Data access control method and system based on zero knowledge proof

Abstract

The invention provides a data access control method and system based on zero-knowledge proof, and relates to the technical field of data access control. According to the method, a root key of a unique hardware identity is generated through a physical unclonable function of a data request equipment chip, and meanwhile, real-time running state data of equipment is collected; the two are subjected to cryptographic binding to form an equipment credible state voucher; secondly, detecting capacitance field disturbance when data holding equipment approaches through a capacitive proximity sensor array, and generating an interactive response mode; then, the equipment credible state certificate and the interactive response mode serve as private input to generate a composite zero-knowledge proof, and identity authenticity and physical proximity are proved to data holding equipment at the same time on the premise that sensitive information is not leaked; and after the data holding device passes verification, the data access permission is authorized immediately, and a temporary encryption communication channel is established, so that the reliability and instantaneity of security authentication between IoT devices can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data access control, and particularly relates to a data access control method and system based on zero-knowledge proof. BACKGROUND

[0002] In the self-organizing network environment of Internet of Things devices, a secure and trusted temporary communication connection needs to be established between devices, while ensuring that the identity authentication process can resist man-in-the-middle attacks and protect sensitive information of the devices from being leaked. This scenario requires that the authentication scheme must consider both physical proximity verification and identity authenticity proof, and cannot rely on a centralized authentication agency or a preset key distribution mechanism, in order to adapt to the needs of dynamic networking and temporary cooperation of devices.

[0003] The mainstream solution to this requirement at present is a proximity authentication technology based on Bluetooth signal strength. This method measures the degree of signal attenuation to determine the physical distance between devices, and completes two-way authentication in combination with a preset key exchange protocol. The system generates a temporary session key for each pair of communication devices, and automatically triggers the authentication process when the signal strength reaches a preset threshold. After the authentication is passed, an encrypted communication channel is established.

[0004] However, this solution has obvious defects. The signal strength is easily affected by environmental interference, leading to misjudgment, and cannot distinguish between real proximity devices and signal relays forged by attackers. At the same time, the preset key exchange mechanism cannot prevent man-in-the-middle attacks, and the authentication process exposes the device identity information. More importantly, this method lacks a hardware-level trusted root, and cannot prove the inseparable relationship between device identity and physical proximity, so that attackers may break through the security line through replay attacks and other means. SUMMARY

[0005] The present application aims to provide a data access control method and system based on zero-knowledge proof, to solve the problems of insufficient reliability and immediacy of device-to-device security authentication in the prior art.

[0006] To solve the above technical problems, in a first aspect, the present application provides a data access control method based on zero-knowledge proof, comprising: In the scenario where a data request device needs to perform trusted interaction with a data holding device, a root key representing a unique hardware identity is generated using a physically unclonable function formed during the chip manufacturing process of the data request device, and real-time running state data of the data request device is synchronously collected. The root key and the real-time running state data are cryptographically bound to form a device trusted state credential. driving a capacitive proximity sensing array embedded in the data request device housing to actively probe a coupling capacitance field disturbance caused by the data holding device approaching, which can reflect a specific physical profile and dielectric constant, and processing the coupling capacitance field disturbance into an interaction response pattern describing a physical interaction process; using the device trusted state credential and the interaction response pattern as private inputs to jointly generate a composite zero-knowledge proof, through which the identity authenticity of the data request device and the immediacy of physical proximity are simultaneously asserted to the data holding device without revealing the root key and the real-time running state data; sending the composite zero-knowledge proof from the data request device to the data holding device over a wireless channel, and after the data holding device receives it, independently verifying the validity of the composite zero-knowledge proof locally to confirm the authenticity and reliability of the identity and physical coexistence state of the other party; after the composite zero-knowledge proof passes the verification, authorizing the data access permission of the data request device by the data holding device in real time, thereby establishing a temporary, end-to-end encrypted data transmission channel between the data holding device and the data request device, which is only limited to successful verification interactions.

[0007] Optionally, using the device trusted state credential and the interaction response pattern as private inputs to jointly generate a composite zero-knowledge proof, through which the identity authenticity of the data request device and the immediacy of physical proximity are simultaneously asserted to the data holding device without revealing the root key and the real-time running state data, includes: independently performing arithmetic processing on the device trusted state credential and the interaction response pattern to convert each of the device trusted state credential and the interaction response pattern into a constraint polynomial with a unique corresponding relationship; establishing a public constraint set for coupling the constraint polynomial, which contains a set of preset algebraic equations describing the judgment condition that the identity authenticity of the data request device and the physical proximity must be simultaneously true without revealing the root key and the real-time running state data; invoking the public constraint set by the data request device and using the constraint polynomial as a private witness to perform localized proof generation calculation based on the judgment condition, and outputting a publicly verifiable composite zero-knowledge proof.

[0008] Optionally, the driving the capacitive proximity sensing array embedded in the data request device shell actively detects the coupling capacitance field disturbance caused by the data holding device approaching, which can reflect the specific physical profile and dielectric constant, and processes the coupling capacitance field disturbance into an interaction response mode describing the physical interaction process, including: Driving each sensing unit in the capacitive proximity sensing array, continuously capturing the original capacitance signal stream generated by the physical proximity of the data holding device in the preset detection period; Performing time-frequency domain transformation on the original capacitance signal stream, extracting amplitude fluctuation information representing the physical profile of the data holding device, and synchronously extracting phase shift information representing the dielectric properties of the data holding device, to form amplitude feature set and phase feature set respectively; Projecting the amplitude feature set and the phase feature set into a preset high-dimensional feature space together, and generating a vectorized interaction response mode capable of uniquely identifying a physical contact event by nonlinear fitting the distribution relationship of the amplitude feature set and the phase feature set in the high-dimensional feature space.

[0009] Optionally, after the composite zero-knowledge proof is verified, the data request device is authorized by the data holding device for data access, thereby establishing a temporary, only for successful verification interaction, end-to-end encrypted data transmission channel between the data holding device and the data request device, 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 broadcasted by the data request device in the initial stage of interaction to encrypt the temporary session credential, and returns the encrypted temporary session credential to the data request device; The data request device and the data holding device use the shared temporary session credential to calculate the same session key independently through the preset key agreement protocol, and use the session key to establish an end-to-end encrypted data transmission channel.

[0010] Optionally, the composite zero-knowledge proof is sent to the data holding device through a wireless channel on the data request device, and after the data holding device receives it, the validity of the composite zero-knowledge proof is verified locally to confirm the true and reliable identity and physical coexistence state of the other party, including: 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 package to be verified, and transmits the data package to be verified to the data holding device through a wireless manner; After the data holding device receives the data package to be verified, the composite zero-knowledge proof and the public verification keys are separated from the data package to be verified; The data holding device performs a deterministic verification operation on the composite zero-knowledge proof by using the public verification keys, and determines the authenticity of the composite zero-knowledge proof by checking whether the output result of the verification operation satisfies a preset algebraic identity, so as to confirm the real and reliable identity and physical coexistence state of the other party.

