A remote attestation method for trusted data space

Abstract

The present invention discloses a remote attestation method for a trusted data space, which relates to the field of computer and data security technology. First, a trusted environment is initialized for heterogeneous terminals containing CPUs, GPUs, and FPGAs based on hardware security modules and physically unclonable functions to generate dynamic identity keys. The verifier generates a challenge value using quantum random numbers, which is transmitted to the prover via quantum encryption and neuromorphic photonic networks. The prover constructs a proof using a tensor decomposition zero-knowledge proof protocol, combines a spatiotemporal causal graph with quantum signature aggregation to complete multi-level verification on the verifier, and finally implements dynamic trust evaluation and adaptive strategy adjustment through quantum Bayesian networks, reinforcement learning, and biofeedback. The present invention improves the remote attestation performance of trusted data spaces, implements dynamic trust evaluation with the help of quantum Bayesian networks and biofeedback, effectively resists attacks, meets real-time needs, accurately evaluates trust, and promotes the development of trusted data spaces.

Description

Technical Field

[0001] The present invention relates to the field of computer and data security technology, and in particular to a remote certification method for a trusted data space. Background Art

[0002] As digital transformation accelerates, the core requirement of a trusted data space, as a key infrastructure for ensuring data security, privacy, and compliance interactions, is to establish a trusted verification mechanism for devices and data. Remote attestation technology, as the core means of achieving trusted device authentication, bears the heavy responsibility of verifying the authenticity of the terminal environment and the compliance of data processing. Traditional remote attestation methods rely on static hardware identification and fixed software hash values ​​for verification, and are difficult to resist attacks in dynamic environments, such as hardware cloning and firmware tampering. With the explosive growth of edge computing and IoT devices, device heterogeneity has significantly increased. Traditional solutions are unable to achieve full-link trusted initialization and dynamic security protection when dealing with hybrid computing architectures such as CPUs, GPUs, and FPGAs, resulting in the risk of a weak trust foundation in the data space.

[0003] Existing remote attestation technologies suffer from significant flaws in balancing efficiency and security. Some schemes employ complex cryptographic protocols to implement zero-knowledge proofs, but the high computational overhead makes them difficult to apply to resource-constrained end devices. Lightweight schemes, however, simplify the verification process and are unable to effectively defend against threats such as replay attacks and man-in-the-middle attacks. Furthermore, when processing large-scale data (such as machine learning model parameters and sensor data streams), traditional zero-knowledge proof technology experiences exponentially increased proof generation and verification time, failing to meet real-time requirements. Furthermore, the storage and management of proof information also present challenges. Traditional storage methods cannot guarantee the long-term security of data, nor can they achieve efficient retrieval and verification.

[0004] With the development of quantum computing technology, traditional encryption and proof mechanisms based on mathematical puzzles are at risk of being cracked, while existing remote proof solutions have yet to form an effective quantum security protection system. In the field of dynamic trust assessment, traditional methods often use static strategies or simple scoring models, which cannot adapt to the rapid changes in device status, usage scenarios, and security threats in the data space. This causes trust assessments to lag behind actual risks and makes it difficult to provide timely and effective defense responses. Furthermore, the lack of cross-domain and cross-device trust transfer mechanisms makes trusted interaction between different data spaces difficult to achieve, severely restricting the efficiency of data circulation and sharing. Summary of the Invention

[0005] The present invention proposes a remote attestation method for a trusted data space to solve the problems mentioned in the above-mentioned prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a remote attestation method for a trusted data space, comprising the following steps:

[0007] Initialization steps for a trusted environment for heterogeneous terminals: Build a dynamic isolation domain based on a hardware root of trust. This achieves full-link trustworthiness for computing resources through CPU microcode-level verification combined with GPU rasterization encryption and FPGA bitstream dynamic watermarking. Device identity keys are generated using a combination of physically unclonable functions and chaotic circuits.

[0008] Remote attestation request generation and transmission steps: Design a quantum-inspired random challenge generation algorithm, use a quantum random number generator to generate a true random number sequence, and construct a time-space bound challenge value through quantum entanglement effects. Use a neuromorphic photonic network to realize the transmission of attestation requests;

[0009] Lightweight zero-knowledge proof construction steps: Propose a tensor decomposition zero-knowledge proof protocol, through the formula Achieve privacy protection for high-dimensional data, where It is the abbreviation of zero-knowledge proof of tensor decomposition. Represents the original high-dimensional tensor data, F is the quantum Fourier transform function, Mapping to low-dimensional space reduces the computational complexity from traditional down to , S is the data sensitivity index, R is the computing resource parameter, and the function G dynamically adjusts the proof granularity according to S and R. Ordinary data uses coarse-grained proof, and finally generates a lightweight zero-knowledge proof P;

[0010] The multi-level aggregation verification mechanism involves designing a spatiotemporal causal graph verification framework, modeling device state changes as a directed acyclic graph, and verifying the consistency of system behavior by examining the causal relationships between nodes in the graph. Furthermore, quantum signature aggregation technology is introduced to compress the signatures of the proving nodes into a single verification point.

