Data access control method and system based on zero-knowledge proof

Abstract

The application provides a data access control method and system based on zero-knowledge proof, and relates to the technical field of data access control. The root key of the unique hardware identity is generated through the physically unclonable function of the data request device chip, and the real-time running state data of the device is collected at the same time. The two are bound by cryptography to form a device trusted state certificate. Secondly, the capacitive proximity sensing array detects the capacitive field disturbance when the data holding device is close, and generates an interaction response mode. Then, the device trusted state certificate and the interaction response mode are used as private inputs to generate a composite zero-knowledge proof, which proves the identity authenticity and physical proximity to the data holding device without revealing sensitive information. After the data holding device is verified, the data access permission is authorized immediately, a temporary encrypted communication channel is established, and the reliability and immediacy of the security authentication between IoT devices can be improved.

Description

Technical Field

[0001] This application relates to the field of data access control technology, and in particular to a data access control method and system based on zero-knowledge proof. Background Technology

[0002] In an IoT device self-organizing network environment, devices need to establish secure and reliable temporary communication connections, while ensuring that the authentication process can resist man-in-the-middle attacks and protect sensitive device information from being leaked. This scenario requires the authentication scheme to take into account both physical proximity verification and identity authenticity proof, and it cannot rely on centralized authentication authorities or preset key distribution mechanisms, in order to adapt to the needs of dynamic device networking and temporary collaboration.

[0003] The mainstream solution for this requirement is currently Bluetooth signal strength-based proximity authentication. This method determines the physical distance between devices by measuring signal attenuation and completes two-way authentication in conjunction with a preset key exchange protocol. The system generates a temporary session key for each pair of communicating devices. When the signal strength reaches a preset threshold, the authentication process is automatically triggered, and an encrypted communication channel is established after successful authentication.

[0004] However, this scheme has significant flaws. Signal strength is susceptible to environmental interference, leading to misjudgments, and it cannot distinguish between genuine nearby devices and signal relays spoofed by attackers. Furthermore, the pre-defined key exchange mechanism is ineffective against man-in-the-middle attacks, and the authentication process exposes device identity information. More importantly, this method lacks a hardware-level root of trust, failing to prove the inseparable relationship between device identity and physical proximity, allowing attackers to potentially breach security defenses through replay attacks and other methods. Summary of the Invention

[0005] The purpose of this application is to provide a data access control method and system based on zero-knowledge proofs to solve the problems of insufficient reliability and immediacy of inter-device security authentication in the prior art.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a data access control method based on zero-knowledge proof, comprising:

[0007] In scenarios where a data requesting device needs to interact with a data holding device in a trusted manner, a root key representing a unique hardware identity is generated using a physically unclonable function formed during the chip manufacturing process of the data requesting device. Simultaneously, real-time operating status data of the data requesting device is collected, and the root key is cryptographically bound to the real-time operating status data to form a trusted status credential for the device.

[0008] The capacitive proximity sensor array embedded in the housing of the data requesting device is driven to actively detect the coupled capacitive field disturbance caused by the data holding device approaching, which can reflect a specific physical profile and dielectric constant, and process the coupled capacitive field disturbance into an interactive response mode describing the physical interaction process.

[0009] The trusted state credential of the device and the interaction response mode are used as private inputs to generate a composite zero-knowledge proof. Through the composite zero-knowledge proof, without disclosing the root key and the real-time running status data, the authenticity of the identity of the data requesting device and the immediacy of its physical proximity are asserted to the data holding device.

[0010] The composite zero-knowledge proof is sent to the data holding device via a wireless channel on the data requesting device. After receiving it, the data holding device independently verifies the validity of the composite zero-knowledge proof locally to confirm the authenticity and reliability of the other party's identity and physical co-existence state.

[0011] After the composite zero-knowledge proof is verified, the data holding device immediately authorizes the data requesting device to access the data, thereby establishing a temporary, end-to-end encrypted data transmission channel between the data holding device and the data requesting device, which is limited to successful verification interactions.

[0012] Optionally, the step of using the device trusted state credential and the interaction response mode as private inputs to generate a composite zero-knowledge proof, and using the composite zero-knowledge proof to simultaneously assert the identity authenticity and physical proximity of the data requesting device to the data holding device without disclosing the root key and the real-time operating status data, includes:

[0013] The trusted state credentials of the device and the interaction response pattern are subjected to independent arithmetic processing to convert each of the trusted state credentials of the device and the interaction response pattern into a constraint polynomial with a unique correspondence.

[0014] Establish a public constraint set for coupling the constraint polynomial. The public constraint set contains a set of preset algebraic equations. The algebraic equations describe the determination conditions under which the identity authenticity and physical proximity of the data requesting device must be met simultaneously without disclosing the root key and the real-time running status data.

[0015] The data requesting device invokes the public constraint set and uses the constraint polynomial as a private witness to perform localized proof generation calculations based on the decision conditions, producing a publicly verifiable composite zero-knowledge proof.

[0016] Optionally, the capacitive proximity sensor array embedded in the housing of the data requesting device actively detects the coupled capacitive field disturbance caused by the proximity of the data holding device, which reflects a specific physical profile and dielectric constant, and processes the coupled capacitive field disturbance into an interactive response mode describing the physical interaction process, including:

[0017] Drive each sensing unit in the capacitive proximity sensing array to continuously capture the original capacitive signal stream generated by the physical proximity of the data holding device within a preset detection period;

[0018] A time-frequency domain transformation is performed on the original capacitive signal stream to extract amplitude fluctuation information that characterizes the physical contour of the data holding device, and phase drift information that characterizes the dielectric properties of the data holding device is extracted simultaneously, forming amplitude feature set and phase feature set respectively;

[0019] The amplitude feature set and the phase feature set are projected together into a preset high-dimensional feature space. By performing nonlinear fitting on the distribution relationship of the amplitude feature set and the phase feature set in the high-dimensional feature space, a vectorized interactive response pattern that can uniquely identify physical contact events is generated.

[0020] Optionally, after the composite zero-knowledge proof passes verification, the step of immediately authorizing the data requesting device to access the data through the data holding device, thereby establishing a temporary, end-to-end encrypted data transmission channel between the data holding device and the data requesting device, limited to successful verification interactions, includes:

[0021] Once the composite zero-knowledge proof is confirmed as valid by the data holding device, a temporary session credential with high randomness is immediately generated locally by the data holding device.

[0022] The data holding device uses the public key broadcast by the data requesting device at the initial stage of the interaction to encrypt the temporary session credential, and then sends the encrypted temporary session credential back to the data requesting device.

[0023] The data requesting device and the data holding device use a shared temporary session credential and a preset key negotiation protocol to independently calculate the same session key and use the session key to establish an end-to-end encrypted data transmission channel.

[0024] Optionally, the step of sending the composite zero-knowledge proof to the data holding device via a wireless channel on the data requesting device, and the data holding device independently verifying the validity of the composite zero-knowledge proof locally after receiving it, to confirm the authenticity and reliability of the other party's identity and physical co-existence state, includes:

[0025] The data request device encapsulates the composite zero-knowledge proof with a set of public verification keys for verifying the composite zero-knowledge proof to form a data packet to be verified, and transmits the data packet to be verified wirelessly to the data holding device.

[0026] After the data holding device receives the data packet to be verified, it separates the composite zero-knowledge proof and the public verification key from the data packet to be verified.

[0027] The data holding device uses the public verification key to perform deterministic verification operations on the composite zero-knowledge proof. By checking whether the output of the verification operation satisfies a preset algebraic identity, the authenticity of the composite zero-knowledge proof is determined, thereby confirming the authenticity and reliability of the other party's identity and physical co-existence state.

