master device (CPU 2.5GHz, RAM 8GB), Slave device (CPU 1.5-2.0GHz, RAM 4GB); Execution: Tasks are allocated based on computing power scores: 60:50:50:40; Batch verification: 10 files per batch, no HMAC anomalies; Encrypted communication: TLS 1.3; Consensus: Majority voting.

[0032] Quantitative results: Time taken: approximately 4.2 seconds (approximately 12.8 seconds for a single device); Efficiency improvement: 67% ((12.8–4.2) / 12.8); Reliability: 99.8%.

[0033] In one embodiment (edge ​​device-smart home network (massive workload)): Scenario: Verifying firmware, logs, and configuration files (approximately 500) between a smart home gateway and 10 edge devices (gateways, sensors, cameras, etc.). Configuration: Master device (CPU 2.8GHz, RAM 16GB), Slave device (CPU 0.8-2.0GHz, RAM 1-4GB); Execution: Weighted task allocation: 70:60:50:50:40:30:30:20:20:10, with critical firmware tasks prioritized for high-scoring devices; Batch verification: 15 files per batch, HMAC checks one abnormal device (25% deviation), which is then removed and reassigned; Encrypted communication: TLS 1.3; Consensus: PBFT (tolerates 3 malicious nodes), 2-second timeout. Quantitative results: Time taken: approximately 6.8 seconds (approximately 22.5 seconds for a single device); Efficiency improvement: 69% ((22.5–6.8) / 22.5); Reliability: 99.7%.

[0034] In one embodiment (edge ​​device - industrial IoT (hyperscale workload)): Scenario: Verifying firmware, data streams, and configuration files (approximately 1000) between an industrial gateway and 15 edge devices (sensors, actuators, etc.). Configuration: Master device (CPU 3.0GHz, memory 32GB), slave device (CPU 0.5-1.5GHz, memory 512MB-2GB); Execution: Weighted task allocation: 100:90:80:70:60:50:40:30:20:15:10:10:10:5:5, dynamically adjusting 2 offline devices; Batch verification: 20 files per batch, anomaly check to remove 2 abnormal devices (consistency deviation 30%). Encrypted communication: TLS 1.3; Consensus: PBFT (tolerates 4 malicious nodes), timeout 2 seconds; Quantitative results: Time taken: approximately 12.5 seconds (approximately 45.0 seconds for a single device); Efficiency improvement: 72% ((45.0–12.5) / 45.0); Reliability: 99.6%.

[0035] The technical effects brought about by this invention include: cross-device collaboration to improve efficiency by utilizing the resources of multiple devices; dynamic distributed consensus and anomaly detection and handling to ensure the credibility of results; weighted allocation and fault-tolerant design to optimize resource utilization, applicable to edge devices and other scenarios.

[0036] This invention addresses the issues of low efficiency and insufficient reliability in integrity verification within distributed systems. It proposes a cross-device collaborative verification method, system, device, and storage medium based on a Trusted Execution Environment (TEE) and an Integrity Measurement Framework (IMA). The master device distributes verification tasks through weighted task allocation. Slave devices' TEEs perform batch IMA checks and detect anomalies. The master device then confirms the results using a dynamic consensus algorithm. This invention leverages the collaborative execution of IMA integrity checks across multiple devices' TEEs, distributing computational tasks and enhancing trust. It optimizes resource utilization and robustness, and improves the reliability of the results, making it particularly suitable for edge devices, blockchain, IoT, and connected vehicle scenarios.

[0037] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. The above embodiments only illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A cross-device collaborative verification method based on TEE and IMA, characterized in that, The method includes: S1: The master device verifies the trustworthiness of the slave device's TEE; S2: The master device allocates IMA verification tasks based on the weighted scores of the slave devices' computing capabilities; S3: Verify the IMA from the device in the TEE, sign it, and return the result; S4: Establish a secure communication channel between devices and transmit data using an encrypted protocol; S5: The master device aggregates the results through majority voting or the PBFT distributed consensus algorithm; In S2, the slave device performs batch verification of IMA and anomaly detection in the TEE. In S5, the master device performs dynamic distributed consensus confirmation.

