Member identity authentication system based on block chain and zero knowledge proof

Abstract

The invention relates to the technical field of information security, in particular to a member identity authentication system based on a block chain and zero knowledge proof, and the system comprises a multi-dimensional trust parameter modeling unit which is used for collecting on-chain state data, client interaction data and ecological environment data; the identity sovereignty credibility evaluation unit is used for receiving the on-chain state data, the client interaction data and the ecological environment data acquired by the multi-dimensional trust parameter modeling unit, and generating a comprehensive credibility index based on the data; the risk early warning unit is used for receiving the comprehensive credibility index and carrying out judgment processing based on the comprehensive credibility index so as to generate a risk early warning signal or a conventional operation signal; the closed-loop feedback adaptive optimization control unit is used for dynamically adjusting preset system parameters in response to the risk early warning signal; according to the invention, the technology span from passive monitoring to active regulation and control is realized, the robustness and user viscosity of the system are improved, and the long-term stability of the system is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information security, in particular to a member identity authentication system based on blockchains and zero-knowledge proofs. BACKGROUND

[0002] In the member identity authentication system based on blockchains and zero-knowledge proofs, the existing technology generally pays excessive attention to apparent performance indicators, while neglecting comprehensive evaluation of the overall credibility of the system, which leads to a value gap between the technical implementation and the core value of the user, and it is difficult to achieve the preset balance among the decentralized characteristics, the security of cryptography, and the user experience in multiple dimensions. Traditional evaluation methods rely on single or one-sided data sources, such as collecting only on-chain state data, lacking multi-dimensional characterization of system state, and existing systems are mostly passive monitoring mechanisms, lacking closed-loop feedback and adaptive optimization capabilities that can convert trust status from passive monitoring to active regulation, which easily leads to the risk of trust collapse due to improper design. Therefore, how to establish a technical framework that can comprehensively evaluate and dynamically optimize the overall credibility of the system to effectively fill the value gap and prevent the risk of trust collapse is a problem that needs to be solved by those skilled in the art. SUMMARY

[0003] To solve the above technical problems, the present application provides a member identity authentication system based on blockchains and zero-knowledge proofs, specifically, the technical solution of the present application is: The member identity authentication system based on blockchains and zero-knowledge proofs comprises: A multi-dimensional trust parameter modeling unit is used to collect on-chain state data, client interaction data, and ecological environment data. An identity sovereignty credibility evaluation unit is used to receive the on-chain state data, client interaction data, and ecological environment data collected by the multi-dimensional trust parameter modeling unit, and generate an overall credibility index based on the data. A risk warning unit is used to receive the overall credibility index and perform discriminant processing based on the overall credibility index to generate a risk warning signal or a regular operation signal. A closed-loop feedback adaptive optimization control unit is used to dynamically adjust the preset system parameters in response to the risk warning signal.

[0004] Preferably, the specific steps of the multi-dimensional trust parameter modeling unit are as follows: Collect the total number of active validation nodes of the blockchain network and the distribution of the staking amount as part of the on-chain state data. Collect the time required for the user device to generate a zero-knowledge proof and the number of interaction steps to complete the authentication process as client interaction data. The aggregation of developer community activity metrics and average response time for security vulnerabilities on code hosting platforms serves as ecosystem data.

[0005] Preferably, the specific steps of the identity sovereignty trustworthiness assessment unit are as follows: Decentralization index is determined based on on-chain state data; Determining the ecological resilience index based on ecological and environmental data; Cryptographic security scores are determined based on on-chain state data and the ecological resilience index. Determine the user interaction friction index based on client interaction data; A comprehensive credibility index is generated by combining the decentralization index, cryptographic security score, and user interaction friction index.

[0006] Preferably, the process for determining the decentralization index is as follows: Obtain the total number of active verification nodes collected by the multidimensional trust parameter modeling unit; The Gini coefficient is calculated based on the distribution of pledged equity collected by the multidimensional trust parameter modeling unit. A decentralization index is generated by combining the total number of active validator nodes with the Gini coefficient.

[0007] Preferably, the process for determining the ecological resilience index is as follows: Obtain developer community activity and average response time for security vulnerabilities collected by the multidimensional trust parameter modeling unit; Obtain the default system security vulnerability bounty program coverage; An ecosystem resilience index is generated by combining developer community activity, average response time for security vulnerabilities, and coverage of system security vulnerability bounty programs.