[0028] Optionally, a public constraint set is established for coupling the constraint polynomial. This public constraint set contains a set of preset algebraic equations. These equations describe the conditions under which the authenticity of the data requesting device's identity and its physical proximity must simultaneously be met without disclosing the root key and the real-time operating status data. These conditions include:

[0029] Independent decision conditions are set for each of the constraint polynomials. The decision conditions require that when the input is valid, the result of the constraint polynomial after a specific operation is always equal to a preset zero value.

[0030] Construct a coupling equation, through which randomized variables are introduced to linearly combine the decision conditions into a single algebraic expression, the overall calculation result of which is equal to the zero value only when two independent decision conditions are satisfied simultaneously;

[0031] The coupled equations and all auxiliary constraints required to satisfy them are compiled into a structured set of public constraints, which serves as the unified computational framework for generating the composite zero-knowledge proof.

[0032] Optionally, the step of projecting the amplitude feature set and the phase feature set together into a preset high-dimensional feature space, and generating a vectorized interactive response pattern that can uniquely identify a physical contact event by performing nonlinear fitting on the distribution relationship of the amplitude feature set and the phase feature set in the high-dimensional feature space, includes:

[0033] A set of preset nonlinear basis functions are used to perform a dimension-up transformation on each data point in the amplitude feature set and the phase feature set, mapping them into a point cloud distributed in the high-dimensional feature space;

[0034] In the high-dimensional feature space, the geometric parameters of a hypersurface are iteratively adjusted so that the hypersurface can maximize the separation of the point cloud, thereby capturing the intrinsic correlation structure between the amplitude feature set and the phase feature set.

[0035] The finalized geometric parameters are arranged in a predetermined order and solidified into a numerical vector, and a vectorized interactive response mode is formed based on the numerical vector.

[0036] Secondly, this application provides a data access control system based on zero-knowledge proof, comprising:

[0037] The binding module is used to generate a root key representing a unique hardware identity by using a physically unclonable function formed during the chip manufacturing process of the data requesting device in scenarios where the data requesting device needs to perform trusted interaction with the data holding device. It also synchronously collects the real-time operating status data of the data requesting device and cryptographically binds the root key with the real-time operating status data to form a trusted status credential of the device.

[0038] The processing module is used to drive the capacitive proximity sensor array embedded in the housing of the data requesting device to actively detect the coupled capacitive field disturbance caused by the data holding device approaching, which can reflect a specific physical profile and dielectric constant, and process the coupled capacitive field disturbance into an interactive response mode describing the physical interaction process.

[0039] The generation module is used to take the trusted state credential of the device and the interaction response mode as private inputs to generate a composite zero-knowledge proof. Through the composite zero-knowledge proof, without disclosing the root key and the real-time running status data, it asserts to the data holding device the authenticity of the data requesting device's identity and the immediacy of its physical proximity.

[0040] The verification module is used to send the composite zero-knowledge proof to the data holding device via a wireless channel on the data request device. After receiving the proof, the data holding device independently verifies the validity of the composite zero-knowledge proof locally to confirm the authenticity and reliability of the other party's identity and physical co-existence state.

[0041] An establishment module is used to immediately authorize the data requesting device to access data after the composite zero-knowledge proof has been verified, thereby establishing a temporary, end-to-end encrypted data transmission channel between the data holding device and the data requesting device, which is limited to successful verification interactions.

[0042] Thirdly, this application provides an electronic device, comprising:

[0043] Memory, used to store computer programs;

[0044] A processor, configured to implement the steps of the zero-knowledge proof-based data access control method as described in the first aspect above when executing the computer program.

[0045] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the zero-knowledge proof-based data access control method described in the first aspect above.

[0046] The zero-knowledge proof-based data access control method provided in this application, in scenarios where a data requesting device needs to conduct trusted interaction with a data holding device, utilizes the physically unclonable function formed during the chip manufacturing process of the data requesting device to generate a root key representing a unique hardware identity. Simultaneously, it collects real-time operational status data of the data requesting device and cryptographically binds the root key to the real-time operational status data to constitute a trusted device status credential. By combining the inherent physically unclonable characteristics of the device chip with real-time operational status data and cryptographically binding them, it effectively solves the problems of easily forged device identities and opaque status information in traditional identity authentication, paving the way for subsequent trusted... The interaction lays the foundation for hardware-level security, ensuring the uniqueness of device identity and the real-time reliability of its status. By driving the capacitive proximity sensor array embedded in the casing of the data requesting device, it actively detects the coupling capacitive field disturbance caused by the proximity of the data holding device, which reflects a specific physical profile and dielectric constant. This coupled capacitive field disturbance is then processed into an interactive response mode describing the physical interaction process. This innovative approach utilizes the capacitive proximity sensor array to detect and process the unique coupling capacitive field disturbance generated when devices are physically close, generating an interactive response mode that accurately reflects the physical profile and dielectric properties of the other device. This overcomes the limitations of traditional signal strength-based proximity verification, which is susceptible to environmental interference and cannot distinguish between genuine and fake devices. Overcoming the limitations of physical contact and signal forgery, this method provides a high-precision, difficult-to-forge proof of physical proximity. By using the device's trusted state credentials and the interaction response pattern as private inputs to generate a composite zero-knowledge proof, this proof, without revealing the root key or the real-time operating status data, simultaneously asserts to the data-holding device the authenticity of the data-requesting device's identity and the immediacy of its physical proximity. This allows for the generation of a composite zero-knowledge proof using the device's trusted state credentials and interaction response pattern as private inputs, achieving the simultaneous proof of device authenticity and the immediacy of physical proximity to the verifier without disclosing any sensitive identity information or physical proximity details. This capability greatly enhances the privacy and security of the authentication process, effectively mitigating the risk of information leakage. By transmitting the composite zero-knowledge proof via a wireless channel from the data requesting device to the data holding device, and upon receipt by the data holding device, the validity of the composite zero-knowledge proof is independently verified locally to confirm the authenticity and reliability of the other party's identity and physical co-existence state. The ability to efficiently transmit the composite zero-knowledge proof via a wireless channel and perform independent local verification at the receiving device ensures the decentralization and efficiency of the authentication process, avoids dependence on third-party certification authorities, guarantees the objectivity and impartiality of the verification results, and improves the flexibility and response speed of self-organizing network authentication.After the composite zero-knowledge proof is verified, the data holding device immediately authorizes the data requesting device to access the data. This establishes a temporary, end-to-end encrypted data transmission channel between the data holding device and the data requesting device, limited to successful verification interactions. This ensures the confidentiality and integrity of data transmission by immediately authorizing data access and establishing a temporary, end-to-end encrypted data transmission channel after the composite zero-knowledge proof verification is successful. It achieves on-demand access control based on trusted authentication, effectively preventing unauthorized data theft and tampering, and guaranteeing the security of communication between devices.