[0011] Dynamic trust evaluation and feedback steps: Construct a quantum Bayesian trust inference model to achieve long-distance correlation of trust propagation through quantum entanglement effect; design an adaptive trust decay surface to generate a trust decay curve based on multi-dimensional parameters; develop a biofeedback trust regulation mechanism to dynamically adjust the trust threshold by analyzing the biological characteristics of the equipment operator.

[0012] Furthermore, it also includes: in the initialization step of the heterogeneous terminal trusted environment, realizing atomic transaction verification across heterogeneous architectures, ensuring the atomicity of data transmission and computing operations between CPU, GPU and FPGA through transactional memory technology; adopting a blockchain-based hardware supply chain traceability mechanism to generate tamper-proof trusted logs for the entire process from chip manufacturing to device assembly, and combining material genomics technology to verify the consistency of the physical properties of hardware components with design specifications.

[0013] Furthermore, it also includes: in the lightweight zero-knowledge proof construction step, proposing a holographic zero-knowledge proof protocol, which converts physical phenomena in three-dimensional space into mathematical proofs, and uses the determinism of physical laws to verify the correctness of the calculation process; developing a proof archiving system based on DNA storage, encoding zero-knowledge proof information into DNA sequences, and using the storage characteristics of DNA to achieve long-term and secure storage of proofs.

[0014] Furthermore, it also includes: in the multi-level aggregation verification mechanism step, realizing a quantum entanglement verification network, using the characteristics of quantum entanglement to build distributed verification nodes, when a node detects an abnormality, the other nodes entangled with it perceive and synchronously adjust the verification strategy; introducing a cognitive verification agent, which learns normal system behavior patterns based on a neural network model, identifies unknown types of attack patterns and generates adaptive verification rules.

[0015] Furthermore, it also includes: in the dynamic trust evaluation and feedback step, constructing a social physical trust network, through the formula Fusion calculates the trust value, where Represents the trust value, Represents the social behavior characteristics of people. Represents the physical characteristics of the device, function It represents the rules or methods of integrated computing. At the same time, it develops a trust contagion control mechanism. When anomalies occur in the local trust domain, the optimal isolation strategy is calculated through the game theory model to prevent the spread of the trust crisis.

[0016] Furthermore, the steps of initializing the trusted environment for heterogeneous terminals also include: implementing nanoscale hardware watermarking technology to form nanoscale physical features through ion implantation during the chip manufacturing process. These features can serve as indelible hardware identity identifiers; building a living hardware verification system to verify whether the hardware is in normal working condition by monitoring the quantum tunneling effect and thermal noise fluctuations inside the chip, thereby preventing hardware cloning and side-channel attacks.

[0017] Furthermore, the remote proof request generation and transmission step also includes: designing a neuromorphic proof carrier, encoding the proof information into a neural pulse sequence, and utilizing the low power consumption and high fault tolerance characteristics of the pulse neural network to achieve transmission; using the principle of quantum teleportation to construct a long-distance transmission channel for the proof information, and realizing the secure transmission of the proof information without actually transmitting the physical carrier.

[0018] Furthermore, the lightweight zero-knowledge proof construction step also includes: developing a proof generation algorithm based on swarm intelligence, simulating ant colony optimization and bee colony decision-making mechanism, and assuming that the proof task set is , decompose it into multiple subtasks for parallel processing, introduce the group collaboration factor C, and use the formula Generate the optimal proof path P, where is a set of proof paths, It is a subtask The weight of Is the path p processing subtask At the same time, it realizes the visualization of stream of consciousness proof, transforms the zero-knowledge proof process into a perceptible visual pattern, and enables the verifier to intuitively understand the proof logic.

[0019] Furthermore, the multi-level aggregation verification mechanism also includes: constructing a space-time curvature verification model, mapping device state changes into a four-dimensional space-time coordinate system, and identifying system behavior deviations by detecting space-time curvature anomalies; developing a virtual verification environment based on the metaverse, mapping physical devices into virtual space for stress testing and attack simulation, and verifying the credibility of the device under extreme conditions.