[0011] Optionally, the establishment of a public constraint set for coupling the constraint polynomials includes: An independent determination condition is set for each constraint polynomial, which requires that when the input is valid, the result of the constraint polynomial after a specific operation is equal to a preset zero value; A coupling equation is constructed, which linearly combines the determination conditions into a single algebraic expression by introducing random variables, and the overall calculation result of the algebraic expression is equal to the zero value only when the two independent determination conditions are satisfied simultaneously; The coupling equation and all auxiliary constraint relationships required to satisfy the coupling equation are collectively compiled into a structured public constraint set, and the public constraint set is used as a unified calculation framework for generating the composite zero-knowledge proof.

[0012] Optionally, the amplitude feature set and the phase feature set are projected into a preset high-dimensional feature space, and a vectorized interaction response mode capable of uniquely identifying a physical contact event is generated by performing nonlinear fitting on the distribution relationship between the amplitude feature set and the phase feature set in the high-dimensional feature space, including: A set of preconfigured nonlinear basis functions are used to perform dimension lifting transformation on each data point in the amplitude feature set and the phase feature set, and the data points are mapped into a point cloud distributed in the high-dimensional feature space; In the high-dimensional feature space, the geometric parameters of a hyper-surface are iteratively adjusted so that the hyper-surface can maximize the separation of the point cloud, thereby capturing the internal association structure between the amplitude feature set and the phase feature set; The finally determined geometric parameters are arranged in a predetermined order and solidified into a numerical vector, and a vectorized interactive response pattern is constructed based on the numerical vector.

[0013] In a second aspect, the present application provides a data access control system based on zero-knowledge proof, comprising: A binding module is configured to, in a scenario where a data requesting device needs to perform trusted interaction with a data holding device, utilize a 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 collect real-time operating status data of the data requesting device, cryptographically bind the root key to the real-time operating status data, and form a device trusted status credential; a processing module configured to drive a capacitive proximity sensor array embedded in the housing of the data requesting device to actively detect coupling capacitance field disturbances caused by the proximity of the data holding device, which disturbances can reflect specific physical contours and dielectric constants, and process the coupling capacitance field disturbances into an interactive response pattern describing a physical interaction process; a generation module configured to use the device trusted status credential and the interactive response pattern as private inputs to jointly generate a composite zero-knowledge proof, wherein the composite zero-knowledge proof simultaneously asserts the authenticity of the identity and the immediacy of the physical proximity of the data requesting device to the data holding device without revealing the root key and the real-time operating status data; a verification module configured to transmit the composite zero-knowledge proof from the data requesting device to the data holding device via a wireless channel, and upon receipt by the data holding device, independently verify the validity of the composite zero-knowledge proof locally to confirm the authenticity and reliability of the other party's identity and physical co-presence; An establishment module is used to instantly authorize the data access rights of the data requesting device through the data holding device after the composite zero-knowledge proof is verified, thereby establishing a temporary, end-to-end encrypted data transmission channel between the data holding device and the data requesting device that is limited to successfully verified interactions.

[0014] In a third aspect, the present application provides an electronic device, comprising: Memory for storing computer programs; A processor is configured to implement the steps of the data access control method based on zero-knowledge proof as described in the first aspect above when executing the computer program.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the steps of the data access control method based on zero-knowledge proof as described in the first aspect above.

[0016] The zero-knowledge proof-based data access control method provided in the application can generate a root key representing a unique hardware identity by using a physically unclonable function formed in the chip manufacturing process of a data request device in a scenario where the data request device needs to perform trusted interaction with a data holding device, and can synchronously collect real-time running state data of the data request device, can perform cryptographic binding between the root key and the real-time running state data to form a device trusted state credential, can effectively solve the problems of easy device identity forgery and non-transparent state information in traditional identity authentication, can lay a hardware-level security foundation for subsequent trusted interaction, and can ensure the uniqueness of the device identity and the real-time trustworthiness of the state, can combine the inherent physically unclonable characteristics of the device chip with the real-time running state data and perform cryptographic binding, can ensure the uniqueness of the device identity and the real-time trustworthiness of the state, can actively detect a coupling capacitance field disturbance caused by the approach of the data holding device and reflecting a specific physical profile and dielectric constant by driving a capacitive proximity sensing array embedded in the shell of the data request device, and can process the coupling capacitance field disturbance into an interaction response mode describing a physical interaction process, can innovatively use the capacitive proximity sensing array to generate an interaction response mode accurately reflecting the physical profile and dielectric characteristics of the opposite device by detecting and processing the unique coupling capacitance field disturbance generated when the devices are in physical proximity, can overcome the limitations of traditional signal strength-based proximity verification, such as being susceptible to environmental interference and being unable to distinguish between real physical contact and signal forgery, and can provide a high-precision and difficult-to-forgery proof of physical proximity, can use the device trusted state credential and the interaction response mode as private inputs to generate a composite zero-knowledge proof, can simultaneously assert the authenticity of the identity of the data request device and the immediacy of the physical proximity to the data holding device without revealing the root key and the real-time running state data through the composite zero-knowledge proof, can use the device trusted state credential and the interaction response mode as private inputs to generate a composite zero-knowledge proof, can realize the ability to simultaneously prove the authenticity of the device identity and the immediacy of the physical proximity to the verifier without revealing any sensitive identity information and physical proximity details, can greatly enhance the privacy protection and security of the authentication process, and can effectively resist information leakage risks, and can transmit the composite zero-knowledge proof from the data request device to the data holding device through a wireless channel, and can independently verify the validity of the composite zero-knowledge proof locally at the data holding device after receiving the composite zero-knowledge proof, to confirm the authenticity and reliability of the identity and the physical co-presence state of the opposite device, can efficiently transmit the composite zero-knowledge proof through a wireless channel and perform local independent verification at the receiving device end, can ensure the decentralization and efficiency of the authentication process, avoid dependence on third-party authentication agencies, ensure the objective impartiality of the verification result, and improve the flexibility and response speed of ad hoc network authentication.By instantaneously authorizing the data access permission of the data request device through the data holding device after the composite zero-knowledge proof passes the verification, a temporary, end-to-end encrypted data transmission channel limited to the successful verification interaction is established between the data holding device and the data request device, which can instantaneously authorize the data access permission and establish a temporary, end-to-end encrypted data transmission channel after the composite zero-knowledge proof passes the verification, ensure the confidentiality and integrity of data transmission, realize the on-demand access control based on trusted authentication, effectively prevent unauthorized data theft and tampering, and ensure the security of communication between devices.

[0017] Further, the device trust state credential and the interaction response mode are arithmetically processed to be converted into constraint polynomials that can be operated by cryptography. Subsequently, a public constraint set containing preset algebraic equations is constructed, and the equations accurately describe the judgment conditions that the identity authenticity and the physical proximity of the data request device must satisfy at the same time. Finally, the data request device uses the public constraint set to execute a localized proof generation calculation by taking the constraint polynomials as private witnesses, thereby outputting a compact and publicly verifiable composite zero-knowledge proof. The core process realizes the joint assertion of device identity and physical proximity without exposing any original sensitive data by uniformly encoding multiple source heterogeneous trust credentials into mathematical structures and using the underlying mechanism of zero-knowledge proof. This not only greatly improves the privacy protection level of the authentication process, but also ensures the non-falsifiability and verifiability of the authentication result, providing key technical support for building a highly secure and privacy-friendly ad hoc network authentication system. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0019] Figure 1 A flowchart of a data access control method based on zero-knowledge proof provided by an embodiment of the present application;

[0020] Figure 2 A specific implementation schematic diagram of a data access control method based on zero-knowledge proof provided by an embodiment of the present application;

[0021] Figure 3 A specific implementation schematic diagram of a data access control method based on zero-knowledge proof provided by an embodiment of the present application;

[0022] Figure 4A schematic diagram of the structure of a data access control system based on zero-knowledge proof provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] In the ad hoc networking environment of 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 initial physical distance determination and key exchange, suffer from significant drawbacks: Signals are susceptible to environmental interference, leading to misjudgment and ineffectively distinguishing between genuine nearby devices and forged signal relays. Pre-set key exchange mechanisms are vulnerable to man-in-the-middle attacks, and the authentication process can leak sensitive device identity information. A further problem lies in the lack of a hardware-level root of trust, making it impossible to prove the inseparable relationship between device identity and physical proximity. This makes the solution vulnerable to threats such as replay attacks, severely limiting the reliability and robustness of secure communication between devices in ad hoc networks.