[0008] Preferably, the process for determining the cryptographic security score is as follows: Obtain the theoretical security foundation score of the preset core cryptographic protocol; Obtain the audit status of on-chain smart contracts collected by the multi-dimensional trust parameter modeling unit; A cryptographic security score is generated by combining the theoretical security baseline score, audit status, and ecological resilience index.

[0009] Preferably, the process for determining the user interaction friction index is as follows: Obtain the average time to generate zero-knowledge proofs and the number of interaction steps required to complete authentication, collected by the multidimensional trust parameter modeling unit; Obtain the preset cognitive load constant corresponding to the user key management scheme; By combining the average time to generate zero-knowledge proofs, the number of interaction steps, and the cognitive load constant, a user interaction friction index is generated.

[0010] Preferably, the specific steps of the risk warning unit are as follows: Obtain the overall credibility index; The rate of change is obtained by calculating the first difference based on the comprehensive credibility index; The comprehensive credibility index is compared and analyzed with the preset early warning threshold, and the rate of change is compared and analyzed with the preset negative threshold of the rate of change. A risk warning signal is generated when the overall credibility index is lower than the preset warning threshold or the rate of change is lower than the preset negative threshold of the rate of change; otherwise, a normal operation signal is generated.

[0011] Preferably, the process for determining the preset system parameters through dynamic adjustment is as follows: Determine whether the overall credibility index is lower than the preset safety threshold; When the overall credibility index is lower than the preset safety threshold, the deviation between the overall credibility index and the preset safety threshold is calculated. The system's preset baseline parameters are then adjusted based on the deviation values ​​to obtain the adjusted system parameters.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. This system constructs a three-dimensional data evaluation model, breaking through the limitations of traditional technologies that rely solely on a single performance indicator. It innovatively integrates on-chain state data reflecting the degree of network decentralization, client interaction data reflecting system usability, and ecological environment data characterizing the long-term health of the project. This cross-level, multi-dimensional data collection and modeling provides a comprehensive and realistic portrayal of the system's status, avoids the risk of one-sided evaluation, and lays a solid data foundation for accurate risk warning and adaptive regulation. 2. This system transforms the abstract concept of trustworthiness into a quantifiable and manageable comprehensive indicator. Through the non-linear synthesis of the decentralization index, cryptographic security score, and user interaction friction index, a comprehensive trustworthiness index is generated. This index not only accurately characterizes the network's resistance to censorship and long-term robustness but also objectifies the user's subjective experience, providing an intuitive and unified decision-making basis for the system's status and effectively bridging the gap between technical indicators and core user value. 3. This system adopts an advanced dual-logic risk early warning mechanism, which significantly improves the sensitivity and foresight of risk monitoring. This mechanism abandons the traditional static single threshold judgment and, by simultaneously monitoring the absolute value of the credibility index and its rate of change over time, can not only identify explicit risks, but also capture early signs of a crisis where the trust status is rapidly deteriorating. This design provides a valuable time window for risk avoidance and system intervention, effectively preventing potential trust collapse events and ensuring the stable operation of the system. 4. This system represents a technological leap from passive monitoring to proactive regulation, constructing a closed-loop feedback adaptive optimization control circuit. When the early warning unit issues a signal, the system can automatically adjust core parameters according to the risk level, such as increasing the consensus confirmation number, thereby dynamically balancing security, decentralization, and user experience without human intervention. This self-regulating capability proactively guides the system state to a safe range, greatly improving the system's robustness and user stickiness, and ensuring its long-term stability. This system solves the problem of lack of environmental credibility during member identity authentication by quantifying on-chain state data and ecological environment data. Through dynamic regulation, the system can ensure identity sovereignty security while also taking into account the convenience of authentication. Attached Figure Description

[0013] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0015] Example 1: Please see Figure 1 A membership authentication system based on blockchain and zero-knowledge proofs includes: The multidimensional trust parameter modeling unit is used to collect on-chain state data, client interaction data, and ecological environment data. The Identity Sovereignty Trustworthiness Assessment Unit is used to receive on-chain state data, client interaction data, and ecosystem data collected by the Multidimensional Trust Parameter Modeling Unit, and generate a comprehensive trustworthiness index based on the data. The risk warning unit is used to receive the comprehensive credibility index and perform discrimination processing based on the comprehensive credibility index to generate risk warning signals or normal operation signals; The closed-loop feedback adaptive optimization control unit is used to dynamically adjust preset system parameters in response to risk warning signals.