[0020] Furthermore, the dynamic trust evaluation and feedback step also includes: implementing emotional computing trust regulation, dynamically adjusting the trust threshold by analyzing the emotional state of the user during the interaction with the system; constructing a quantum trust ledger, using the quantum no-cloning theorem to ensure the security of trust records, and achieving superlinear acceleration of trust evaluation through quantum parallel computing.

[0021] Compared with the existing technology, the beneficial effects of the present invention are:

[0022] In terms of security, a dynamic isolation domain based on a hardware root of trust is constructed. This combines physically unclonable functions with chaotic circuits to generate dynamic identity keys, achieving strong authentication and tamper resistance for device identity. Quantum-inspired encryption technology and neuromorphic photonic networks are used to transmit proof requests, ensuring absolute data security during transmission. Holographic zero-knowledge proof protocols and quantum signature aggregation technology defend against attacks throughout the entire proof construction and verification process, effectively mitigating the risk of quantum computing cracking and building a solid security defense for the trusted data space.

[0023] In terms of efficiency optimization, the tensor decomposition zero-knowledge proof protocol, combined with the quantum Fourier transform, significantly reduces the computational complexity of proofs, shortening proof generation time to a fraction of traditional methods when processing large amounts of data. Neuromorphic photonic networks enable ultra-high-speed transmission of proof requests, and quantum signature aggregation technology improves verification efficiency by multiple orders of magnitude, enabling the system to meet the needs of high-concurrency, real-time remote proofing.

[0024] At the dynamic adaptability level, the quantum Bayesian trust inference model leverages the principle of quantum superposition to implement multi-dimensional trust state assessment. Combined with reinforcement learning, it dynamically adjusts trust decay strategies, enabling rapid response to device status and environmental changes. The biofeedback trust regulation mechanism incorporates human factors into the assessment system, while the socio-physical trust network integrates device physical characteristics with social behavioral data to enhance trust assessment accuracy. Furthermore, cross-domain trust transfer and dynamic defense strategies ensure the sustained and stable operation of the trusted data space in complex environments.

[0025] Overall, the technology in this application effectively solves the bottlenecks of traditional solutions in terms of security, efficiency, and adaptability, provides reliable technical support for the construction and development of trusted data space, and has significant application value and social and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic block diagram of a remote attestation method for a trusted data space proposed by the present invention;

[0027] Figure 2 A bar chart comparing the security of trusted environments for heterogeneous terminals;

[0028] Figure 3 Transmit performance line graph for remote attestation requests;

[0029] Figure 4 The bar chart shows the comparison of abnormal behavior detection rates of different verification technologies. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0031] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0032] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the accompanying drawings.

[0033] Reference Figures 1 to 4 :A remote attestation method for a trusted data space, comprising the following steps:

[0034] Heterogeneous Terminal Trust Environment Initialization Steps: In this embodiment, an edge computing node is used as a heterogeneous terminal device. This device integrates an Intel Xeon CPU, an NVIDIA A100 GPU, a Xilinx Virtex UltraScale+ FPGA, and an independent hardware security module (HSM). When the device boots, the BIOS / UEFI firmware first loads the Trusted Execution Environment (TEE). This solution uses ARM TrustZone technology to isolate the TEE from the normal execution environment.

[0035] To generate a unique device identity key pair, the system invokes the elliptic curve cryptography (ECC) algorithm within the HSM to generate a key pair (SK, PK) based on the P-256 curve. Simultaneously, the device's hardware fingerprint is calculated by reading information such as the CPU microcode version, GPU core frequency, and the hash value of the FPGA configuration bitstream, combined with hardware parameters such as memory capacity and hard drive serial number. To ensure a strong binding between the key and the hardware fingerprint, the PCR (Platform Configuration Register) of the Trusted Platform Module (TPM2.0) is used for storage. Each time the device restarts, the hardware fingerprint is verified to be consistent with the PCR record.

[0036] To address the dynamic reconfiguration requirements of FPGAs, this solution uses the Xilinx Vivado tool chain to implement partial dynamic reconfiguration. During system operation, a new configuration bitstream is sent to the FPGA via the AXI bus. Simultaneously, a formal verification module written in the hardware description language (Verilog) verifies the reconfigured hardware logic using the Coq theorem prover to ensure compliance with safety specifications. A hardware configuration proof containing the verification results is then generated.