[0024] In response to the deficiencies of the above-mentioned existing technologies, this application proposes a data access control method based on zero-knowledge proof, the core of which lies in the coordinated application of device status data, capacitive proximity sensor arrays, and physical unclonability. This solution generates a unique root of trust for the device through PUF, and builds trusted credentials in combination with real-time device status data; at the same time, it uses a capacitive proximity sensor array to accurately sense physical proximity interaction patterns. These are used as private inputs to generate a composite proof through a zero-knowledge proof mechanism, which verifies the authenticity and physical proximity of the device identity without leaking any sensitive information. This solution fundamentally solves the problems of traditional methods such as signal susceptibility to interference, insufficient protection against man-in-the-middle attacks, identity information leakage, and lack of hardware-level root of trust. It significantly improves the security, reliability, and anti-attack capabilities of IoT device self-organizing network authentication, and provides strong protection for data access control in dynamic and temporary collaborative scenarios.

[0025] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the embodiments described are only a part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present application.

[0026] The core of this application is to provide a data access control method based on zero-knowledge proof, and a flowchart of a specific implementation method is shown as follows: Figure 1 As shown, the method includes: S101, in the scene that the data request device needs to interact with the data holding device, a root key representing the unique hardware identity is generated by using the physically unclonable function formed in the chip manufacturing process of the data request device, and real-time running state data of the data request device is synchronously collected, the root key and the real-time running state data are bound by cryptography, and a device trusted state certificate is constructed; In the above scheme, the physically unclonable function uses the physical difference in the chip manufacturing process to generate a unique digital fingerprint of the device, ensuring that 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 hardware. The real-time running state data is the current power, temperature, network connection and other information of the device, reflecting the actual working environment of the device. Cryptographic binding is to tightly associate the data through encryption algorithms to ensure integrity. The device trusted state certificate is a digital proof formed after combining the unique hardware identity of the device and the current running state data through cryptographic processing, which reliably indicates the effectiveness of the device identity and state.

[0027] In the embodiments of the present application, first, when the device needs secure interaction, the system utilizes the physically unclonable function (PUF) formed in the chip manufacturing process. PUF is a unique physical fingerprint of the chip. By exciting PUF and measuring its response, a 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 physical characteristics. After digitization and error correction, the root key is obtained.

[0028] Secondly, while generating the root key, the system synchronously collects the real-time running state data of the device. These data include the power, CPU usage, network connection type, etc. of the device, reflecting the current real running environment and state of the device. Finally, the root key and the real-time running state data are bound by cryptography to form a device trusted state certificate. This is usually achieved through a hash function or an encryption algorithm. The root key and the real-time running state data are input, and a unique digest value is calculated through a secure hash algorithm, or the state data is encrypted and signed using the root key. Any tampering with the root key or the state data will result in verification failure, ensuring the inseparability of the device identity and the current state. For example, the hash value of the root key and the real-time running state data is calculated after splicing, and the hash value is the device trusted state certificate, proving the device identity and the current state.

[0029] S102, drive the capacitive proximity sensor array embedded in the shell of the data request device, actively detect the coupling capacitance field disturbance caused by the data holding device approaching, which can reflect the specific physical profile and dielectric constant, and process the coupling capacitance field disturbance into an interaction response mode describing the physical interaction process;

[0030] Alternatively, as Figure 2 As shown, step S102 may specifically include the following steps: S1021: driving each sensing unit in the capacitive proximity sensing array to continuously capture a raw capacitive signal stream generated by the physical proximity of the data holding device within a preset detection period; S1022. Performing a time-frequency domain transformation on the original capacitance signal stream to extract amplitude fluctuation information representing the physical profile of the data holding device, and simultaneously extracting phase drift information representing the dielectric properties of the data holding device, to form an amplitude feature set and a phase feature set respectively; S1023. Project the amplitude feature set and the phase feature set together into a preset high-dimensional feature space, and generate a vectorized interactive response pattern that can uniquely identify a physical contact event by performing nonlinear fitting on the distribution relationship between the amplitude feature set and the phase feature set in the high-dimensional feature space.

[0031] Among them, step S1023 may specifically include the following processes: using a set of preset nonlinear basis functions to perform dimensionality lifting transformation on each data point in the amplitude feature set and the phase feature set, and mapping them into point clouds 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 forming a vectorized interactive response pattern based on the numerical vector.

[0032] In the above scheme, the capacitive proximity sensor array is a system composed of multiple capacitive sensors that can detect changes in the electric field caused by nearby objects. Coupled capacitive field perturbation refers to the effect of an object's physical shape and material properties on the electric field when it approaches a capacitive sensor. The raw capacitive signal stream is the time-varying capacitance data continuously collected by the capacitive sensor. Time-frequency domain transformation is a mathematical method that converts signals from the time domain to the frequency domain, used to analyze the signal's frequency components. Amplitude fluctuations reflect changes in signal strength and are related to the physical contours of the object. Phase shifts reflect the offset of the signal waveform's starting point and are related to the object's dielectric properties. The amplitude feature set and phase feature set are sets of extracted amplitude and phase information, respectively. A high-dimensional feature space is an abstract mathematical space used to represent complex data points, where each dimension represents a feature. Vectorized interaction response patterns represent the characteristics of the physical interaction process as a multidimensional digital vector, facilitating computer processing and recognition.

[0033] In the embodiments of the present application, first, through step S1021, each sensing unit in the capacitive proximity sensing array is activated to start working according to a preset detection period, continuously emitting a weak electric field to the surrounding space and receiving the reflected signal, so as to continuously measure the capacitance value of its surrounding environment. When the data holding device gradually approaches, its physical existence and material properties will change the electric field distribution around the sensing array, causing the sensing unit to capture a series of original capacitance signals that change over time. The slight fluctuations of these signals accurately reflect the dynamic process of the approaching object. For example, when a user's finger approaches in a certain posture, the capacitive array on a smart bracelet will continuously record the slight changes in capacitance value at a very high frequency, forming a continuous, time-stamped capacitance signal sequence that contains the complete dynamic information of the finger approaching, contacting, and leaving.