[0047] Furthermore, the device's trusted state credentials and interaction response patterns are arithmetically transformed into constraint polynomials suitable for cryptographic operations. Subsequently, a public constraint set containing predefined algebraic equations is constructed. These equations precisely describe the conditions that must be simultaneously satisfied for the data requesting device to verify its identity and physical proximity. Finally, the data requesting device uses this public constraint set, with the constraint polynomials as private witnesses, to perform localized proof generation computation, thereby producing a compact and publicly verifiable composite zero-knowledge proof. This core process, by uniformly encoding multi-source heterogeneous trust credentials into a mathematical structure and utilizing the underlying mechanism of zero-knowledge proofs, achieves a joint assertion of device identity and physical proximity without exposing any original sensitive data. This not only significantly improves the privacy protection level of the authentication process but also ensures the unforgeability and verifiability of the authentication results, providing key technical support for building a highly secure and privacy-friendly self-organizing network authentication system. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 A flowchart illustrating a data access control method based on zero-knowledge proof provided in an embodiment of this application;

[0050] Figure 2 A schematic diagram illustrating a specific implementation of a data access control method based on zero-knowledge proof provided in this application embodiment;

[0051] Figure 3 A schematic diagram illustrating a specific implementation of a data access control method based on zero-knowledge proof provided in this application embodiment;

[0052] Figure 4This is a schematic diagram of a data access control system based on zero-knowledge proof, provided as an embodiment of this application. Detailed Implementation

[0053] In an ad-hoc network environment for IoT devices, establishing secure and reliable temporary communication connections between devices faces numerous challenges. Existing proximity authentication technologies based on Bluetooth signal strength, while capable of preliminary physical distance assessment and key exchange, suffer from significant drawbacks: the signal is susceptible to environmental interference leading to misjudgments, and it cannot effectively distinguish between genuine nearby devices and signal relays forged by attackers; the pre-defined key exchange mechanism is vulnerable to man-in-the-middle attacks, and the authentication process may leak sensitive device identity information; a deeper problem lies in the lack of a hardware-level root of trust, which fails to prove the inseparable relationship between device identity and physical proximity, making it susceptible to threats such as replay attacks, severely restricting the reliability and robustness of secure communication between devices in an ad-hoc network environment.

[0054] To address the shortcomings of existing technologies, this application proposes a data access control method based on zero-knowledge proofs. Its core lies in the collaborative application of device status data, capacitive proximity sensor arrays, and physically unclonable functionality. This scheme generates a unique root of trust for each device through PUF and constructs a trusted credential by combining it with real-time device status data. Simultaneously, it utilizes a capacitive proximity sensor array to accurately perceive physical proximity interaction patterns. These serve as private inputs, generating a composite proof through a zero-knowledge proof mechanism, verifying both the authenticity of the device's identity and its physical proximity without disclosing any sensitive information. This scheme fundamentally solves the problems of signal susceptibility to interference, insufficient defense against man-in-the-middle attacks, identity information leakage, and the lack of hardware-level roots of trust in traditional methods. It significantly improves the security, reliability, and anti-attack capabilities of IoT device self-organizing network authentication, providing strong protection for data access control in dynamic and temporary collaborative scenarios.

[0055] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0056] The core of this application is to provide a data access control method based on zero-knowledge proof, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0057] S101. In a scenario where the data requesting device needs to interact with the data holding device in a trusted manner, a root key representing a unique hardware identity is generated by using the physically unclonable function formed during the chip manufacturing process of the data requesting device, and the real-time operating status data of the data requesting device is collected synchronously. The root key is cryptographically bound to the real-time operating status data to form a trusted status certificate of the device.

[0058] In the above scheme, the physically unclonable function utilizes physical differences in the chip manufacturing process to generate a unique digital fingerprint for each device, ensuring its identity cannot be copied. The root key is an initial encryption key generated based on the physically unclonable function, representing the uniqueness of the device's hardware. Real-time operating status data includes information such as the device's current battery level, temperature, and network connection, reflecting the device's actual operating environment. Cryptographic binding tightly links the data through encryption algorithms, ensuring integrity. The device's trusted status credential is a digital proof formed by cryptographic processing, combining the device's unique hardware identity and current operating status data, reliably demonstrating the validity of the device's identity and status.

[0059] In this embodiment, firstly, when secure interaction is required between the devices, the system utilizes the Physically Unclonable Function (PUF) characteristic formed during the device chip manufacturing process. The PUF is a unique physical fingerprint of the chip. By stimulating the PUF and measuring its response, unique binary data is extracted to generate a root key representing the unique hardware identity of the device. For example, a challenge signal is input to the PUF, and the PUF generates a response based on its physical characteristics. After digitization and error correction, the root key is obtained.

[0060] Secondly, while generating the root key, the system simultaneously collects real-time operating status data of the device. This data includes the device's battery level, CPU usage, network connection type, etc., reflecting the device's current real-time operating environment and status. Finally, the root key is cryptographically bound to the real-time operating status data to form a trusted device status credential. This is typically achieved through a hash function or encryption algorithm. Using the root key and real-time operating status data as input, a unique digest value is calculated using a secure hash algorithm, or the status data is encrypted and signed using the root key. Any tampering with the root key or status data will result in verification failure, ensuring the inseparability of the device's identity and current state. For example, concatenating the root key and real-time operating status data and calculating the hash value yields the trusted device status credential, proving the device's identity and current state.

[0061] S102. Drive the capacitive proximity sensor array embedded in the housing of the data request device to actively detect the coupled capacitive field disturbance caused by the data holding device approaching, which can reflect a specific physical profile and dielectric constant, and process the coupled capacitive field disturbance into an interactive response mode describing the physical interaction process.

[0062] Optionally, such as Figure 2 As shown, step S102 may specifically include the following steps:

[0063] S1021. Drive each sensing unit in the capacitive proximity sensing array to continuously capture the original capacitive signal stream generated by the physical proximity of the data holding device within a preset detection period.

[0064] S1022. Perform time-frequency domain transformation on the original capacitor signal stream to extract amplitude fluctuation information that characterizes the physical contour of the data holding device, and simultaneously extract phase drift information that characterizes the dielectric properties of the data holding device, thereby forming an amplitude feature set and a phase feature set respectively.

[0065] S1023. Project the amplitude feature set and the phase feature set together into a preset high-dimensional feature space. By performing nonlinear fitting on the distribution relationship of the amplitude feature set and the phase feature set in the high-dimensional feature space, a vectorized interactive response pattern that can uniquely identify physical contact events is generated.

[0066] Specifically, step S1023 may include the following process: using a set of preset nonlinear basis functions, performing a dimension-up transformation on each data point in the amplitude feature set and the phase feature set, mapping it to a point cloud distributed in the high-dimensional feature space; in the high-dimensional feature space, iteratively adjusting the geometric parameters of a hypersurface so that the hypersurface can maximize the separation of the point cloud, thereby capturing the intrinsic correlation structure between the amplitude feature set and the phase feature set; arranging the finally determined geometric parameters in a predetermined order and solidifying them into a numerical vector, and constructing a vectorized interactive response mode based on the numerical vector.

[0067] In the above scheme, the capacitive proximity sensing array is a system composed of multiple capacitive sensors, capable of detecting changes in the electric field caused by nearby objects. Coupled capacitive field perturbation refers to the influence of an object's physical shape and material properties on the electric field when it approaches the capacitive sensor. The original capacitive signal stream is the capacitance value data that changes over time, continuously acquired by the capacitive sensors. Time-frequency domain transformation is a mathematical method that converts a signal from the time domain to the frequency domain, used to analyze the frequency components of the signal. Amplitude fluctuation information reflects changes in signal strength and is related to the physical contours of the object. Phase drift information reflects the offset of the signal waveform's starting point and is related to the dielectric properties of the object. The amplitude feature set and phase feature set are the extracted amplitude and phase information sets, respectively. The high-dimensional feature space is an abstract mathematical space used to represent complex data points, where each dimension represents a feature. The vectorized interactive response pattern represents the characteristics of the physical interaction process as a multi-dimensional digital vector, facilitating computer processing and recognition.

[0068] In this embodiment, firstly, through step S1021, each sensing unit within the capacitive proximity sensing array is activated and begins operating according to a preset detection cycle, continuously emitting a weak electric field into the surrounding space and receiving reflected signals, thereby continuously measuring the capacitance value of its surrounding environment. As the data holding device gradually approaches, its physical presence and material properties alter the electric field distribution around the sensing array, causing the sensing units to capture a series of time-varying raw capacitance signals. The minute fluctuations in these signals accurately reflect the dynamic process of the approaching object. For example, in a smart bracelet, when a user's finger approaches in a specific posture, the array continuously records minute changes in capacitance values ​​at extremely high frequencies, forming a continuous, timestamped sequence of capacitance signals. This sequence contains complete dynamic information about the finger's approach, contact, and departure.