[0037] To further enhance security, this solution incorporates a key generation technology that combines a physically unclonable function (PUF) with a chaotic circuit. The ring oscillator within the FPGA serves as the PUF unit, generating a random response by measuring the frequency differences between different oscillators. Simultaneously, a chaotic circuit integrated within the CPU chip generates a chaotic signal that is XORed with the PUF response to generate a dynamic identity key. This key is updated every millisecond, and during the update process, physical parameters such as device temperature and electromagnetic radiation are collected in real time as entropy sources to ensure key unpredictability.

[0038] Remote attestation request generation and transmission steps: In the remote attestation process, the verifier is deployed in a cloud data center using an Intel Xeon Platinum 8380 processor cluster, while the prover is an edge computing node. The verifier first uses a quantum random number generator (QRNG) to generate a true random number sequence. Leveraging quantum entanglement, this sequence is bound to a timestamp and geolocation information to generate a challenge value (nonce) that is unique across time and space. To ensure the secure transmission of the challenge value, this solution establishes a quantum-inspired communication channel. Quantum key distribution (QKD) technology is used to establish a quantum key between the verifier and the prover, which is used to symmetrically encrypt the challenge value. Simultaneously, identity-based cryptography (IBE) is employed to directly map the device's hardware fingerprint to a public key for asymmetric encryption of the quantum key. Furthermore, to achieve ultra-high-speed transmission, a neuromorphic photonic network, composed of optical neurons and optical synapses, is introduced. This network can adaptively adjust the photon transmission path based on network traffic and interference. In this embodiment, the neuromorphic photonic network is implemented using a silicon photonic chip, optical neurons are constructed based on microring resonators, and optical synapses achieve signal modulation through a Mach-Zehnder interferometer.

[0039] Lightweight zero-knowledge proof construction steps: In this embodiment, the prover needs to provide a credible proof of the training process of the deep learning model. First, the tensor decomposition zero-knowledge proof (TD-ZKP) protocol is used to pass the formula , the model parameter tensor is mapped to a low-dimensional space through quantum Fourier transform (QFT). F is the quantum Fourier transform function, S is the data sensitivity index, R is the computing resource parameter, and the function G dynamically adjusts the proof granularity according to S and R. Specifically, the GoogleCirq library is used to build a quantum circuit to implement the QFT operation. This operation is based on the above formula and converts the original The complexity of the proof calculation is reduced to .in, It is the abbreviation of zero-knowledge proof of tensor decomposition. Represents the original high-dimensional tensor data (such as deep learning model parameters, sensor stream data), F is the quantum Fourier transform function, which can be Mapping to a low-dimensional space.

[0040] In order to achieve adaptive proof granularity control, the system monitors computing resource utilization and data sensitivity in real time. When it is detected that the GPU utilization exceeds 80%, the level of detail of the proof is automatically reduced; for data containing personal sensitive information, fine-grained proof is mandatory. In this solution, fine-grained proof verifies the input data, intermediate results, and outputs of each training batch, while coarse-grained proof only verifies the final output and training parameters of the model. For the trusted proof of machine learning models, this solution has developed a dedicated zero-knowledge proof protocol. The protocol is based on a polynomial commitment scheme, which encodes the model parameters into polynomials and proves the compliance of the model training process by verifying the correctness of the polynomials. At the same time, DNA storage technology is introduced to encode zero-knowledge proof information into DNA sequences and store them in a synthetic DNA library. In this embodiment, TwistBioscience's DNA synthesis technology is used to achieve storage of 10 per gram of DNA. 15 Bit data, with a storage life of more than a thousand years.

[0041] Multi-level aggregation verification mechanism steps: During the verification process, the verifier constructs a spatiotemporal causal graph verification framework. The device's system state changes (such as process startup, file reading and writing, and network connection) are modeled as a directed acyclic graph (DAG), where each node represents a state change event and edges represent the causal relationship between events. The DAG is analyzed using a graph neural network (GNN) to detect abnormal causal relationships between nodes. In this embodiment, the PyTorchGeometric library is used to implement the GNN model, and the training data is derived from historical normal system behavior logs. To improve verification efficiency, this solution introduces quantum signature aggregation technology. Utilizing the principle of quantum state superposition, the signature information of multiple proof nodes is encoded into the quantum state and compressed into a single verification point through quantum measurement operations.

[0042] The system also deploys a physical environment perception verification module, which uses integrated microphones, cameras, and environmental sensors to collect sound, images, and environmental parameters around the device. It uses a convolutional neural network (CNN) to analyze the collected sound and image data to determine whether the device is in a normal operating environment. It also compares environmental parameters with historical data to verify the authenticity of the device's operating status.