[0034] Subsequently, through step S1022, fine time-frequency domain transformation processing is performed on these continuously captured original capacitance signal streams. This process aims to decompose the original, 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 original signal is converted from the time domain to the frequency domain, so that two key feature sets can be extracted: one is the amplitude fluctuation information, which reflects the energy intensity changes of the signal at different frequencies, and these changes are closely related to the physical profile of the approaching object (such as its size, shape, and surface area), forming the amplitude feature set; the other is the phase shift information, which reflects the starting point offset of the signal waveform at different frequencies, and these offsets are closely related to the dielectric properties of the approaching object (such as the conductivity and insulation of its material), forming the phase feature set. For example, performing short-time Fourier transform on the collected capacitance signal can obtain a series of frequency spectra, from which the energy peak values in a certain frequency range and their delay phases relative to the reference signal can be extracted, and these values constitute the amplitude feature set and the phase feature set.

[0035] Finally, the amplitude feature set and the phase feature set obtained above are jointly projected into a pre-set high-dimensional feature space by step S1023. In this high-dimensional space, each physical interaction event, regardless of its complexity, will be represented as a unique, multi-dimensional digital point. By performing a non-linear fitting on the data point distribution formed by the amplitude feature set and the phase feature set in the high-dimensional space, for example, using a machine learning algorithm such as support vector machine, neural network or Gaussian mixture model, an optimal mathematical model can be found to describe and distinguish different physical interaction patterns. This model is finally solidified as a vectorized interaction response pattern, which is a multi-dimensional vector that can uniquely and accurately identify a specific physical contact event, thereby converting the complex physical interaction process into a computable and identifiable digital representation. For example, the key values in the amplitude feature set and the phase feature set are taken as coordinates in the high-dimensional space to form a data point cloud, and then a neural network model is trained to enable it to recognize the point cloud distribution corresponding to different interaction patterns, and the output layer of the neural network can generate a vector representing the vectorized representation of the interaction pattern.

[0036] In practical applications, in the scenario of temporary trusted networking and data exchange between Internet of Things devices, for example, a smart access control system / data request device needs to prove its legitimacy to a smart key / data holding device without the intervention of a central server to issue certificates. When the smart key is close to the smart access control, the capacitive proximity sensor array embedded in the access control system shell will actively detect the capacitive field disturbance caused by the key approaching, and process these disturbances into interaction response patterns that describe the physical interaction process. First, through step S1021, the capacitive sensing unit in the access control system will continuously capture the original capacitive signal stream generated when the smart key approaches at a very high sampling rate. These signal streams contain the changes in the capacitive value during the entire dynamic process of the key approaching and touching the surface of the access control. Second, through step S1022, the access control system performs time-frequency domain transformation on these original capacitive signal streams, for example, using continuous wavelet transform, to extract amplitude fluctuation information reflecting the physical profile of the key, such as the shape and size of the key teeth, and phase shift information reflecting the dielectric properties of the key material, such as metal or plastic parts, to form amplitude feature sets and phase feature sets, respectively. For example, by analyzing the energy distribution and phase spectrum of the wavelet coefficients, the energy response and time delay of the key at different frequencies can be quantified. Finally, through step S1023, the amplitude feature sets and phase feature sets are projected together into a pre-set 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 legal key form a specific cluster in the feature space, when a new interaction pattern is detected, the distance between the new pattern and the known legal patterns can be calculated to determine whether it is a legal interaction. This vectorized interaction response pattern can then be used as one of the private inputs for zero-knowledge proof 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.

[0037] The overall scheme of step S102 described above can accurately capture and quantify the physical interaction characteristics between devices by actively driving the capacitive proximity sensor array and performing fine time-frequency domain analysis and high-dimensional feature space mapping on the captured coupled capacitive field disturbances. This method overcomes the limitations of traditional signal strength-based judgment, effectively avoids misjudgment caused by environmental interference and signal forgery, and significantly improves the accuracy and reliability of physical proximity verification. By converting physical interaction into a computable vectorized pattern, it provides a solid foundation for subsequent encryption authentication and data exchange, enhancing the self-organizing security capabilities of Internet of Things devices in a decentralized trust environment.

[0038] S103, using the device trusted state credential and the interaction response pattern as private inputs to generate a composite zero-knowledge proof that simultaneously asserts the identity authenticity and the physical proximity of the data requesting device without revealing the root key and the real-time running state data to the data holding device;

[0039] Optionally, as shown in FIG. 1 1, step S103 can specifically include the following steps: Figure 3 S1031, independently performing arithmetic processing on the device trusted state credential and the interaction response pattern to convert the device trusted state credential and the interaction response pattern into a constraint polynomial having a unique corresponding relationship respectively; S1032, establishing a public constraint set for coupling the constraint polynomial, the public constraint set containing a group of preset algebraic equations describing a judgment condition that the identity authenticity and the physical proximity of the data requesting device must be simultaneously true without revealing the root key and the real-time running state data; S1032 can specifically include the following processes: setting independent judgment conditions for the constraint polynomial respectively, the judgment conditions requiring that when the inputs are valid, the result of the constraint polynomial after a specific operation is equal to a preset zero value; constructing a coupling equation that linearly combines the judgment conditions into a single algebraic expression by introducing a random variable, the algebraic expression being equal to the zero value only when the two independent judgment conditions are simultaneously satisfied; and compiling the coupling equation and all auxiliary constraint relationships required to satisfy the coupling equation into a structured public constraint set, using the public constraint set as a unified computing framework for generating the composite zero-knowledge proof subsequently.

[0040] S1033, calling the public constraint set by the data requesting device and using the constraint polynomial as a private witness to perform a localized proof generation calculation based on the judgment conditions, and outputting a publicly verifiable composite zero-knowledge proof.

[0041] ​In the above scheme, the device trust status credential is an encrypted representation of the device identity and real-time running status, ensuring its authenticity and integrity. The interaction response pattern is a unique signal feature generated by the device in a specific physical interaction, used to verify physical proximity. The composite zero-knowledge proof is an encryption technology that allows one party to prove the authenticity of a certain statement to another party without revealing private information. The constraint polynomial is a mathematical form of the device trust status credential and interaction response pattern, allowing for zero-knowledge proof calculations. The public constraint set is a set of pre-defined algebraic equations that define the conditions under which identity authenticity and physical proximity are simultaneously true, 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.

[0042] In the embodiments of the present application, first, the device trust status credential and the interaction response pattern are independently processed by step S1031. This process is to convert these unstructured or semi-structured data and behavior patterns into mathematical forms that can be understood and processed by the zero-knowledge proof system, i.e., constraint polynomials with unique corresponding relationships. Specifically, for the device trust status credential, it may contain the unique identifier of the device, the configuration information of its internal security module, and the encrypted digest of the current running status. These information will be mapped to a specific polynomial through a pre-defined encoding function, such as a hash function or an encryption commitment scheme. The coefficients or roots of this polynomial will 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 is constructed such that the integer is the evaluation result of the polynomial at a certain specific point. Similarly, for the interaction response pattern, such as the continuous acquisition of capacitance signal sequences from the sensor array, the amplitude, phase, frequency, and other features of these signals will be extracted and converted into another polynomial according to specific rules, such as Fourier transform or wavelet analysis feature vectors. This polynomial can capture the unique "fingerprint" of the physical interaction, such as the rhythm of tapping, sliding gestures, etc. This arithmetic processing is the basis of zero-knowledge proof, which abstracts complex information in the real world into mathematical objects, allowing subsequent proof and verification to be efficiently performed in the pure mathematical field, while ensuring the integrity and privacy of the information.