[0016] It should be noted that the membership authentication of this invention refers to the process by which the system uses zero-knowledge proof technology to verify the validity of the identity credentials held by the user in a blockchain network. This system regards identity authentication as a dynamic credibility determination process. Only when the comprehensive credibility index evaluated by the system meets the standard is the zero-knowledge proof submitted by the user considered valid, thereby completing the confirmation of membership and ensuring the security of identity sovereignty.

[0017] This embodiment provides a membership authentication system based on blockchain and zero-knowledge proofs. The system aims to overcome the value gap problem caused by excessive focus on apparent performance indicators in existing technologies. By establishing a technical framework that can comprehensively evaluate and dynamically optimize the overall credibility of the system, it ensures that the decentralized characteristics, cryptographic security, and user experience of the system achieve a preset balance. The system consists of four core units, forming a closed-loop technology system of data acquisition, evaluation, early warning, and control. The purpose of the multidimensional trust parameter modeling unit is to provide the system with comprehensive, objective, and real-time state data input, avoiding the one-sidedness of traditional evaluation methods that rely on a single performance indicator. In this embodiment, the unit continuously collects on-chain state data, client interaction data, and ecological environment data through distributed probes and external API interfaces to construct a multidimensional system state profile. The purpose of the identity sovereignty credibility assessment unit is to transform the raw data collected by the preceding units, which are of different dimensions and scales, into a single, quantifiable comprehensive assessment index. In this embodiment, the unit receives on-chain state data, client interaction data, and ecosystem data collected by the multi-dimensional trust parameter modeling unit, and generates a comprehensive credibility index based on a multi-dimensional weighted assessment model. This index is used to intuitively reflect the degree of trust guarantee of user identity sovereignty in the system. The risk warning unit aims to continuously monitor the trust status of the system and issue timely alarms when the trust status deteriorates to prevent the risk of trust collapse. In this embodiment, the unit receives a comprehensive credibility index and generates a risk warning signal or a normal operation signal based on the discrimination processing of the absolute value of the index and its rate of change over time. This dual threshold judgment mechanism can effectively capture slow trust erosion and sudden trust crises. The purpose of the closed-loop feedback adaptive optimization control unit is to enable the system to have adaptive adjustment capabilities, transforming trust assessment from passive monitoring to active regulation. In this embodiment, the unit responds to risk warning signals and dynamically adjusts preset system parameters. For example, when a decrease in decentralization is detected, the number of nodes required for transaction confirmation is automatically increased, thereby guiding the system state to a safe range. This invention constructs a dynamic adaptive identity authentication system capable of self-perception, self-evaluation, and self-regulation. Compared with existing technologies, this system can quantify and safeguard the core value of users, effectively bridging the value gap between technical indicators and user value, significantly improving the robustness of the system and user stickiness, and reducing the risk of trust collapse caused by improper design.

[0018] Example 2: The specific steps of the multidimensional trust parameter modeling unit are as follows: Collect the time required for user devices to generate zero-knowledge proofs and the number of interaction steps to complete the authentication process, as client interaction data; The aggregation of developer community activity metrics and average response time for security vulnerabilities on code hosting platforms serves as ecosystem data.