[0043] Dynamic Trust Assessment and Feedback Step: During the dynamic trust assessment phase, the verifier constructs a quantum Bayesian trust inference model. Leveraging the principle of quantum superposition, it simultaneously assesses multiple trust states of the device (e.g., high trust, medium trust, and low trust). Quantum entanglement effects enable remote correlation between different trust states. In this embodiment, an IBM quantum computing simulator is used to implement a quantum Bayesian network, updating the trust assessment results by adjusting the probability amplitude of the quantum state.

[0044] To achieve adaptive trust decay, the system constructs a multidimensional trust decay surface. The parameters of this surface are dynamically adjusted by a reinforcement learning agent, which uses information such as device type, data sensitivity, and usage scenario as input and is trained using trust assessment errors as reward signals.

[0045] In addition, the system integrates a biofeedback trust regulation mechanism, which uses wearable devices to collect the operator's biometrics, such as heart rate and brain waves. If the operator's anxiety is detected, the device's trust threshold is automatically lowered; when the operator's condition stabilizes, the trust threshold is gradually raised.

[0046] The present invention also includes: in the step of initializing the trusted environment of the heterogeneous terminal, implementing atomic transaction verification across heterogeneous architectures. The atomicity of transaction operations on the CPU is ensured by Intel Transactional Synchronization Extensions (TSX) technology; on the GPU side, the atomicity of video memory operations is achieved by using CUDA's atomic operation functions; for FPGAs, the atomicity of data transmission is achieved by using the transaction control mechanism of the AXI bus protocol. At the same time, blockchain technology is used to record hardware supply chain information. From chip manufacturing to each link of device assembly, key information (such as manufacturing date, quality inspection report) is uploaded to the chain, and Ethereum smart contracts are used to achieve traceability verification of hardware components. Combined with material genomics technology, the physical properties of hardware components (such as semiconductor band structure and metal resistivity) are theoretically calculated and compared with actual measured values ​​to ensure that the hardware meets the design specifications.

[0047] The present invention also includes: During the lightweight zero-knowledge proof construction step, a holographic zero-knowledge proof (Holo-ZKP) protocol is proposed. This protocol utilizes the principle of optical interference to encode proof information as the phase and amplitude information of light. Specifically, the proof data is loaded onto a laser beam using a spatial light modulator (SLM). After transformation through a series of optical elements (such as lenses and prisms), the light propagation path and interference pattern conform to the proof logic. By detecting the final state of the light, the verifier can verify the correctness of the proof, leveraging the deterministic laws of physics to ensure the reliability of the proof. Simultaneously, a proof archiving system based on DNA storage is developed. The zero-knowledge proof information is encoded into a DNA sequence according to the principle of complementary base pairing. Using synthetic biology techniques, the DNA sequence is stored in a laboratory cryogenic storage vault. When verification is required, the sequence information is read using DNA sequencing technology and decoded into the original proof data.

[0048] The present invention also includes: in the step of the multi-level aggregation verification mechanism, a quantum entanglement verification network is constructed. Quantum channels are established between multiple verification nodes using quantum entangled photon pairs. When a node detects an anomaly, other nodes entangled with it can instantly perceive and synchronously adjust the verification strategy. In the specific implementation, the quantum entanglement distribution technology developed by the University of Science and Technology of China is adopted to achieve stable transmission of quantum entangled states within a range of 100 kilometers. A cognitive verification agent is introduced, and a neural network model is constructed based on the Transformer architecture. The model learns a large number of normal system behavior logs and can identify unknown types of attack patterns. When abnormal behavior is detected, the cognitive verification agent automatically generates adaptive verification rules and sends them to each verification node to achieve dynamic defense.

[0049] The present invention also includes: in the dynamic trust evaluation and feedback step, building a social physical trust network (CPTN). The trust value is calculated by integrating the social behavior characteristics of people and the physical characteristics of devices, specifically through the formula Trust values ​​are calculated through fusion. Here, T represents the trust value, and SB represents the social behavioral characteristics of a person, such as reputation on social platforms obtained through web crawling technology and social relationship data such as collaborations with others analyzed using graph convolutional networks (GCNs). EP represents the physical characteristics of the device, using GPS and sensors to collect information such as the device's location and operating status. Function f represents the rules or methods for fusion calculation, which inputs these two types of information into the trust assessment model for comprehensive calculation. Simultaneously, a trust contagion control mechanism is developed. Based on a game theory model, when an anomaly occurs in a local trust domain, an optimal isolation strategy is calculated, including measures such as disconnecting the network and restricting data access, to prevent the spread of the trust crisis.