[0043] 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 needs to satisfy. The core of these equations lies in the fact that they describe the judgment conditions under which the identity authenticity of the data requesting device and the physical proximity must simultaneously hold without revealing the device root key and real-time running state data. For example, an equation may require that the evaluation result of a polynomial representing the device trusted state credential at a certain public point must match a pre-published, encrypted device identity information. Another equation may require that the evaluation result of a polynomial representing the interaction response pattern at another public point must conform to the published characteristics of the expected physical interaction pattern. These equations are connected by logic to ensure that only when both conditions are met will the entire proof be considered valid. This public constraint set is a "rulebook" known to all parties, 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 leakage of any sensitive information.

[0044] Finally, through step S1033, the data requesting device invokes this public constraint set and performs a localized proof generation calculation with its own generated constraint polynomials as private witnesses. This process is the core operation of zero-knowledge proof, and the device uses its private, unpublished polynomials corresponding to the credentials and interaction patterns, combined with the algebraic equations defined in the public constraint set, through complex cryptographic algorithms such as polynomial commitment, homomorphic encryption, or specific zero-knowledge proof protocols such as Groth16 or Plonk, to construct a proof. During the calculation, the device will perform a series of mathematical operations such as polynomial evaluation, polynomial multiplication and addition to prove that its private input satisfies all equations in the public constraint set without revealing the private input itself. Finally, this calculation will produce a compact and publicly verifiable composite zero-knowledge proof. This proof is a short encrypted credential that any verifier with the public constraint set can quickly and efficiently verify its validity, thereby confirming that the data requesting device has both identity authenticity and physical proximity without accessing any sensitive original data.

[0045] In practical applications, imagine a smart home environment where a smart door lock needs to verify a family member's smart bracelet to automatically unlock. First, when the family member wearing the smart bracelet approaches the door lock, the bracelet will perform arithmetic processing on its device trust status certificate, including the bracelet's unique encrypted identity and the current state of its internal security chip, and the specific knock door interaction response pattern captured by the bracelet's built-in capacitive sensor array, such as the unique waveform formed by the frequency, force, and duration of the knock. These information are respectively converted into two unique constraint polynomials, one representing the identity and state of the bracelet, and the other representing its physical interaction behavior.

[0046] Next, the door lock and the bracelet both share a public constraint set in advance. This set contains a set of algebraic equations that collectively define the conditions that the bracelet must satisfy: its identity polynomial must match the registered identity information of the family member after evaluation at a specific public point; at the same time, its interaction pattern polynomial must match the pre-set "knocking" gesture pattern after evaluation at another public point. These equations ensure that only when the bracelet is a legitimate family member device and indeed performs the correct physical interaction can it pass the verification.

[0047] Subsequently, the smart bracelet uses the two private constraint polynomials as secret evidence, combined with the public constraint set provided by the door lock, to perform a localized proof generation calculation in the security chip inside the bracelet. The bracelet will run a zero-knowledge proof algorithm to prove that its private input satisfies all the equations defined in the public constraint set through a series of complex mathematical operations, without revealing the bracelet's encrypted identity or specific knock waveform data to the door lock. This calculation process eventually generates a compact composite zero-knowledge proof.

[0048] Finally, when the bracelet sends this composite zero-knowledge proof to the door lock, the door lock receives the proof without accessing any sensitive raw data of the bracelet or knowing the root key or specific knock pattern of the bracelet, and can verify the proof to confirm that the bracelet is a legitimate family member device and indeed performed the expected physical interaction near the door lock. Once the proof verification is passed, the door lock is safely and automatically unlocked, and the entire process protects user privacy while ensuring the reliability and immediacy of authentication.

[0049] The overall scheme of the above step S103 significantly improves the security and efficiency of temporary networking authentication between Internet of Things devices by ingeniously integrating device identity and physical interaction patterns into the zero-knowledge proof framework. It realizes the simultaneous verification of device identity authenticity and physical proximity immediacy without exposing any sensitive device information, effectively resisting man-in-the-middle attacks and identity forgery, providing a lightweight and powerful mechanism for establishing trust between devices, thereby enhancing the security resilience and privacy protection capabilities of the entire network.

[0050] S104, sending the composite zero-knowledge proof to the data holding device through a wireless channel on the data requesting device, and verifying the validity of the composite zero-knowledge proof independently after receiving the composite zero-knowledge proof to confirm the authenticity and reliability of the physical co-presence state of the other party; Optionally, step S104 can specifically include the following steps: S1041, packaging the composite zero-knowledge proof and a set of public verification keys for verifying the composite zero-knowledge proof to form a data package to be verified through the data requesting device, and transmitting the data package to be verified to the data holding device through a wireless manner; S1042, separating the composite zero-knowledge proof and the public verification keys from the data package to be verified after the data holding device receives the data package to be verified; S1043, performing a deterministic verification operation on the composite zero-knowledge proof through the data holding device using the public verification keys, and determining the authenticity of the composite zero-knowledge proof by checking whether the output result of the verification operation satisfies a preset algebraic identity to confirm the authenticity and reliability of the physical co-presence state of the other party.

[0051] In the above scheme, the composite zero-knowledge proof is a special kind of encryption voucher, which can prove the authenticity of a statement to the verifier without revealing any specific information of the statement itself. The public verification key is a string of public data paired with the composite zero-knowledge proof, which is used to verify the validity of the proof. The data package to be verified refers to a data set formed by packaging the composite zero-knowledge proof and the public verification key, which is prepared for transmission through a wireless manner. The wireless channel is a medium for data transmission, such as Bluetooth, Wi-Fi or cellular network. The algebraic identity is an equation that always holds in mathematics, which is used here to judge the correctness of the zero-knowledge proof verification result.

[0052] In the embodiments of the present application, first, through step S1041, the data requesting device encapsulates its previously generated composite zero-knowledge proof with a set of public verification keys for verifying the proof. This encapsulation process is to package the proof itself and the public information required to verify it into a unified data package to be verified. For example, the data requesting device will use a standard data encapsulation protocol to take the composite zero-knowledge proof as the data payload and the public verification key as the metadata or header information of the data package, ensuring that the two are closely associated and can be correctly parsed by the recipient. This data package to be verified is then transmitted to the data holding device in an encrypted or non-encrypted form through wireless means, such as using Bluetooth Low Energy or Wi-Fi direct technology. During transmission, the data package may be channel coded and modulated to adapt to the characteristics of the wireless environment, ensuring the reliability of data transmission. For example, after generating the composite zero-knowledge proof, a smart bracelet will package it together with the corresponding public verification key and broadcast it to the nearby smart door lock through Bluetooth.

[0053] Subsequently, through step S1042, after receiving the data package to be verified, the data holding device will parse it and separate the composite zero-knowledge proof and the public verification key from it. This separation process is the operation of the receiver to unpack the data package. For example, the data holding device will identify and extract each component in the data package according to the pre-set data package format. It will first parse the data package header to obtain the public verification key, and then extract the composite zero-knowledge proof from the data payload. This step ensures that the data holding device can obtain all the necessary information required for verification, preparing for the subsequent proof verification operation. For example, after receiving the Bluetooth data package sent by the smart bracelet, the smart door lock will parse the data package and extract the composite zero-knowledge proof and the public verification key from it.