[0019] This embodiment is a concrete implementation of the multidimensional trust parameter modeling unit; the data acquisition process of this unit is designed as three parallel sub-processes to ensure the accuracy and comprehensiveness of subsequent evaluations. Collect the total number of active validator nodes and the distribution of staked equity in the blockchain network as part of the on-chain state data; The total number of active validator nodes refers to the number of nodes that actually participate in block generation and verification during the current consensus cycle; its role is to directly reflect the physical decentralized foundation of the network; it is obtained by calling the standard RPC interface of the blockchain network to query the consensus layer status information in real time. The distribution of staked tokens refers to the statistical distribution of the staked tokens of all validators in the system; its purpose is to assess the degree of concentration of consensus power and prevent a few nodes from controlling the network; its source is the real-time analysis of the internal state data of the on-chain staking contract. The combination of these two elements aims to accurately characterize the degree of decentralization of the network from two dimensions: the number of nodes and the distribution of power. Collect the time required for user devices to generate zero-knowledge proofs and the number of interaction steps to complete the authentication process, as client interaction data; The time required to generate a zero-knowledge proof refers to the computation time consumed by the user's device locally from obtaining the credentials to generating the final proof; its function is to quantify the performance friction in the authentication process; its source is the time recorded by the timer built into the client application before and after the proof is generated. The number of interaction steps to complete the authentication process refers to the total number of clicks, inputs, and other operations that a user needs to actively perform from initiating an authentication request to obtaining the final authentication result; its purpose is to quantify the operational complexity and cognitive burden in the authentication process; its source is the counting of user interaction events by the client application. The combination of these two elements aims to objectively evaluate the smoothness of the user experience from two dimensions: time cost and operational cost. Aggregate developer community activity metrics and average response time for security vulnerabilities on code hosting platforms as ecological environment data; The developer community activity metric is a comprehensive index calculated by weighting data such as the number of code commits, merge requests, and active contributors to the project's core codebase within a specific time window. Its purpose is to evaluate the project's technical iteration capabilities and community health. It is aggregated from code hosting platforms such as GitHub through API interfaces. The average response time for a security vulnerability refers to the average time from when a security vulnerability is reported through official channels to when the development team releases a patch; its purpose is to measure a project's ability to respond to sudden security risks; it is derived from data aggregated from security community reporting platforms or data disclosed by the project itself. The combination of these two aspects aims to assess the project's long-term robustness from two dimensions: its intrinsic vitality and its external security. Compared to traditional approaches that only collect on-chain performance metrics, this embodiment constructs a more comprehensive and multi-dimensional evaluation foundation through cross-level data collection. This multi-dimensional data input ensures that subsequent credibility assessments accurately reflect the true state of the system, avoids the one-sidedness of evaluation dimensions, and provides high-quality data support for achieving accurate risk warnings and dynamic optimization.

[0020] Example 3: The specific steps of the identity sovereignty credibility assessment unit are as follows: Decentralization index is determined based on on-chain state data; Determining the ecological resilience index based on ecological and environmental data; Cryptographic security scores are determined based on on-chain state data and the ecological resilience index. Determine the user interaction friction index based on client interaction data; A comprehensive credibility index is generated by combining the decentralization index, cryptographic security score, and user interaction friction index.

[0021] This embodiment is a concretization of the process by which the identity sovereignty credibility assessment unit generates a comprehensive credibility index. This process follows a bottom-up, progressive logic, gradually abstracting and converging multi-source heterogeneous data to form a single decision basis. The core task of this evaluation unit is to execute a computational process that includes several key steps: The decentralization index is determined based on on-chain state data; the purpose of this step is to transform the abstract concept of decentralization into a computable numerical value that quantifies the system’s ability to resist censorship and single points of failure. The ecological resilience index is determined based on ecological and environmental data; the purpose of this step is to assess the system's survival and development potential in the face of external technological challenges and security threats, reflecting its long-term robustness. The cryptographic security score is determined based on on-chain state data and the ecological resilience index. The purpose of this step is to comprehensively evaluate the security of the system. It not only considers the theoretical strength of the core encryption algorithm, but also innovatively incorporates the engineering implementation quality and the supporting capabilities of the ecological environment. The user interaction friction index is determined based on client interaction data. The purpose of this step is to quantify the time, operation, and cognitive costs perceived by users when using the identity authentication service, which is directly related to user acceptance and retention rate. In summary, on-chain state data determines the decentralization index, reflecting the blockchain network's ability to resist censorship of member identity data; ecological environment data determines the ecological resilience index, reflecting the system's ability to continuously maintain and address security vulnerabilities. If the indices generated by on-chain state data and ecological environment data are low, it means that identity sovereignty faces the risk of centralized control or security attacks. Therefore, the purpose of generating a comprehensive credibility index is to quantify these environmental factors that affect the security of identity sovereignty and use them as a basis for determining whether member identity authentication is trustworthy.