[0069] Subsequently, in step S1022, a refined time-frequency domain transformation is performed on these continuously captured raw capacitance signal streams. This process aims to decompose the raw, time-varying capacitance signal into energy distribution and phase information at different frequencies and time points. Specifically, by applying signal processing techniques such as short-time Fourier transform or wavelet transform, the raw signal is transformed from the time domain to the frequency domain, thereby extracting two key feature sets: first, amplitude fluctuation information, which reflects the energy intensity changes of the signal at different frequencies. These changes are closely related to the physical contours of the nearby object (e.g., its size, shape, surface area), forming an amplitude feature set; second, phase drift information, which reflects the shift of the signal waveform's starting point at different frequencies. These shifts are closely related to the dielectric properties of the nearby object (e.g., the conductivity and insulation of its material), forming a phase feature set. For example, performing a short-time Fourier transform on the acquired capacitance signal yields a series of spectrograms. From these spectrograms, energy peaks within a specific frequency range and their delayed phase relative to the reference signal can be extracted. These values ​​constitute the amplitude feature set and the phase feature set.

[0070] Finally, in step S1023, the previously obtained amplitude feature set and phase feature set are projected together into a pre-defined high-dimensional feature space. In this high-dimensional space, each physical interaction event, regardless of its complexity, is represented as a unique, multi-dimensional digital point. By performing nonlinear fitting on the distribution of these data points formed by the amplitude and phase feature sets in the high-dimensional space, such as using machine learning algorithms like support vector machines, neural networks, or Gaussian mixture models, an optimal mathematical model can be found to describe and distinguish different physical interaction patterns. This model is ultimately solidified into a vectorized interaction response pattern. This multi-dimensional vector can uniquely and accurately identify a specific physical contact event, thus transforming the complex physical interaction process into a computable and identifiable digital representation. For example, by using the key values ​​in the amplitude and phase feature sets as coordinates in the high-dimensional space to form a data point cloud, a neural network model can be trained to identify the point cloud distribution corresponding to different interaction patterns. The output layer of the neural network can then generate a vector representing the vectorized representation of the interaction pattern.

[0071] In practical applications, scenarios involving temporary trusted networking and data exchange between IoT devices, such as a smart access control system / data requesting device needing to prove its legitimacy to a smart key / data holding device without the need for a central server to issue certificates, are common. When a smart key approaches the smart access control system, the capacitive proximity sensor array embedded in the system's casing actively detects the capacitive field disturbances caused by the key's approach and processes these disturbances into interactive response patterns describing the physical interaction process. First, through step S1021, the capacitive sensing unit within the access control system continuously captures the raw capacitive signal stream generated when the smart key approaches at an extremely high sampling rate. These signal streams contain the capacitance value changes throughout the entire dynamic process of the key's approach to the access control surface. Second, through step S1022, the access control system performs time-frequency domain transformation on these raw capacitive signal streams, for example, using continuous wavelet transform, to extract amplitude fluctuation information reflecting the key's physical contours, such as the shape and size of the key teeth, and phase drift information reflecting the dielectric properties of the key material, such as the metal or plastic parts, forming amplitude feature sets and phase feature sets, respectively. For example, by analyzing the energy distribution and phase spectrum of wavelet coefficients, the energy response and time delay of the key at different frequencies can be quantified. Finally, in step S1023, these amplitude feature sets and phase feature sets are jointly projected into a pre-defined high-dimensional feature space, and a nonlinear fitting algorithm, such as a radial basis function kernel support vector machine, is used to model the distribution relationship of these features in the high-dimensional space, generating a vectorized interaction response pattern. This pattern can uniquely identify the physical interaction event of "a specific smart key touching the access control in a specific way". For example, if the interaction patterns of a legitimate key form a specific cluster in the feature space, then when a new interaction pattern is detected, its distance from known legitimate patterns can be calculated to determine whether it is a legitimate interaction. This vectorized interaction response pattern can then be used as one of the private inputs for zero-knowledge proofs to prove to the access control system that there is a real physical proximity interaction between the key and the access control without revealing the specific physical characteristics of the key, thereby establishing a secure connection.

[0072] The overall scheme in step S102 described above, by actively driving a capacitive proximity sensor array and performing refined time-frequency domain analysis and high-dimensional feature space mapping on the captured coupled capacitive field disturbances, can accurately capture and quantify the physical interaction characteristics between devices. This method overcomes the limitations of traditional signal strength-based judgments, effectively avoids misjudgments caused by environmental interference and signal spoofing, and significantly improves the accuracy and reliability of physical proximity verification. By transforming physical interactions into computable vectorized patterns, it provides a solid foundation for subsequent encryption authentication and data exchange, enhancing the self-organizing security capabilities of IoT devices in decentralized trust environments.

[0073] S103. The trusted state credential of the device and the interaction response mode are used as private inputs to generate a composite zero-knowledge proof. The composite zero-knowledge proof asserts the authenticity of the identity of the data requesting device and the immediacy of its physical proximity to the data holding device without disclosing the root key and the real-time running status data.

[0074] Optionally, such as Figure 3 As shown, step S103 may specifically include the following steps:

[0075] S1031. Perform independent arithmetic processing on the device trusted status certificate and the interaction response mode to convert the device trusted status certificate and the interaction response mode into a constraint polynomial with a unique correspondence.

[0076] S1032. Establish a public constraint set for coupling the constraint polynomial. The public constraint set contains a set of preset algebraic equations. The algebraic equations describe the determination conditions under which the identity authenticity and physical proximity of the data requesting device must be met simultaneously without disclosing the root key and the real-time running status data.

[0077] Specifically, step S1032 may include the following processes: setting independent decision conditions for each constraint polynomial, wherein the decision conditions require that, when the input is valid, the result of the constraint polynomial after a specific operation is always equal to a preset zero value; constructing a coupling equation, and through the introduction of randomized variables, linearly combining the decision conditions into a single algebraic expression, wherein the overall calculation result of the algebraic expression is equal to the zero value only when two independent decision conditions are satisfied simultaneously; compiling the coupling equation and all auxiliary constraint relationships required to satisfy the coupling equation into a structured public constraint set, and using the public constraint set as a unified computational framework for subsequently generating the composite zero-knowledge proof.

[0078] S1033. The data request device calls the public constraint set and uses the constraint polynomial as a private witness to perform localized proof generation calculation based on the judgment conditions, producing a publicly verifiable composite zero-knowledge proof.

[0079] In the above scheme, the device trusted state credential is an encrypted representation of the device's identity and real-time operating status, ensuring its authenticity and integrity. The interaction response pattern is a unique signal characteristic generated by the device in a specific physical interaction, used to verify physical proximity. Composite zero-knowledge proof is an encryption technique that allows one party to prove the truth of a statement to another without revealing private information. The constraint polynomial converts the device trusted state credential and interaction response pattern into mathematical form for zero-knowledge proof computation. The public constraint set is a predefined set of algebraic equations defining the conditions under which both identity authenticity and physical proximity are simultaneously satisfied, serving as a public reference for the proof process. The private witness is secret information possessed by the prover, used to construct the zero-knowledge proof.