[0054] Finally, through step S1043, the data holding device uses the separated 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, which is a purely mathematical calculation process and does not involve any leakage of private information. The data holding device will run a pre-set verification algorithm corresponding to the proof generation algorithm, taking the composite zero-knowledge proof and the public verification key as input. The verification algorithm will perform a series of complex algebraic operations and check whether the output result satisfies the pre-set algebraic identities. If the result of the verification operation satisfies these identities, it indicates that the composite zero-knowledge proof is authentic and valid, thereby confirming the authenticity of the identity of the data requesting device and the authenticity and reliability of the physical co-presence state; otherwise, if the identities do not hold, the proof is invalid. For example, the smart door lock uses the extracted public verification key to perform verification calculation on the composite zero-knowledge proof sent by the smart bracelet. If the calculation result satisfies a specific algebraic equation, the door lock confirms that the bracelet is legitimate and is indeed nearby.

[0055] In practical applications, a smart sensor (data requesting device) needs to prove its identity legitimacy and physical proximity to a nearby smart gateway (data holding device) in order to upload environmental data. First, after completing the generation of the composite zero-knowledge proof, the smart sensor will encapsulate it with a pre-set public verification key to form a pending verification data package. This data package is sent to the smart gateway through the wireless module. This encapsulation process ensures that the information required for proof and verification can be transmitted as a whole.

[0056] Next, after receiving the pending verification data package, the smart gateway will immediately analyze it. The gateway's communication module will accurately separate the composite zero-knowledge proof and the public verification key from the received data stream according to the predetermined protocol format. This separation operation is automatic and efficient, ensuring that the gateway can obtain all necessary components for the next step of verification without human intervention or additional queries.

[0057] Subsequently, the smart gateway uses the separated public verification key to perform a strict deterministic verification operation 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 the pre-set algebraic identities, which are inherent mathematical properties of the zero-knowledge proof system and directly determine the validity of the proof.

[0058] Finally, by checking the output result of the verification operation, the smart gateway can determine the authenticity of the composite zero-knowledge proof. If the verification is successful, the gateway can be sure that the smart sensor is an authenticated legitimate device and is indeed within its physical sensing range. This process achieves reliable confirmation of the sensor's identity and physical coexistence state without revealing any sensitive identity information or original measurement data of the sensor, allowing the sensor to safely begin transmitting environmental data to the gateway.

[0059] The overall scheme of the above step S104 achieves fast, privacy-protected confirmation of the identity and physical coexistence state of the data requesting device through efficient wireless transmission and localized zero-knowledge proof verification mechanisms. It avoids the delay and single-point failure risk of centralized verification, ensuring the immediacy and reliability of temporary trusted connections between devices, while maximizing the protection of sensitive information of devices and enhancing the security and autonomy of self-organizing networks in the Internet of Things.

[0060] S105, after the composite zero-knowledge proof is verified, the data access permission of the data request device is authorized by the data holding device in real time, so as to establish a temporary, only for successful verification interaction, end-to-end encrypted data transmission channel between the data holding device and the data request device.

[0061] Optionally, step S105 can specifically include the following steps: S1051, once the composite zero-knowledge proof is confirmed as valid by the data holding device, a temporary session ticket with high randomness is immediately generated locally by the data holding device; S1052, the temporary session ticket is encrypted by the data holding device using the public key broadcasted by the data request device in the initial stage of interaction, and the encrypted temporary session ticket is returned to the data request device; S1053, the data request device and the data holding device use the shared temporary session ticket to calculate the same session key independently through a preset key agreement protocol, and use the session key to establish an end-to-end encrypted data transmission channel.

[0062] In the above scheme, the temporary session ticket is a string of random data generated by the data holding device, which is used to identify and authorize the data request device within a short time. The public key is the encryption key disclosed by the data request device at the beginning of communication, which is used to encrypt information, and only the corresponding private key can decrypt it. The key agreement protocol is a secure communication mechanism that allows both parties to generate a shared key that only they know without directly exchanging the key. The session key is a temporary encryption key generated according to the key agreement protocol, which is used to encrypt and decrypt all data in this session. The end-to-end encrypted data transmission channel means that the data is in an encrypted state throughout the transmission process from the sender to the receiver, ensuring that only the communication parties can read the data content, and the intermediate link cannot eavesdrop or tamper.

[0063] In the embodiment of the application, first, through step S1051, once the data holding device successfully verifies the composite zero-knowledge proof sent by the data request device and confirms the legitimacy of its identity and physical location, the data holding device will immediately generate a brand new, highly random temporary session ticket in itself. This ticket is like a one-time pass, its randomness ensures the uniqueness and security of each connection, for example, a smart door lock will immediately generate a random number string as a temporary session ticket after confirming the validity of the zero-knowledge proof of the smart bracelet.

[0064] Subsequently, the data holding device encrypts the newly generated temporary session credential with the public key broadcasted by the data requesting device at the beginning of the communication, through step S1052. This encryption ensures that only the data requesting device with the corresponding private key can successfully decrypt and obtain the credential, thus preventing the credential from being stolen or tampered with by a third party during transmission. For example, the smart door lock encrypts the previously generated random number string with the public key of the smart bracelet, and then sends the encrypted number string back to the smart bracelet.

[0065] Finally, after receiving the encrypted temporary session credential, the data requesting device decrypts it with its own private key to obtain the original temporary session credential, through step S1053. At this point, both the data requesting device and the data holding device have the same temporary session credential. Then, both parties use this shared temporary session credential to independently calculate a same session key through a pre-configured key agreement protocol, such as the Diffie-Hellman key exchange protocol. This session key is the secret key for this communication, known only to both 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, and all subsequent data exchanges will be conducted through this encrypted channel, ensuring the confidentiality and integrity of the communication content. For example, after the smart bracelet decrypts the random number string, it uses the number string together with the smart door lock to calculate a shared session key through a key agreement algorithm, and then uses this key to establish a secure communication channel for subsequent unlocking instruction transmission.

[0066] In practical applications, consider a smart home scenario where a smart speaker serves as the data holding device and a smart phone serves as the data requesting device. After the smart phone proves its legal identity and physical proximity to the smart speaker through zero-knowledge proof, the smart speaker immediately generates a temporary session credential, such as a random hexadecimal string.

[0067] The smart speaker then encrypts the credential with the public key previously broadcasted by the smart phone, and sends the encrypted credential to the smart phone. After receiving the encrypted credential, the smart phone decrypts it with its own private key to obtain the original temporary session credential. At this point, both the smart speaker and the smart phone have the same temporary session credential.

[0068] Next, both parties use this shared credential to independently compute a same session key through a pre-set key agreement protocol. For example, both parties 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 or tampered with. This channel is temporary and only valid for this successful verification interaction, ensuring the security and efficiency of communication.

[0069] The overall scheme of step S105 significantly improves the security and efficiency of temporary connections between Internet of Things devices by establishing an encrypted communication channel immediately after successful zero-knowledge proof verification. It ensures the confidentiality and integrity of data transmission, effectively resisting man-in-the-middle attacks and data eavesdropping, while avoiding dependence on centralized servers, enabling autonomous secure communication between devices, thereby enhancing the flexibility and reliability of the entire Internet of Things ecosystem.