[0022] A comprehensive trustworthiness index T is generated by combining the decentralization index, cryptographic security score, and user interaction friction index using a nonlinear synthesis formula. The formula used to achieve this is as follows: ; in, : Comprehensive credibility index, dimensionless value, calculated in this step; Decentralization index, dimensionless value; Cryptographic security score, dimensionless value; User interaction friction index, a dimensionless value; User interaction friction index The range of the value obtained after normalization is: This multiplication model corrects the original addition-subtraction model and can better reflect the constraint relationship between user interaction friction and the overall reliability of the system. That is, when user friction is extremely high, The overall credibility index approaches 1, regardless of how high other indices are. Both will tend to approach 0, which is more in line with real-world scenarios; Weight coefficients are dimensionless, adjustable parameters. Their initial values ​​can be set using the analytic hierarchy process (AHP) and dynamically optimized using subsequent machine learning models. This optimization process utilizes a historical dataset for supervised training, which contains the system's input features at different historical moments and corresponding supervision labels. For clarity, the supervision labels here, such as user adoption rate and retention rate, are denoted by independent symbols. and The relationship between input features and supervision labels is fitted using a machine learning model, and the weight coefficients are iteratively optimized. The value of is chosen to make the overall credibility index . It can predict real user behavior and system value to the greatest extent possible; to ensure the normalization of the basic weight allocation of the decentralization index and cryptographic security score, it is usually set to... This ensures that the two constitute a complete weight distribution in the core value contribution of the system; This embodiment decomposes a complex, multi-dimensional credibility problem into several independently quantifiable, interrelated sub-problems, and combines them into a single index that is easy to understand and use. This structured evaluation process makes the analysis of system credibility clear and well-documented. It not only quantifies the current state of the system, but also reveals the inherent constraints between different dimensions, providing profound insights for subsequent optimization decisions.

[0023] Example 4: The process for determining the decentralization index is as follows: Obtain the total number of active verification nodes collected by the multidimensional trust parameter modeling unit; The Gini coefficient is calculated based on the distribution of pledged equity collected by the multidimensional trust parameter modeling unit. A decentralization index is generated by combining the total number of active validator nodes with the Gini coefficient.

[0024] This embodiment details the process of determining the decentralization index; its underlying logic lies in drawing on the Gini coefficient in economics and the entropy theory in information theory to comprehensively quantify the network's ability to resist censorship and single points of failure. This determination process obtains the total number of active verification nodes collected by the multidimensional trust parameter modeling unit; this number is denoted as... ; It refers to the total number of active verification nodes in the network. Its function is to serve as a basic measure of the network's degree of dispersion. Its source is on-chain state data collected in real time by the multi-dimensional trust parameter modeling unit. Based on the equity pledge distribution collected by the multidimensional trust parameter modeling unit, the Gini coefficient is calculated; this coefficient is denoted as... ; This refers to the Gini coefficient calculated based on on-chain staking distribution data, with a value range of [missing value]. Its function is to measure the fairness of the distribution of consensus power. The closer it is to 0, the more dispersed the power is. Its source is the data of equity pledge collected by the multidimensional trust parameter modeling unit and calculated by the area under the Lorenz curve method. A decentralization index is generated by combining the total number of active validator nodes with the Gini coefficient. In this embodiment, the following formula is used for calculation: ; in, Decentralization index, a dimensionless value, is calculated in this step; : Total number of active verification nodes, an integer, collected by the multidimensional trust parameter modeling unit; : Gini coefficient of equity pledge amount, a floating-point number between 0 and 1, calculated based on on-chain state data; : Weighting coefficient, a dimensionless adjustable parameter whose value is determined by regression analysis of historical network attack cases, used to balance the relative importance of the number of nodes and the power distribution; The formula's construction takes into account both the dual implications of decentralization: the number of nodes and the quality of power; it is processed using a logarithmic function. This reflects the diminishing marginal utility of the number of nodes; through This item directly incorporates the fairness of power distribution into its considerations; when the number of network nodes... Reduce or concentrate power When it increases, The value will decrease accordingly; this design makes the assessment of the degree of decentralization no longer a vague qualitative judgment, but a precise, dynamic, and quantifiable indicator that can effectively warn of centralization risks.

[0025] Example 5: The process for determining the ecological resilience index is as follows: Obtain developer community activity and average response time for security vulnerabilities collected by the multidimensional trust parameter modeling unit; Obtain the default system security vulnerability bounty program coverage; An ecosystem resilience index is generated by combining developer community activity, average response time for security vulnerabilities, and coverage of system security vulnerability bounty programs.