2. The cross-device collaborative verification method based on TEE and IMA according to claim 1, characterized in that, In S2, the weighted allocation IMA verification task specifically includes: The computing device's capability score is weighted differently based on the number of CPU cores, CPU clock speed, memory, and network connectivity. Score=t1 cpu_num+t2 cpu_freq+t3 memory+t4 network; Where t1, t2, t3, and t4 represent weights; Files are segmented based on device capability scores: Task[i]=N (score[i] / ∑score) Where N is the total file size; A dynamic real-time adjustment mechanism is implemented so that when a slave device's load exceeds a threshold or it goes offline, the master device reallocates tasks. On edge devices, the firmware and data streams of edge nodes are verified, and devices with higher capability scores are processed first for critical firmware or other large files.

3. The cross-device collaborative verification method based on TEE and IMA according to claim 2, characterized in that, In S5, dynamic distributed consensus is used to improve the credibility of verification results, where: When the number of devices is less than or equal to the discrimination value, a consensus is reached through majority voting. When the number of devices exceeds the discrimination value, the PBFT distributed consensus algorithm is used to tolerate L(n-1) / 3 malicious nodes; If the connection times out, it will be reassigned.

4. The cross-device collaborative verification method based on TEE and IMA according to claim 3, characterized in that, In S2, anomaly detection from the device specifically includes: Task integrity check: HMAC is used to verify the task package; Consistency check: Mark an anomaly when the result deviation exceeds a threshold; Test response: Send a pseudo-file for testing; Follow-up processing: Remove abnormal devices and documents.

5. The cross-device collaborative verification method based on TEE and IMA according to claim 4, characterized in that, In the method: During the initialization phase, the master device broadcasts a challenge code, and the slave device responds with a TEE signature. If the verification is trustworthy, the task allocation phase begins; if the task is untrustworthy, the untrustworthy device is removed. During the task allocation phase, the master device calculates the capability score of the slave device, segments the file, and generates a task package by weighted allocation, and broadcasts the task through an encrypted channel. During the collaborative verification phase, the device decrypts the task in the TEE, executes the IMA task in batches, and signs the verification result. During the aggregation and consensus phase, the master device collects the signature results and determines whether a timeout has occurred. If so, the task is reassigned; otherwise, the dynamic consensus algorithm is executed. In the post-processing stage, an integrity report is generated and signed.

6. The cross-device collaborative verification method based on TEE and IMA according to claim 5, characterized in that, In S2, the master device allocates IMA verification tasks based on the weighted scores of the slave devices' computing capabilities, specifically including: Enter the list of files to be verified and set the capability parameters; Calculate the capability score, allocate tasks to file slices based on the capability score, determine whether critical files should be processed first, adjust the slice order if so, otherwise generate a task package including file, hash, and HMAC information. The task package is compressed and encrypted, and then broadcast to the slave device TEE.

7. The cross-device collaborative verification method based on TEE and IMA according to claim 6, characterized in that, In S2, the master device verifies the trustworthiness of the slave device TEE through remote authentication and generates a trust list.

8. A cross-device collaborative verification system based on TEE and IMA, characterized in that, The system includes a master device, slave devices, and a communication channel establishment unit, wherein: The master device verifies the trustworthiness of the slave device's TEE; The master device allocates IMA verification tasks based on the weighted scores of the slave devices' computing capabilities; The device verifies the IMA in the TEE, signs it, and returns the result. The communication channel establishment unit establishes a secure communication channel between devices and transmits data using an encryption protocol. The master device aggregates the results through majority voting or the PBFT distributed consensus algorithm; In this process, the slave device performs batch verification of IMA and anomaly detection in the TEE, while the master device performs dynamic distributed consensus confirmation.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-7.

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