[0070] The following is a complete embodiment for steps S101-S105: Assuming in the scenario of temporary trusted networking and data exchange of Internet of Things, a smart environmental sensor needs to establish a trusted connection with a smart air purifier and exchange data. Through step S101, the smart environmental sensor generates a root key representing its unique hardware identity using the physical unclonable function formed by its chip during the manufacturing process. At the same time, the sensor collects real-time operating state data such as battery level, firmware version, operating temperature, etc. Subsequently, the sensor cryptographically binds the root key with real-time operating state data to form a device trust state credential, which can prove the authenticity of the sensor's identity and the integrity of its current operating state.

[0071] Through step S102, the capacitive proximity sensor array embedded in the smart environmental sensor's shell is driven to actively detect the coupling capacitance 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 these coupling capacitance field disturbance data and converts them into an interaction response pattern that describes the physical interaction process. This pattern is a unique fingerprint of the physical proximity and specific form matching between devices.

[0072] By step S103, the smart environment sensor uses the previously generated device trust status credential and the newly obtained interaction response pattern as private inputs to generate a composite zero-knowledge proof. The uniqueness of this proof is that it can simultaneously assert the identity authenticity of the sensor and its physical proximity in real time without directly leaking the sensor root key and real-time running state data. This means that the air purifier can confirm the legitimacy of the sensor and whether it is currently in close contact without obtaining any sensitive information.

[0073] By step S104, the smart environment sensor sends the generated composite zero-knowledge proof to the smart air purifier through the low-power Bluetooth wireless channel. After receiving the proof, the smart air purifier independently verifies the validity of the composite zero-knowledge proof using the pre-set public verification parameters. This verification process is deterministic and aims to confirm whether the proof satisfies a specific algebraic identity. Once the verification is passed, the air purifier can confirm the identity authenticity of the sensor and its physical coexistence state, laying the foundation for subsequent data exchange.

[0074] By step S105, after the composite zero-knowledge proof passes the local verification of the smart air purifier, the air purifier immediately authorizes the data access rights of the smart environment sensor. Subsequently, the two devices independently calculate the same session key using the shared temporary session credential and the pre-set key negotiation protocol. This session key is then used to establish a temporary, end-to-end encrypted data transmission channel between the smart environment sensor and the smart air purifier, which is only limited to this successful verification interaction. This channel ensures the confidentiality, integrity, and non-repudiation of subsequent environmental data (such as air quality index, temperature, humidity) during transmission, ensuring secure communication even in an open wireless environment.

[0075] The data access control method based on zero-knowledge proof provided in the present application combines device inherent hardware identity, real-time running state, and physical proximity perception to construct a multi-dimensional trust credential. It uses zero-knowledge proof technology to quickly confirm device identity and physical coexistence state without leaking sensitive information. This enables devices to establish temporary trusted connections autonomously, effectively resisting man-in-the-middle attacks and device forgery, significantly improving the security, flexibility, and immediacy of data exchange in self-organizing networks of the Internet of Things, without relying on centralized servers for authentication, thereby reducing system complexity and potential single point of failure risks, providing a solid foundation for direct and secure interaction between Internet of Things devices.

[0076] Figure 4 A specific implementation structure diagram of a data access control system based on zero-knowledge proof provided by an embodiment of the present application is shown in Figure 4 The system can include: The binding module 41 is configured to, in a scenario where the data request device needs to perform trusted interaction with the data holding device, generate a root key representing a unique hardware identity by using a physically unclonable function formed in a chip manufacturing process of the data request device, and synchronously collect real-time running state data of the data request device, perform cryptographic binding between the root key and the real-time running state data, and construct a device trusted state credential. The processing module 42 is configured to drive a capacitive proximity sensing array embedded in a shell of the data request device, actively detect a coupling capacitance field disturbance caused by the data holding device approaching, which can reflect a specific physical profile and dielectric constant, and process the coupling capacitance field disturbance into an interaction response mode describing a physical interaction process. The generating module 43 is configured to use the device trusted state credential and the interaction response mode as private inputs to generate a composite zero-knowledge proof, and use the composite zero-knowledge proof to simultaneously assert the identity authenticity of the data request device and the immediacy of physical proximity to the data holding device without revealing the root key and the real-time running state data. The verification module 44 is configured to send the composite zero-knowledge proof to the data holding device through a wireless channel on the data request device, and perform independent verification on the validity of the composite zero-knowledge proof locally after the data holding device receives the composite zero-knowledge proof, to confirm the authenticity and reliability of the identity and physical coexistence state of the other party. The establishing module 45 is configured to, after the composite zero-knowledge proof is verified, authorize data access permission of the data request device by the data holding device in real time, so as to establish a temporary, only-for-successful-verification-interaction, end-to-end encrypted data transmission channel between the data holding device and the data request device.

[0077] The zero-knowledge proof-based data access control system according to the embodiments of the present application is used to implement the foregoing zero-knowledge proof-based data access control method, and therefore the specific implementation of the zero-knowledge proof-based data access control system can be seen from the foregoing embodiment part of the zero-knowledge proof-based data access control method, and the specific implementation can be referred to the description of the corresponding embodiment part, which will not be described herein again.

[0078] The present application also provides an electronic device, comprising a memory configured to store a computer program, and a processor configured to execute the computer program to implement the steps of the zero-knowledge proof-based data access control method.

[0079] The application further provides a computer readable storage medium, and the computer readable storage medium stores a computer program.

[0080] In an example embodiment, the computer readable storage medium can include, but is not limited to, a U disk, a read-only memory, a random access memory, a mobile hard disk, a magnetic disk or an optical disk, and various media capable of storing a computer program.

[0081] The embodiments of the application further provide a computer program product, and the computer program product includes a computer program, and the computer program is executed by a processor to implement the steps in the embodiments of the zero-knowledge proof based data access control method.

[0082] The skilled person can further realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in the above description in general terms. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0083] The above describes in detail the zero-knowledge proof based data access control method and system provided by the application. The principles and implementation manners of the application are described by using specific examples in this paper, and the above description of the examples is only used to help understand the method of the application and its core idea. It should be pointed out that for ordinary skilled person in the art, without departing from the principles of the application, the application can be improved and modified in several ways, and these improvements and modifications also fall within the protection scope of the application.

Claims

1. A data access control method based on zero-knowledge proof, characterized in that, The application relates to a method for establishing a temporary, end-to-end encrypted data transmission channel between a data request device and a data holding device, comprising the following steps: In a scenario where a data request device needs to interact with a data holding device in a trusted manner, a root key representing a unique hardware identity is generated by using a physically unclonable function formed during the chip manufacturing process of the data request device, real-time running state data of the data request device is synchronously collected, the root key and the real-time running state data are bound by cryptography, and a device trusted state certificate is formed; A capacitive proximity sensing array embedded in the shell of the data request device is driven to actively detect a coupling capacitance field disturbance caused by the data holding device approaching, which can reflect a specific physical profile and dielectric constant, and the coupling capacitance field disturbance is processed into an interaction response mode describing a physical interaction process; The device trusted state certificate and the interaction response mode are used as private inputs to generate a composite zero-knowledge proof, and the identity authenticity of the data request device and the immediacy of physical proximity are simultaneously asserted to the data holding device through the composite zero-knowledge proof without leaking the root key and the real-time running state data; The composite zero-knowledge proof is sent to the data holding device through a wireless channel on the data request device, and after being received 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 identity and physical coexistence state of the other party; After the composite zero-knowledge proof is verified, the data access permission of the data request device is authorized by the data holding device in real time, so that a temporary, end-to-end encrypted data transmission channel is established between the data holding device and the data request device.