[0050] Furthermore, we implement trust regulation through affective computing. We use computer vision and natural language processing technologies to analyze emotional information, such as facial expressions and language, during user interactions with the system. Specifically, we use the OpenFace library for facial recognition and the BERT model for sentiment analysis. Based on the analysis results, we dynamically adjust the trust threshold. For example, if a user expresses dissatisfaction, we lower the device's trust level and increase the verification frequency.

[0051] In the present invention, the step of initializing the trusted environment of heterogeneous terminals also includes: implementing nanoscale hardware watermarking technology to form nanoscale physical features through ion implantation during the chip manufacturing process. These features can serve as indelible hardware identity identifiers; building a living hardware verification system to verify whether the hardware is in normal working condition by monitoring the quantum tunneling effect and thermal noise fluctuations inside the chip, thereby preventing hardware cloning and side-channel attacks.

[0052] In the present invention, the remote attestation request generation and transmission steps further include: when designing a neuromorphic attestation carrier, digitally preprocessing the attestation information to convert it into a binary data stream. Then, using specialized encoding rules, the binary data stream is mapped into neural pulse sequences. These sequences, similar to the way biological neurons transmit information, represent the attestation information in the form of discrete electrical pulses. Transmission is performed using a spiking neural network (SNN). SNNs mimic the working mode of biological neurons, triggering output pulses only when receiving input pulses of sufficient intensity. This event-driven computing approach significantly reduces power consumption. Furthermore, due to the discrete and sparse nature of the pulse signal, even if some pulses are lost or misinterpreted during transmission, they are unlikely to significantly impact the overall information, resulting in high fault tolerance. During transmission, parameters such as the connection weights between neurons in the SNN and pulse emission timing are precisely adjusted to ensure that the neural pulse sequence accurately and efficiently transmits the attestation information.

[0053] The construction of a long-distance transmission channel for proof information is based on the principle of quantum teleportation, which relies on quantum entanglement. First, through specialized quantum preparation techniques, such as laser cooling and trapped ion technology, a pair of entangled quantum bits (such as photon pairs) is prepared. A quantum state correlation operation is performed on one of the qubits (the transmitter) and the proof information to be transmitted. Without actually transmitting a physical carrier, the non-local nature of quantum entanglement allows the quantum state associated with the sender to be instantaneously displayed on the other entangled qubit at the receiver, thus enabling the secure long-distance transmission of the proof information.

[0054] In the present invention, the lightweight zero-knowledge proof construction step also includes: developing a proof generation algorithm based on swarm intelligence, simulating ant colony optimization and bee colony decision-making mechanism. Assume that the proof task set is , decompose it into multiple subtasks for parallel processing. The group collaboration factor C is introduced. Specifically, when simulating the ant colony optimization mechanism, the proof task is compared to the process of ants looking for food sources. Each ant represents a computing unit. When searching for the proof path, it selects the next path by releasing and sensing the concentration of pheromones. Paths with high pheromone concentrations are more likely to be selected, just like better paths are explored first in the proof task. When simulating the bee colony decision-making mechanism, different proof strategies are regarded as information conveyed by different dance movements of bees. Scout bees will explore different proof paths and convey the pros and cons of the paths to other bees (computing units) through specific dances, guiding the bee colony (computing resources) to converge on better proof paths. Through the simulation of these two mechanisms, the proof task set is More efficient processing planning. Generate the optimal proof path P, where is a set of proof paths, It is a subtask The weight of Is the path p processing subtask At the same time, the stream-of-consciousness proof visualization is realized, which transforms the zero-knowledge proof process into a perceptible visual pattern, allowing the verifier to intuitively understand the proof logic.

[0055] In the present invention, the multi-level aggregation verification mechanism step also includes: when constructing the space-time curvature verification model, first sort out the various parameters of the device state change, such as the device's operating speed, position movement, performance indicator fluctuations, etc. These parameters are converted into coordinate values ​​in a four-dimensional space-time coordinate system according to specific mapping rules. The four-dimensional space-time coordinate system here includes three-dimensional spatial dimensions (length, width, height) and one-dimensional time dimension. In this coordinate system, the state changes of the device during normal operation will form a relatively smooth trajectory. The space-time curvature is calculated by a complex mathematical algorithm. Once an abnormality in the space-time curvature is detected, such as a sudden increase in curvature or irregular fluctuations, it means that the system behavior has deviated, and there may be safety hazards or operational failures.