[0026] This embodiment details the process of determining the ecological resilience index; this index aims to quantify a project's comprehensive ability to cope with external risks and achieve sustainable development, and is an external perspective assessment of system robustness; The developer community activity and average response time for security vulnerabilities are obtained from the multidimensional trust parameter modeling unit; the developer community activity is normalized and denoted as... The average response time for security vulnerabilities is denoted as... ; Obtain the preset system security vulnerability bounty program coverage; this coverage is then normalized and scored as follows. The coverage rate of the system security vulnerability bounty program refers to the comprehensive score of the scope and reward amount of vulnerabilities discovered by a project through public platforms, relative to its codebase complexity and asset size. Its purpose is to incentivize external security researchers to participate in project audits and improve the ability to proactively discover risks. Its source is a preset value set by security experts based on publicly available information. An ecosystem resilience index is generated by combining developer community activity, average response time for security vulnerabilities, and system security vulnerability bounty program coverage. In this embodiment, the following weighted summation formula is used for calculation: ; The correction here will affect the response time item. With the maximum acceptable time Normalization was performed to avoid the dimensional error of directly subtracting dimensionless numbers from time units in the original formula; simultaneously, through... The function ensures that the minimum value of this term is 0, so that when the response time exceeds the maximum acceptable time, it is directly reduced to zero as a penalty term, which enhances the robustness of the model. in, Ecological resilience index, normalized to The dimensionless value is calculated in this step; Normalized developer community activity, a dimensionless value calculated based on ecosystem data; Average response time for security vulnerabilities, in time units, collected by the multidimensional trust parameter modeling unit; Maximum acceptable response time, in units of time, based on a preset benchmark parameter according to industry emergency response standards; The normalized score for the security vulnerability bounty program is a dimensionless value, preset by security experts based on the project requirements. : Item weight coefficients, dimensionless values, and sum to 1, calibrated using industry benchmark data; The calculation logic of this formula integrates three key aspects: the project's intrinsic driving force, emergency response capability, and proactive defense posture; if the vulnerability response time... Exceeding the maximum acceptable time If the value is zero, then the term is 0, constituting a strong penalty term. Through the calculation of this index, the health status and resilience of an open source project ecosystem can be comprehensively and quantitatively assessed, providing a key moderating factor that reflects the level of practice for cryptographic security scoring.

[0027] Example 6: The process for determining cryptographic security scores is as follows: Obtain the theoretical security foundation score of the preset core cryptographic protocol; Obtain the audit status of on-chain smart contracts collected by the multi-dimensional trust parameter modeling unit; A cryptographic security score is generated by combining the theoretical security foundation score, audit status, and ecological resilience index. This embodiment details the process of determining the cryptographic security score; this score aims to combine the security of abstract cryptographic theory, the quality of engineering implementation, and ecological robustness to form a quantifiable comprehensive security score. The theoretical security foundation score of the preset core cryptographic protocol is obtained, denoted as... Theoretical safety foundation This refers to the quantitative assignment of security levels to the core cryptographic algorithms used by the system; its purpose is to provide a theoretical foundation for security assessment; and it is based on security standards published by authoritative organizations such as NIST. Obtain the audit status of on-chain smart contracts collected by the multi-dimensional trust parameter modeling unit and convert it into audit status codes. Audit status code This refers to a quantitative representation of the depth and breadth of the audits conducted by third-party security companies on core smart contracts; for example, 0 represents no audit, 0.5 represents audited by a single agency, and 1 represents audited by multiple agencies and includes formal verification; its purpose is to assess the security of the code at the engineering implementation level; its source is the rating given by security experts based on publicly available audit reports; A cryptographic security score is generated by combining theoretical security baseline score, audit status, and ecological resilience index. The calculation formula is as follows: ; in, Cryptographic security score, a dimensionless value, calculated in this step; Theoretical safety baseline score, dimensionless value, preset according to industry standards; Security audit status code, a floating-point number between 0 and 1, rated according to the audit report; Audit impact factor: a dimensionless, adjustable parameter, an empirical parameter adjusted by security experts based on the depth and quality of the audit report; Ecological resilience index, dimensionless value; Ecological impact weight, a dimensionless adjustable parameter, is used to adjust the impact of ecological robustness on the overall safety score; this parameter specifically quantifies the project's long-term health and risk response capability; the gain or reduction effect on theoretical and engineering safety reflects the efficiency of the transformation from theoretical safety to effective safety in the real world. This assessment is based on the theoretical safety of the core algorithm. Based on the audit status The first revision is made, taking into account the quality of the project completion, and then based on the resilience of the ecosystem. A second weighting is applied to take into account the robustness of the external environment. This progressive evaluation method, from theory to practice and from the kernel to the periphery, ensures that even if a system adopts a high-security algorithm, its security score S will be significantly lowered if there is a lack of auditing, community stagnation, or slow vulnerability response. This provides a more realistic and reliable security profile than a single-dimensional evaluation.