2. The method of claim 1, wherein, The device trusted state certificate and the interaction response mode are used as private inputs to generate a composite zero-knowledge proof, and the identity authenticity of the data request device and the immediacy of physical proximity are simultaneously asserted to the data holding device through the composite zero-knowledge proof without leaking the root key and the real-time running state data, comprising: The device trusted state certificate and the interaction response mode are independently processed by arithmetic to convert the device trusted state certificate and the interaction response mode into a constraint polynomial with a unique corresponding relationship; A public constraint set for coupling the constraint polynomial is established, the public constraint set contains a group of preset algebraic equations, and the algebraic equations describe the judgment condition that the identity authenticity and physical proximity of the data request device must be simultaneously established without leaking the root key and the real-time running state data; The public constraint set is called by the data request device, the constraint polynomial is used as a private witness, and a localized proof generation calculation is performed based on the judgment condition to output a publicly verifiable composite zero-knowledge proof.

3. The method of claim 1, wherein, The driving the capacitive proximity sensing array embedded in the data request device shell actively detects the coupling capacitance field disturbance caused by the data holding device approaching, which can reflect the specific physical profile and dielectric constant, and processes the coupling capacitance field disturbance into an interaction response mode describing the physical interaction process, including: Driving each sensing unit in the capacitive proximity sensing array, continuously capturing the original capacitance signal stream generated by the physical proximity of the data holding device in the preset detection period; Performing time-frequency domain transformation on the original capacitance signal stream, extracting amplitude fluctuation information representing the physical profile of the data holding device, and synchronously extracting phase shift information representing the dielectric properties of the data holding device, respectively forming amplitude feature set and phase feature set; Projecting the amplitude feature set and the phase feature set into a preset high-dimensional feature space, generating a vectorized interaction response mode that can uniquely identify a physical contact event by nonlinear fitting the distribution relationship of the amplitude feature set and the phase feature set in the high-dimensional feature space.

4. The method of claim 1, wherein, After the composite zero-knowledge proof is verified, the data request device is authorized by the data holding device for data access, thereby establishing a temporary, only for successful verification interaction, end-to-end encrypted data transmission channel between the data holding device and the data request device, 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 broadcasted by the data request device in the initial stage of interaction to encrypt the temporary session credential, and returns the encrypted temporary session credential to the data request device; Through the data request device and the data holding device using the shared temporary session credential, through the preset key agreement protocol, each independently calculates the same session key, and uses the session key to establish an end-to-end encrypted data transmission channel.

5. The method of claim 1, wherein, The composite zero-knowledge proof is sent to the data holding device through a wireless channel on the data request device, and after the data holding device receives it, the validity of the composite zero-knowledge proof is verified locally to confirm the true and reliable identity and physical coexistence state of the other party, including: 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 package to be verified, and transmits the data package to be verified to the data holding device through wireless means; After the data holding device receives the data package to be verified, the composite zero-knowledge proof and the public verification key are separated from the data package to be verified; The data holding device performs a deterministic verification operation on the composite zero-knowledge proof by using the public verification key, and determines the authenticity of the composite zero-knowledge proof by checking whether the output result of the verification operation satisfies a preset algebraic identity, so as to confirm the authenticity and the physical coexistence state of the identity of the other party.

6. The method of claim 2, wherein, The establishment of a public constraint set for coupling the constraint polynomials, the public constraint set containing a set of preset algebraic equations, the algebraic equations describing the determination condition that the identity authenticity and the physical proximity of the data request device must be simultaneously true without revealing the root key and the real-time running state data, including: An independent determination condition is set for each constraint polynomial, and the determination condition requires that when the input is valid, the result of the constraint polynomial after a specific operation is equal to a preset zero value; A coupling equation is constructed, and the determination conditions are linearly combined into a single algebraic expression by introducing random variables through the coupling equation, and the overall calculation result of the algebraic expression is equal to the zero value only when the two independent determination conditions are simultaneously satisfied; The coupling equation and all auxiliary constraint relationships required to satisfy the coupling equation are collectively compiled into a structured public constraint set, and the public constraint set is used as a unified calculation framework for generating the composite zero-knowledge proof.

7. The method of claim 3, wherein, The amplitude feature set and the phase feature set are projected into a preset high-dimensional feature space, and a vectorized interaction response mode capable of uniquely identifying a physical contact event is generated by nonlinear fitting the distribution relationship between the amplitude feature set and the phase feature set in the high-dimensional feature space, including: A set of preset nonlinear basis functions are used to perform dimension lifting transformation on each data point in the amplitude feature set and the phase feature set, and the data points are mapped into a point cloud distributed in the high-dimensional feature space; In the high-dimensional feature space, the geometric parameters of a hyper-surface are iteratively adjusted so that the hyper-surface can maximize the separation of the point cloud, thereby capturing the internal association structure between the amplitude feature set and the phase feature set; The finally determined geometric parameters are arranged in a predetermined order and solidified into a numerical vector, and the vectorized interaction response mode is formed based on the numerical vector.

8. A data access control system based on zero-knowledge proof, characterized by, 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 request device in a scenario where the data request device needs to perform trusted interaction with the data holding device, and to synchronously collect real-time running state data of the data request device, and to perform cryptographic binding of the root key and the real-time running state data to form a device trusted state certificate. The processing module is used to drive the capacitive proximity sensor array embedded in the shell of the data request device to actively detect the coupling capacitance field disturbance caused by the proximity of the data holding device, which can reflect the specific physical profile and dielectric constant, and process the coupling capacitance field disturbance into an interaction response mode describing the physical interaction process. ​ The generating module is configured to jointly generate a composite zero-knowledge proof by taking the device trust status certificate and the interaction response mode as private inputs, and the composite zero-knowledge proof is used to simultaneously assert the identity authenticity and the instant physical proximity of the data request device without leaking the root key and the real-time running status data. The verifying module is configured to send the composite zero-knowledge proof to the data holding device through a wireless channel on the data request device, and after the data holding device receives the composite zero-knowledge proof, the data holding device independently verifies the validity of the composite zero-knowledge proof locally to confirm the authenticity and reliability of the identity and the physical co-presence state of the other party. The establishing module is configured to instantly authorize the data access permission of the data request device by the data holding device after the composite zero-knowledge proof is verified, so as to establish a temporary, successfully verified interaction limited, and end-to-end encrypted data transmission channel between the data holding device and the data request device.

9. An electronic device, comprising: The computer program is stored in the computer readable storage medium, and the computer program is executed by the processor to implement the steps of the data access control method based on the zero-knowledge proof. The computer program is stored in the computer readable storage medium, and the computer program is executed by the processor to implement the steps of the data access control method based on the zero-knowledge proof. ​ 10. A computer-readable storage medium, characterized in that, ​

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