[0056] When developing a virtual verification environment based on the Metaverse, advanced digital twin technology is used to accurately model the structure, functions, and operational logic of physical devices, thereby mapping them into a virtual space. Within this virtual space, various extreme scenarios are constructed to stress-test the mapped virtual devices, simulating high loads and resource shortages to observe their performance and operational stability. Simultaneously, attack simulations are conducted using a variety of cyberattack methods, such as vulnerability exploits and denial-of-service attacks commonly used by hackers, to test the device's defense capabilities and data protection capabilities under attack, thereby verifying the device's trustworthiness under extreme conditions.

[0057] In the present invention, the dynamic trust assessment and feedback steps also include: When implementing affective computing trust regulation, a multimodal emotion perception system is first deployed. A camera captures the user's facial micro-expressions. OpenCV is used for image preprocessing and facial key point extraction. The OpenFace library is then used to analyze facial features and identify subtle changes in expression. Simultaneously, a microphone array captures the user's voice, and the librosa library is used to extract audio features. The system then uses the BERT model to analyze the emotional tendencies of the voice, including intonation and volume changes. Furthermore, the system collects interactive behavior data such as user click frequency and input speed. This multimodal data is input into a fused emotion analysis model, which processes time series data using an LSTM network and weightedly fuses features from different modalities using an attention mechanism. The model then outputs a probability distribution of the user's emotional state, such as anxiety, satisfaction, or neutrality. If the user is detected to be anxious, the system automatically lowers the trust threshold of the current interactive device, increases the frequency of identity verification, and requires additional gesture verification or biometric recognition. If the user expresses satisfaction, the trust threshold is raised, streamlining subsequent interactions.

[0058] The quantum trust ledger is constructed based on quantum key distribution (QKD) technology, employing the BB84 protocol or its variants. Keys are encoded in the polarization state of single photons, and the quantum no-cloning theorem ensures absolute key transmission security. Trust records are stored in quantum bits (qubits). Leveraging the principle of quantum superposition, each qubit simultaneously represents a superposition of 0 and 1. Quantum entanglement and quantum measurement techniques enable synchronous ledger updates. During trust assessment, quantum parallel computing leverages the capabilities of quantum computing to simultaneously process multiple trust factors, such as historical device behavior, current environmental parameters, and user emotional state. The quantum Fourier transform (QFT) is used to accelerate feature extraction and reduce algorithmic complexity. To ensure ledger integrity, a quantum zero-knowledge proof mechanism is introduced, allowing verifiers to confirm the authenticity of ledger records without revealing specific data. The quantum trust ledger utilizes quantum error-correcting codes (such as surface codes) to mitigate quantum decoherence and ensure long-term data reliability.

[0059] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A remote attestation method for a trusted data space, characterized in that: The following steps are involved: Initialization steps for a trusted environment for heterogeneous terminals: Build a dynamic isolation domain based on a hardware root of trust. This achieves full-link trustworthiness for computing resources through CPU microcode-level verification combined with GPU rasterization encryption and FPGA bitstream dynamic watermarking. Device identity keys are generated using a combination of physically unclonable functions and chaotic circuits. Remote attestation request generation and transmission steps: Design a quantum-inspired random challenge generation algorithm, use a quantum random number generator to generate a true random number sequence, and construct a time-space bound challenge value through quantum entanglement effects. Use a neuromorphic photonic network to realize the transmission of attestation requests; Lightweight zero-knowledge proof construction steps: Propose a tensor decomposition zero-knowledge proof protocol, through the formula Achieve privacy protection for high-dimensional data, where It is the abbreviation of zero-knowledge proof of tensor decomposition. is the original high-dimensional tensor data, F is the quantum Fourier transform function, Mapping to low-dimensional space reduces the computational complexity from traditional down to , S is the data sensitivity index, R is the computing resource parameter, and the function G dynamically adjusts the proof granularity according to S and R. Ordinary data uses coarse-grained proof, and finally generates a lightweight zero-knowledge proof P; The multi-level aggregation verification mechanism involves designing a spatiotemporal causal graph verification framework, modeling device state changes as a directed acyclic graph, and verifying the consistency of system behavior by examining the causal relationships between nodes in the graph. Furthermore, quantum signature aggregation technology is introduced to compress the signatures of the proving nodes into a single verification point. Dynamic trust assessment and feedback steps: Construct a quantum Bayesian trust inference model to achieve long-distance correlation of trust propagation through quantum entanglement effects; design an adaptive trust decay surface to generate a trust decay curve based on multi-dimensional parameters; develop a biofeedback trust regulation mechanism to dynamically adjust the trust threshold by analyzing the biometric characteristics of the device operator; The lightweight zero-knowledge proof construction step also includes: developing a proof generation algorithm based on swarm intelligence, simulating ant colony optimization and bee colony decision-making mechanism, and assuming that the proof task set is , decompose it into multiple subtasks for parallel processing, introduce the group collaboration factor C, and use the formula Generate the optimal proof path P, where is a set of proof paths, It is a subtask The weight of Is the path p processing subtask At the same time, it realizes the visualization of stream of consciousness proof, transforms the zero-knowledge proof process into a perceptible visual pattern, and enables the verifier to intuitively understand the proof logic.