[0028] Example 7: The process for determining the user interaction friction index is as follows: Obtain the average time to generate zero-knowledge proofs and the number of interaction steps required to complete authentication, collected by the multidimensional trust parameter modeling unit; Obtain the preset cognitive load constant corresponding to the user key management scheme; By combining the average time to generate zero-knowledge proofs, the number of interaction steps, and the cognitive load constant, a user interaction friction index is generated.

[0029] This embodiment details the process of determining the user interaction friction index; the index is designed to comprehensively evaluate the usability of the system by quantifying time cost, operational complexity and cognitive burden; The average time to generate zero-knowledge proofs and the number of interaction steps required to complete authentication, collected by the multidimensional trust parameter modeling unit, are denoted as follows: and ; Obtain the preset cognitive load constant corresponding to the user key management scheme, denoted as Cognitive load constant It refers to a constant that quantifies the cognitive difficulty of different key management schemes; its purpose is to evaluate the mental model complexity and error probability required by users when storing and using keys; it is derived from controlled user usability experiments, by measuring the user error rate and task completion time of different schemes and then normalizing them. By combining the average time to generate zero-knowledge proofs, the number of interaction steps, and the cognitive load constant, a user interaction friction index is generated. To ensure dimensional consistency, normalization is used in the calculation, as shown in the following formula: ; in, User interaction friction index, a dimensionless value, is calculated in this step; The average time to generate zero-knowledge proofs, measured in units of time, is obtained from client-side interactive data collection. Baseline time, a time unit, preset based on the performance indicators of similar applications or product design goals; : The number of interaction steps required to complete authentication, an integer, obtained from client interaction data collection; Baseline number of steps, an integer, preset based on the design or product design goals of similar applications; The cognitive load constant of the key management scheme is a dimensionless value, calibrated and preset through user experiments. The weighting coefficients for various friction factors are dimensionless values. They are derived from user experience surveys and behavioral data analysis, reflecting the differences in users' sensitivity to different sources of friction. This formula integrates the three most critical sources of friction in user experience—time cost, operational complexity, and cognitive burden—into a unified quantitative model; by comparing it with a benchmark value... and The comparison makes the evaluation results relative and easy to compare horizontally; this quantitative method allows the subjective feeling of user experience to be objectively measured, providing accurate data support for the system to weigh security and ease of use.

[0030] Example 8: The specific steps of the risk warning unit are as follows: Obtain the overall credibility index; The rate of change is obtained by calculating the first difference based on the comprehensive credibility index; The comprehensive credibility index is compared and analyzed with the preset early warning threshold, and the rate of change is compared and analyzed with the preset negative threshold of the rate of change. A risk warning signal is generated when the overall credibility index is lower than the preset warning threshold or the rate of change is lower than the preset negative threshold of the rate of change; otherwise, a normal operation signal is generated.

[0031] This embodiment is a specific implementation of the risk warning unit's judgment and processing procedure; this unit aims to achieve sensitive and reliable monitoring of the system's trust status; Obtain the overall credibility index This index is calculated and output in real time by the identity sovereignty credibility assessment unit; The rate of change is obtained by calculating the first difference based on the comprehensive credibility index. Rate of change This refers to the rate of change of the comprehensive credibility index T per unit time; its function is to capture the changing trend of trust status; it is derived from the difference calculation of the time series data of T; here, the trust status is the comprehensive credibility index. The system reliability level represented by the index, which satisfies the conditions for a trust state, refers to the comprehensive credibility index. Above the preset warning threshold, and its rate of change The value is not lower than the preset negative threshold for the rate of change, which indicates that the current blockchain network environment and ecosystem of the system are in a stable and secure range, sufficient to support trusted member identity authentication. The system performs a dual-condition comparison analysis; the first comparison is to compare the comprehensive credibility index T with the preset warning threshold. Perform comparative analysis; preset early warning thresholds This refers to a critical value used to determine whether a system has entered a risky state; its function is to define the minimum tolerable level of the system's credibility; it is set based on statistical analysis of the distribution of the historical comprehensive credibility index T of the target decentralized application, and takes its specific quantile as a reference. The second comparison involves the rate of change. Compared with the preset negative threshold value of change Comparative analysis was performed; a negative threshold for the rate of change was preset. This refers to a rate threshold used to determine whether a system's trust is experiencing a rapid collapse; its purpose is to detect sudden trust crises in advance; it is set based on analyzing projects that have experienced major trust crises in the past, and their overall credibility index before the crisis erupted. The rate of descent is used to determine this; A signal is generated based on the comparison results: when the comprehensive credibility index T is lower than the preset warning threshold... or rate of change Below the preset negative threshold of change When the time is right, a risk warning signal is generated; otherwise, a normal operation signal is generated. This embodiment innovatively combines state thresholds and rate of change thresholds in its discrimination logic. By introducing rate of change monitoring, it can trigger high-level warnings in advance when the absolute value of the index is still acceptable but the rate of decline is extremely rapid. This greatly improves the sensitivity and foresight of the warning system, providing a time window for the system to avoid catastrophic risks. (The overall credibility index is also mentioned.) The absolute value can determine whether the system is currently in a low-trust state, thereby identifying explicit environmental deterioration; while the rate of change As a first-order difference, it can determine the rate of decline of the system's trust status; combining the two, it can capture both slow trust erosion through absolute value and sudden trust crises through rate of change, thereby ensuring the security of member identity authentication in various risk scenarios.