[0080] In this embodiment, firstly, through step S1031, the device trusted state credential and the interaction response pattern are independently processed arithmetically. This process transforms these unstructured or semi-structured data and behavioral patterns into a mathematical form that the zero-knowledge proof system can understand and process, namely, a constraint polynomial with a unique correspondence. Specifically, for the device trusted state credential, it may contain the device's unique identifier, configuration information of its internal security modules, and a cryptographic digest of the current operating state. This information is mapped to a specific polynomial through a predefined encoding function, such as based on a hash function or cryptographic commitment scheme. The coefficients or roots of this polynomial uniquely represent the original credential information. For example, if the credential is a binary string, it can be interpreted as a large integer, and then a polynomial can be constructed such that the integer is the result of evaluating the polynomial at a specific point. Similarly, for the interaction response pattern, such as a sequence of capacitance signals continuously acquired from a sensor array, the amplitude, phase, frequency, and other features of these signals are extracted and transformed into another polynomial according to specific rules, such as the feature vectors after Fourier transform or wavelet analysis. This polynomial can capture the unique "fingerprints" of physical interactions, such as the rhythm of tapping or the gesture of swiping. This arithmetic processing is the foundation of zero-knowledge proofs. It abstracts complex information from the real world into mathematical objects, enabling subsequent proofs and verifications to be performed efficiently in the realm of pure mathematics, while ensuring the integrity and privacy of the information.

[0081] Subsequently, through step S1032, the system establishes a public constraint set for coupling these constraint polynomials. This set is not a simple list of data, but a carefully designed set of algebraic equations that collectively define the conditions that the prover must satisfy. At the heart of these equations are the criteria that describe the simultaneous fulfillment of the identity authenticity and physical proximity of the data requesting device without revealing the device's root key and real-time operational status data. For example, one equation might require that the evaluation result of the polynomial representing the device's trusted state credentials at a specific public point must match a pre-published, encrypted device identity information. Another equation might require that the evaluation result of the polynomial representing the interaction response pattern at another public point must conform to the publicly disclosed characteristics of the expected physical interaction pattern. These equations are logically connected to ensure that the entire proof is considered valid only when both conditions are met. This public constraint set is a "rulebook" known to all participants, providing a common reference framework for the generation and verification of zero-knowledge proofs, ensuring the transparency and fairness of the verification process, while strictly limiting the disclosure of any sensitive information.

[0082] Finally, through step S1033, the data requesting device invokes this public constraint set and uses its own generated constraint polynomial as a private witness to perform localized proof generation computation. This process is the core operation of zero-knowledge proof. The device utilizes its private, unpublished credentials and the polynomial corresponding to the interaction mode, combined with the algebraic equations defined in the public constraint set, to construct a proof using complex cryptographic algorithms, such as polynomial commitments, homomorphic encryption, or specific zero-knowledge proof protocols, such as those based on Groth16 or Plonk. During the computation, the device performs a series of mathematical operations, such as polynomial evaluation, polynomial multiplication, and addition, to prove that its private inputs satisfy all equations in the public constraint set without revealing these private inputs themselves. Ultimately, this computation produces a compact and publicly verifiable composite zero-knowledge proof. This proof is a short, encrypted credential that any verifier possessing the public constraint set can quickly and efficiently verify its validity, thereby confirming that the data requesting device possesses both identity authenticity and physical proximity without accessing any sensitive raw data.

[0083] In practical applications, imagine a smart home environment where a smart lock requires verification of a family member's smart bracelet to unlock automatically. First, when a family member wearing the smart bracelet approaches the lock, the bracelet arithmetically processes its trusted status credentials—including the bracelet's unique encrypted identity and the current state of its internal security chip—as well as the specific knocking response pattern captured by the bracelet's built-in capacitive sensor array. This response pattern is used to generate unique waveforms formed by the frequency, force, and duration of the knocks. This information is then converted into two unique constraint polynomials: one representing the bracelet's identity and state, and the other representing its physical interaction behavior.

[0084] Next, both the door lock and the wristband pre-share a set of public constraints. This set contains a set of algebraic equations that collectively define the conditions the wristband must satisfy: its identity polynomial, evaluated at a specific common point, must match the registered identity information of the family member; simultaneously, its interaction pattern polynomial, evaluated at another common point, must match a preset "knock" gesture pattern. These equations ensure that verification only succeeds if the wristband is a legitimate family member device and actually performs the correct physical interaction.

[0085] Subsequently, the smart bracelet uses these two private constraint polynomials as secret evidence, combined with the public constraint set provided by the door lock, to perform localized proof generation computation within the bracelet's internal security chip. The bracelet runs a zero-knowledge proof algorithm, proving through a series of complex mathematical operations that its private inputs satisfy all equations defined in the public constraint set, without revealing the bracelet's encrypted identity or specific tapping waveform data to the door lock. This computation process ultimately generates a compact, composite zero-knowledge proof.

[0086] Finally, when the wristband sends this composite zero-knowledge proof to the door lock, the lock receives the proof and, without accessing any sensitive raw data from the wristband, nor knowing the wristband's root key or specific tapping patterns, can verify that the wristband is a legitimate family member device and that the expected physical interaction was indeed performed near the door lock. Once the proof verification is successful, the door lock automatically and securely unlocks. The entire process protects user privacy while ensuring the reliability and immediacy of authentication.

[0087] The overall solution in step S103 above significantly improves the security and efficiency of temporary network authentication between IoT devices by cleverly integrating device identity and physical interaction mode into a zero-knowledge proof framework. Without exposing any sensitive device information, it achieves simultaneous verification of device identity authenticity and physical proximity, effectively resisting man-in-the-middle attacks and identity forgery. It provides a lightweight yet powerful mechanism for establishing trust between devices, thereby enhancing the overall network's security resilience and privacy protection capabilities.

[0088] S104. The composite zero-knowledge proof is sent to the data holding device via a wireless channel on the data requesting device. After receiving the proof, the data holding device independently verifies the validity of the composite zero-knowledge proof locally to confirm the authenticity and reliability of the other party's identity and physical co-existence state.

[0089] Optionally, step S104 may specifically include the following steps:

[0090] S1041. The data request device encapsulates the composite zero-knowledge proof with a set of public verification keys for verifying the composite zero-knowledge proof to form a data packet to be verified, and transmits the data packet to be verified wirelessly to the data holding device.

[0091] S1042. After the data holding device receives the data packet to be verified, the composite zero-knowledge proof and the public verification key are separated from the data packet to be verified.

[0092] S1043. Using the public verification key, the data holding device performs a deterministic verification operation on the composite zero-knowledge proof. By checking whether the output result of the verification operation satisfies a preset algebraic identity, the authenticity of the composite zero-knowledge proof is determined to confirm the authenticity and reliability of the other party's identity and physical co-existence state.

[0093] In the above scheme, a composite zero-knowledge proof is a special type of cryptographic credential that can prove the truth of a statement to a verifier without revealing any specific information about the statement itself. The public verification key is a string of public data paired with the composite zero-knowledge proof, used to verify the validity of the proof. The data packet to be verified refers to the data set formed by encapsulating the composite zero-knowledge proof and the public verification key together, ready for wireless transmission. A wireless channel is the medium for data transmission, such as Bluetooth, Wi-Fi, or a cellular network. An algebraic identity is a mathematically valid equation, used here to determine the correctness of the zero-knowledge proof verification result.

[0094] In this embodiment, firstly, through step S1041, the data requesting device encapsulates its previously generated composite zero-knowledge proof with a set of public verification keys used to verify the proof. This encapsulation process packages the proof itself and the public information required to verify it into a unified data packet to be verified. For example, the data requesting device may use a standard data encapsulation protocol to use the composite zero-knowledge proof as the data payload and the public verification key as the metadata or header information of the data packet, ensuring that the two are closely related and can be correctly parsed by the receiver. This data packet to be verified is then transmitted wirelessly to the data holding device in encrypted or unencrypted form, such as using Bluetooth Low Energy or Wi-Fi Direct. During transmission, the data packet may undergo channel coding and modulation to adapt to the characteristics of the wireless environment and ensure the reliability of data transmission. For example, after generating a composite zero-knowledge proof, a smart bracelet may package it together with the corresponding public verification key and broadcast it to a nearby smart lock via Bluetooth.