2. A remote attestation method for a trusted data space according to claim 1, characterized in that: Also includes: In the initialization step of the heterogeneous terminal trusted environment, atomic transaction verification is implemented across heterogeneous architectures, and the atomicity of data transmission and computing operations between the CPU, GPU, and FPGA is ensured through transactional memory technology. A blockchain-based hardware supply chain traceability mechanism is adopted to generate tamper-proof trusted logs for the entire process from chip manufacturing to device assembly, and material genomics technology is combined to verify the consistency of the physical properties of hardware components with design specifications.

3. A remote attestation method for a trusted data space according to claim 1, characterized in that: Also includes: In the lightweight zero-knowledge proof construction step, a holographic zero-knowledge proof protocol is proposed, which converts physical phenomena in three-dimensional space into mathematical proofs and uses the determinism of physical laws to verify the correctness of the calculation process; a proof archiving system based on DNA storage is developed, which encodes zero-knowledge proof information into DNA sequences and uses the storage characteristics of DNA to achieve long-term and secure storage of proofs.

4. A remote attestation method for a trusted data space according to claim 1, characterized in that: Also includes: In the multi-level aggregation verification mechanism step, a quantum entanglement verification network is implemented, and distributed verification nodes are constructed using the characteristics of quantum entanglement. When a node detects an anomaly, other entangled nodes perceive it and synchronously adjust the verification strategy; a cognitive verification agent is introduced, which learns normal system behavior patterns based on a neural network model, identifies unknown types of attack patterns, and generates adaptive verification rules.

5. A remote attestation method for a trusted data space according to claim 1, characterized in that: Also includes: In the dynamic trust evaluation and feedback step, a social physical trust network is constructed, and the formula Fusion calculates the trust value, where Represents the trust value, Represents the social behavior characteristics of people. Represents the physical characteristics of the device, function It represents the rules or methods of integrated computing. At the same time, it develops a trust contagion control mechanism. When anomalies occur in the local trust domain, the optimal isolation strategy is calculated through the game theory model to prevent the spread of the trust crisis.

6. A remote attestation method for a trusted data space according to claim 1, characterized in that: The steps of initializing the trusted environment for heterogeneous terminals also include: implementing nanoscale hardware watermarking technology to form nanoscale physical features through ion implantation during the chip manufacturing process. These features can serve as indelible hardware identity identifiers; building a living hardware verification system to verify whether the hardware is in normal working condition by monitoring the quantum tunneling effect and thermal noise fluctuations inside the chip, thereby preventing hardware cloning and side-channel attacks.

7. A remote attestation method for a trusted data space according to claim 1, characterized in that: The remote proof request generation and transmission step also includes: designing a neuromorphic proof carrier, encoding the proof information into a neural pulse sequence, and utilizing the low power consumption and high fault tolerance characteristics of the pulse neural network to achieve transmission; using the principle of quantum teleportation to construct a long-distance transmission channel for the proof information, thereby achieving secure transmission of the proof information without actually transmitting the physical carrier.

8. A remote attestation method for a trusted data space according to claim 1, characterized in that: The multi-level aggregation verification mechanism also includes: building a space-time curvature verification model, mapping device state changes into a four-dimensional space-time coordinate system, and identifying system behavior deviations by detecting space-time curvature anomalies; developing a virtual verification environment based on the metaverse, mapping physical devices into virtual space for stress testing and attack simulation, and verifying the credibility of the device under extreme conditions.

9. A remote attestation method for a trusted data space according to claim 1, characterized in that: The dynamic trust evaluation and feedback steps also include: implementing emotional computing trust regulation, dynamically adjusting the trust threshold by analyzing the emotional state of the user during the interaction with the system; constructing a quantum trust ledger, using the quantum no-cloning theorem to ensure the security of trust records, and achieving superlinear acceleration of trust evaluation through quantum parallel computing.

Images