[0095] Subsequently, in step S1042, after receiving the data packet to be verified, the data holding device parses it and separates the composite zero-knowledge proof and the public verification key. This separation process is the receiver's decapsulation operation of the data packet. For example, the data holding device identifies and extracts the various components of the data packet according to a preset data packet format. It first parses the data packet header to obtain the public verification key, and then extracts the composite zero-knowledge proof from the data payload. This step ensures that the data holding device can obtain all the necessary information for verification, preparing for subsequent proof verification operations. For example, after receiving a Bluetooth data packet sent by a smart bracelet, the smart door lock parses the data packet and extracts the composite zero-knowledge proof and the public verification key from it.

[0096] Finally, in step S1043, the data holding device uses the extracted public verification key to perform a deterministic verification operation on the composite zero-knowledge proof. This verification operation is the verification phase of the zero-knowledge proof protocol; it is a purely mathematical calculation process that does not involve the disclosure of any private information. The data holding device runs a preset verification algorithm, corresponding to the proof generation algorithm, taking the composite zero-knowledge proof and the public verification key as input. The verification algorithm performs a series of complex algebraic operations and checks whether its output satisfies preset algebraic identities. If the result of the verification operation satisfies these identities, it indicates that the composite zero-knowledge proof is genuine and valid, thus confirming the authenticity of the data requesting device's identity and the reliability of its physical co-existence state; conversely, if the identities do not hold, the proof is invalid. For example, a smart door lock uses the extracted public verification key to perform a verification calculation on a composite zero-knowledge proof sent by a smart bracelet. If the calculation result satisfies a specific algebraic equation, the door lock confirms that the bracelet is legitimate and is indeed nearby.

[0097] In practical applications, a smart sensor (data requesting device) needs to prove its identity and physical proximity to a nearby smart gateway (data holding device) in order to upload environmental data. First, after generating a composite zero-knowledge proof, the smart sensor encapsulates it with a pre-defined public verification key to form a data packet to be verified. This data packet is then sent to the smart gateway via a wireless module. This encapsulation process ensures that the information required for proof and verification is transmitted as a whole.

[0098] Next, upon receiving this data packet to be verified, the smart gateway immediately parses it. The gateway's communication module accurately separates the composite zero-knowledge proof and the public verification key from the received data stream according to a predetermined protocol format. This separation operation is automatic and efficient, ensuring that the gateway obtains all the necessary components for the next step of verification without manual intervention or additional queries.

[0099] Subsequently, the smart gateway uses the separated public verification key to perform rigorous deterministic verification operations on the composite zero-knowledge proof. The security chip inside the gateway runs a zero-knowledge proof verification algorithm that takes the proof and the key as input and performs a series of complex mathematical checks. These checks aim to verify whether the proof satisfies preset algebraic identities, which are inherent mathematical properties of the zero-knowledge proof system, and their validity directly determines the validity of the proof.

[0100] Finally, by checking the output of the verification operation, the smart gateway can determine the authenticity of the composite zero-knowledge proof. If the verification passes, the gateway can be certain that the smart sensor is a certified and legitimate device and is indeed within its physical sensing range. This process reliably confirms the sensor's identity and physical presence without disclosing any sensitive identity information or raw measurement data, thus allowing the sensor to securely begin transmitting environmental data to the gateway.

[0101] The overall solution in step S104 above achieves rapid and privacy-preserving confirmation of the identity and physical co-existence state of the data requesting device through efficient wireless transmission and a localized zero-knowledge proof verification mechanism. It avoids the delays and single-point-of-failure risks of centralized verification, ensures the immediacy and reliability of temporary trusted connections between devices, and maximizes the protection of sensitive device information, thereby enhancing the security and autonomy of the IoT self-organizing network.

[0102] S105. After the composite zero-knowledge proof is verified, the data holding device immediately authorizes the data requesting device to access the data, thereby establishing a temporary, end-to-end encrypted data transmission channel between the data holding device and the data requesting device, which is limited to successful verification interactions.

[0103] Optionally, step S105 may specifically include the following steps:

[0104] S1051. Once the composite zero-knowledge proof is confirmed as valid by the data holding device, a temporary session credential with high randomness is immediately generated locally by the data holding device.

[0105] S1052. The data holding device uses the public key broadcast by the data requesting device in the initial stage of the interaction to encrypt the temporary session credential, and then sends the encrypted temporary session credential back to the data requesting device.

[0106] S1053. The data requesting device and the data holding device use a shared temporary session credential and a preset key negotiation protocol to independently calculate the same session key and use the session key to establish an end-to-end encrypted data transmission channel.

[0107] In the above scheme, the temporary session credential is a string of random data generated by the data holding device, used to identify and authorize the data requesting device for a short period of time. The public key is an encryption key publicly disclosed by the data requesting device at the start of communication, used to encrypt information; only the corresponding private key can decrypt it. The key agreement protocol is a secure communication mechanism that allows the communicating parties to jointly generate a shared key known only to the two parties without directly exchanging keys. The session key is a temporary encryption key generated according to the key agreement protocol, used to encrypt and decrypt all data in this session. An end-to-end encrypted data transmission channel means that data is encrypted throughout its transmission from the sender to the receiver, ensuring that only the communicating parties can read the data content, and that no intermediate links can eavesdrop or tamper with it.

[0108] In this embodiment, firstly, through step S1051, once the data holding device successfully verifies the composite zero-knowledge proof sent by the data requesting device and confirms the legitimacy of its identity and physical location, the data holding device immediately generates a brand-new, highly randomized temporary session credential within itself. This credential acts like a one-time pass; its randomness ensures the uniqueness and security of each connection. For example, after a smart door lock confirms the validity of the zero-knowledge proof of a smart bracelet, it immediately generates a random string of numbers as a temporary session credential.

[0109] Subsequently, in step S1052, the data holding device uses the public key broadcast by the data requesting device at the beginning of the communication to encrypt the newly generated temporary session credential. This encryption method ensures that only the data requesting device with the corresponding private key can successfully decrypt and obtain the credential, thereby preventing the credential from being stolen or tampered with by a third party during transmission. For example, a smart door lock uses the public key of a smart bracelet to encrypt a previously generated random number string, and then sends the encrypted number string back to the smart bracelet.

[0110] Finally, in step S1053, after receiving the encrypted temporary session credential, the data requesting device decrypts it using its own private key to obtain the original temporary session credential. At this point, both the data requesting device and the data holding device possess the same temporary session credential. Next, using this shared temporary session credential, both parties independently calculate the same session key through a pre-defined key negotiation protocol, such as the Diffie-Hellman key exchange protocol. This session key is the secret key for this communication, known only to the two parties. Once the session key is generated, the data requesting device and the data holding device use this key to establish an end-to-end encrypted data transmission channel. All subsequent data exchanges will be conducted through this encrypted channel, ensuring the confidentiality and integrity of the communication content. For example, after a smart bracelet decrypts to obtain a random number string, it and a smart door lock use this number string together to calculate a shared session key through a key negotiation algorithm. Then, this key is used to establish a secure communication channel for subsequent unlocking command transmission.

[0111] In practical applications, imagine a smart home scenario where a smart speaker acts as the data holder and a smartphone acts as the data requester. Once the smartphone proves its legitimate identity and physical proximity to the smart speaker using zero-knowledge proofs, the smart speaker immediately generates a temporary session credential, such as a random hexadecimal string.

[0112] The smart speaker then encrypts the credential using the public key pre-broadcast by the smartphone and sends the encrypted credential to the smartphone. Upon receiving the encrypted credential, the smartphone decrypts it using its private key to obtain the original temporary session credential. At this point, both the smart speaker and the smartphone possess the same temporary session credential.

[0113] Next, using this shared credential, both parties independently calculate the same session key through a pre-defined key negotiation protocol. For example, they can generate a unique session key based on the elliptic curve Diffie-Hellman algorithm, combined with the randomness of the temporary session credential. Once the session key is generated, an end-to-end encrypted data transmission channel is established between the smartphone and the smart speaker. Through this channel, the smartphone can securely send voice commands to the smart speaker, such as playing music or controlling smart lights, without worrying about the commands being eavesdropped on or tampered with. This channel is temporary and valid only for the successfully verified interaction, ensuring the security and efficiency of communication.

[0114] The overall solution in step S105 above significantly improves the security and efficiency of temporary connections between IoT devices by establishing an encrypted communication channel immediately after successful zero-knowledge proof verification. It ensures the confidentiality and integrity of data transmission, effectively resists man-in-the-middle attacks and data eavesdropping, while avoiding dependence on centralized servers and achieving autonomous and secure communication between devices, thereby enhancing the resilience and reliability of the entire IoT ecosystem.

[0115] The following is a complete embodiment for steps S101 to S105:

[0116] In a scenario of temporary trusted networking and data exchange within the Internet of Things (IoT), a smart environmental sensor needs to establish a trusted connection and exchange data with a smart air purifier. Through step S101, during the manufacturing process, the smart environmental sensor utilizes the physically unclonable function formed by its chip to generate a root key representing its unique hardware identity. Simultaneously, the sensor collects its own operational status data in real time, such as battery level, firmware version, and operating temperature. Subsequently, the sensor cryptographically binds this root key with the real-time operational status data to form a device trusted status credential. This credential proves the sensor's authenticity and the integrity of its current operational status.

[0117] In step S102, the capacitive proximity sensor array embedded in the smart environmental sensor housing is activated to actively detect the coupling capacitive field disturbance caused by the smart air purifier approaching. This disturbance accurately reflects the specific physical profile and dielectric constant of the air purifier. The sensor processes this coupling capacitive field disturbance data, transforming it into an interaction response pattern describing the physical interaction process. This pattern is a unique fingerprint of physical proximity and specific shape matching between devices.

[0118] In step S103, the smart environmental sensor uses the previously generated device trusted state credential and the newly acquired interaction response pattern as private inputs to generate a composite zero-knowledge proof. The unique aspect of this proof is that it can simultaneously assert the sensor's identity and the immediacy of its physical proximity to the smart air purifier without directly revealing the sensor's root key and real-time operating status data. This means the air purifier can verify the sensor's legitimacy and whether it is currently in close proximity without obtaining any sensitive information.

[0119] In step S104, the smart environmental sensor transmits the generated composite zero-knowledge proof to the smart air purifier via a Bluetooth Low Energy wireless channel. Upon receiving the proof, the smart air purifier independently verifies its validity locally using preset public verification parameters. This verification process is deterministic, aiming to confirm whether the proof satisfies a specific algebraic identity. Once the verification is successful, the air purifier can confirm the sensor's authentic identity and its physical presence, laying a foundation of trust for subsequent data exchange.

[0120] In step S105, after the composite zero-knowledge proof is locally verified by the smart air purifier, the air purifier immediately grants the smart environmental sensor data access permissions. Subsequently, the two devices independently calculate the same session key using a shared temporary session credential and a preset key negotiation protocol. This session key is then used to establish a temporary, end-to-end encrypted data transmission channel between the smart environmental sensor and the smart air purifier, limited to this successful verification interaction. This channel ensures the confidentiality, integrity, and immutability of subsequent environmental data (such as air quality index, temperature, and humidity) during transmission, enabling secure communication even in open wireless environments.

[0121] The zero-knowledge proof-based data access control method provided in this application constructs multi-dimensional trusted credentials by combining the device's inherent hardware identity, real-time operating status, and physical proximity awareness. It utilizes zero-knowledge proof technology to quickly confirm the device's identity and physical co-existence state without disclosing sensitive information. This enables devices to autonomously establish temporary trusted connections, effectively resisting man-in-the-middle attacks and device forgery. It significantly improves the security, resilience, and immediacy of data exchange in IoT self-organizing networks. It eliminates the need for centralized server authentication, thereby reducing system complexity and potential single point of failure risks, and providing a solid foundation for direct and secure interaction between IoT devices.

[0122] Figure 4 This application provides a schematic diagram of a specific implementation of a data access control system based on zero-knowledge proof, referring to... Figure 4 The system may include:

[0123] The binding module 41 is used to generate a root key representing a unique hardware identity by using a physically unclonable function formed during the chip manufacturing process of the data requesting device in a scenario where the data requesting device needs to perform trusted interaction with the data holding device, and to synchronously collect the real-time operating status data of the data requesting device, and to cryptographically bind the root key with the real-time operating status data to form a trusted status credential of the device.

[0124] Processing module 42 is used to drive the capacitive proximity sensor array embedded in the housing of the data request device to actively detect the coupled capacitive field disturbance caused by the data holding device approaching, which can reflect a specific physical contour and dielectric constant, and process the coupled capacitive field disturbance into an interactive response mode describing the physical interaction process.

[0125] The generation module 43 is used to take the trusted state credential of the device and the interaction response mode as private inputs to generate a composite zero-knowledge proof. Through the composite zero-knowledge proof, without disclosing the root key and the real-time running status data, it asserts to the data holding device the authenticity of the identity of the data requesting device and the immediacy of its physical proximity.

[0126] The verification module 44 is used to send the composite zero-knowledge proof to the data holding device via a wireless channel on the data request device. After receiving the proof, the data holding device independently verifies the validity of the composite zero-knowledge proof locally to confirm the authenticity and reliability of the other party's identity and physical co-existence state.

[0127] Module 45 is configured to, after the composite zero-knowledge proof has been verified, immediately authorize the data requesting device to access the data through the data holding device, thereby establishing a temporary, end-to-end encrypted data transmission channel between the data holding device and the data requesting device, which is limited to successful verification interactions.

[0128] The data access control system based on zero-knowledge proof in this application is used to implement the aforementioned data access control method based on zero-knowledge proof. Therefore, the specific implementation of the data access control system based on zero-knowledge proof can be found in the embodiment section of the data access control method based on zero-knowledge proof above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0129] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described zero-knowledge proof-based data access control methods.

[0130] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described zero-knowledge proof-based data access control methods.

[0131] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0132] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the data access control method based on zero-knowledge proof.

[0133] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0134] The data access control method and system based on zero-knowledge proof provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A data access control method based on zero-knowledge proof, characterized in that, include: In scenarios where a data requesting device needs to interact with a data holding device in a trusted manner, a root key representing a unique hardware identity is generated using a physically unclonable function formed during the chip manufacturing process of the data requesting device. Simultaneously, real-time operating status data of the data requesting device is collected, and the root key is cryptographically bound to the real-time operating status data to form a trusted status credential for the device. The capacitive proximity sensor array embedded in the housing of the data requesting device is driven to actively detect the coupled capacitive field disturbance caused by the data holding device approaching, which can reflect the physical contour and dielectric constant, and process the coupled capacitive field disturbance into an interactive response mode describing the physical interaction process. The trusted state credential of the device and the interaction response mode are used as private inputs to generate a composite zero-knowledge proof. Through the composite zero-knowledge proof, without disclosing the root key and the real-time running status data, the authenticity of the identity of the data requesting device and the immediacy of its physical proximity are asserted to the data holding device. The composite zero-knowledge proof is sent to the data holding device via a wireless channel on the data requesting device. After receiving it, the data holding device independently verifies the validity of the composite zero-knowledge proof locally to confirm the authenticity and reliability of the other party's identity and physical co-existence state. After the composite zero-knowledge proof is verified, the data holding device immediately authorizes the data requesting device to access the data, thereby establishing a temporary, end-to-end encrypted data transmission channel between the data holding device and the data requesting device, which is limited to successful verification interactions. The step of using the device's trusted state credentials and the interaction response mode as private inputs to generate a composite zero-knowledge proof, and using this composite zero-knowledge proof to simultaneously assert the identity authenticity and physical proximity of the data requesting device to the data holding device without disclosing the root key and the real-time operating status data, includes: The trusted state credentials of the device and the interaction response pattern are subjected to independent arithmetic processing to convert each of the trusted state credentials of the device and the interaction response pattern into a constraint polynomial with a unique correspondence. Establish a public constraint set for coupling the constraint polynomial. The public constraint set contains a set of preset algebraic equations. The algebraic equations describe the determination conditions under which the identity authenticity and physical proximity of the data requesting device must be met simultaneously without disclosing the root key and the real-time running status data. The data requesting device invokes the public constraint set and uses the constraint polynomial as a private witness to perform localized proof generation calculations based on the decision conditions, producing a publicly verifiable composite zero-knowledge proof.

2. The method according to claim 1, characterized in that, The capacitive proximity sensor array embedded in the housing of the data requesting device actively detects the coupled capacitive field disturbance caused by the proximity of the data holding device, which reflects the physical contour and dielectric constant, and processes the coupled capacitive field disturbance into an interactive response mode describing the physical interaction process, including: Drive each sensing unit in the capacitive proximity sensing array to continuously capture the original capacitive signal stream generated by the physical proximity of the data holding device within a preset detection period; A time-frequency domain transformation is performed on the original capacitive signal stream to extract amplitude fluctuation information that characterizes the physical contour of the data holding device, and phase drift information that characterizes the dielectric properties of the data holding device is extracted simultaneously, forming amplitude feature set and phase feature set respectively; The amplitude feature set and the phase feature set are projected together into a preset high-dimensional feature space. By performing nonlinear fitting on the distribution relationship of the amplitude feature set and the phase feature set in the high-dimensional feature space, a vectorized interactive response pattern that can uniquely identify physical contact events is generated.

3. The method according to claim 1, characterized in that, After the composite zero-knowledge proof is verified, the data holding device immediately authorizes the data requesting device to access the data, thereby establishing a temporary, end-to-end encrypted data transmission channel between the data holding device and the data requesting device, limited to successful verification interactions, including: Once the composite zero-knowledge proof is confirmed as valid by the data holding device, a temporary session credential with high randomness is immediately generated locally by the data holding device. The data holding device uses the public key broadcast by the data requesting device at the initial stage of the interaction to encrypt the temporary session credential, and then sends the encrypted temporary session credential back to the data requesting device. The data requesting device and the data holding device use a shared temporary session credential and a preset key negotiation protocol to independently calculate the same session key and use the session key to establish an end-to-end encrypted data transmission channel.

4. The method according to claim 1, characterized in that, The process involves transmitting the composite zero-knowledge proof via a wireless channel from the data requesting device to the data holding device. Upon receiving the proof, the data holding device independently verifies its validity locally to confirm the authenticity and reliability of the other party's identity and physical co-existence status. This includes: The data request device encapsulates the composite zero-knowledge proof with a set of public verification keys for verifying the composite zero-knowledge proof to form a data packet to be verified, and transmits the data packet to be verified wirelessly to the data holding device. After the data holding device receives the data packet to be verified, it separates the composite zero-knowledge proof and the public verification key from the data packet to be verified. The data holding device uses the public verification key to perform deterministic verification operations on the composite zero-knowledge proof. By checking whether the output of the verification operation satisfies a preset algebraic identity, the authenticity of the composite zero-knowledge proof is determined, thereby confirming the authenticity and reliability of the other party's identity and physical co-existence state.

5. The method according to claim 1, characterized in that, The step involves establishing a public constraint set for coupling the constraint polynomial. This public constraint set contains a set of preset algebraic equations that describe the conditions under which the authenticity of the data requesting device's identity and its physical proximity must simultaneously be met without disclosing the root key and the real-time operating status data. These equations include: Independent decision conditions are set for each of the constraint polynomials. The decision conditions require that when the input is valid, the result of the constraint polynomial after the operation is always equal to a preset zero value. Construct a coupling equation, through which randomized variables are introduced to linearly combine the decision conditions into a single algebraic expression, the overall calculation result of which is equal to the zero value only when two independent decision conditions are satisfied simultaneously; The coupled equations and all auxiliary constraints required to satisfy them are compiled into a structured set of public constraints, which serves as the unified computational framework for generating the composite zero-knowledge proof.

6. The method according to claim 2, characterized in that, The step of projecting the amplitude feature set and the phase feature set together into a preset high-dimensional feature space, and generating a vectorized interactive response pattern that can uniquely identify a physical contact event by performing nonlinear fitting on the distribution relationship of the amplitude feature set and the phase feature set in the high-dimensional feature space, includes: A set of preset nonlinear basis functions are used to perform a dimension-up transformation on each data point in the amplitude feature set and the phase feature set, mapping them into a point cloud distributed in the high-dimensional feature space; In the high-dimensional feature space, the geometric parameters of a hypersurface are iteratively adjusted so that the hypersurface can maximize the separation of the point cloud, thereby capturing the intrinsic correlation structure between the amplitude feature set and the phase feature set. The finalized geometric parameters are arranged in a predetermined order and solidified into a numerical vector, and a vectorized interactive response mode is formed based on the numerical vector.

7. A data access control system based on zero-knowledge proof, used to execute the steps of the data access control method based on zero-knowledge proof according to any one of claims 1 to 6, characterized in that, include: The binding module is used to generate a root key representing a unique hardware identity by using a physically unclonable function formed during the chip manufacturing process of the data requesting device in scenarios where the data requesting device needs to perform trusted interaction with the data holding device. It also synchronously collects the real-time operating status data of the data requesting device and cryptographically binds the root key with the real-time operating status data to form a trusted status credential of the device. The processing module is used to drive the capacitive proximity sensor array embedded in the housing of the data requesting device to actively detect the coupled capacitive field disturbance caused by the data holding device approaching, which can reflect the physical contour and dielectric constant, and process the coupled capacitive field disturbance into an interactive response mode describing the physical interaction process. The generation module is used to take the trusted state credential of the device and the interaction response mode as private inputs to generate a composite zero-knowledge proof. Through the composite zero-knowledge proof, without disclosing the root key and the real-time running status data, it asserts to the data holding device the authenticity of the data requesting device's identity and the immediacy of its physical proximity. The verification module is used to send the composite zero-knowledge proof to the data holding device via a wireless channel on the data request device. After receiving the proof, the data holding device independently verifies the validity of the composite zero-knowledge proof locally to confirm the authenticity and reliability of the other party's identity and physical co-existence state. An establishment module is used to immediately authorize the data requesting device to access data after the composite zero-knowledge proof has been verified, thereby establishing a temporary, end-to-end encrypted data transmission channel between the data holding device and the data requesting device, which is limited to successful verification interactions.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the zero-knowledge proof-based data access control method as described in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the data access control method based on zero-knowledge proof as described in any one of claims 1 to 6.

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