[0032] Example 9: The process for determining the preset system parameters through dynamic adjustment is as follows: Determine whether the overall credibility index is lower than the preset safety threshold; When the overall credibility index is lower than the preset safety threshold, the deviation between the overall credibility index and the preset safety threshold is calculated. The system's preset baseline parameters are then adjusted based on the deviation values ​​to obtain the adjusted system parameters.

[0033] This embodiment is a specific example of the dynamic adjustment process of system parameters in a closed-loop feedback adaptive optimization control unit; the process aims to transform early warning signals into actual system control actions, enabling the system to have adaptive risk suppression capabilities; Determine whether the overall credibility index T is lower than the preset safety threshold. Preset safety threshold This refers to the ideal lower bound of system reliability, which is usually higher than the warning threshold. Its function is to define a buffer zone that triggers active regulation; its setting is based on and Similar, but with a higher quantile; When the overall credibility index T is lower than the preset safety threshold At that time, the deviation between the comprehensive credibility index and the preset safety threshold is calculated. This deviation value serves as a positive error signal, and its magnitude is proportional to the degree to which the system deviates from a safe state. The system's preset baseline parameters are adjusted based on the deviation value to obtain the adjusted system parameters. The adjustment logic follows the feedback control formula: ; in, The adjusted system parameters, their dimensions and reference values To maintain consistency, the results calculated in this step are applied to the system; The system's preset baseline parameters, such as the minimum number of consensus node confirmations required to process high-value identity claims, are set according to the initial system design. The current overall credibility index is a dimensionless value provided by the evaluation unit. : Preset safety threshold, dimensionless value, preset; : Control coefficient, a dimensionless adjustable parameter, determined through simulation testing, designed to ensure the speed and stability of the system response; The system's preset parameter safety lower limit, whose dimensions are the same as the benchmark value. Maintain consistency. This parameter is used to ensure consistency even with the system credibility index. Far above the safety threshold In this case, the system's core security parameters It will not be reduced to a dangerous level, thus enhancing the robustness of the closed-loop control system; This adjustment logic is a proportional control feedback loop; when the system reliability... It dropped and fell below the safety line. When, deviation If the value is positive, the system will proportionally increase the security-related parameters. ; The greater the decrease, the greater the increase in parameters; after taking measures... The rebound occurred, the deviation decreased, and the parameter... It also smoothly moves towards the baseline value. This design seamlessly integrates risk warning with system regulation, enabling the system to automatically weigh and optimize multiple objectives such as security, efficiency, and decentralization based on its own credibility status. This effectively suppresses risks and maintains the system's stable operation without human intervention. This dynamic adjustment mechanism forms a complete feedback control closed loop: the adjusted parameters output by the control unit... The results will be adopted by the system execution layer, and their execution effect will be used as a new state. This state will be collected by the multi-dimensional trust parameter modeling unit in the next cycle, thus affecting the comprehensive credibility index in the next round. Calculations; for example, when the adjusted system parameters Once effective, it will alter the on-chain state data and client interaction data subsequently collected by the multi-dimensional trust parameter modeling unit, thereby changing the user interaction friction index. and Decentralization Index The system obtains a comprehensive credibility index through the next round of calculations. This allows for feedback on whether the adjustment has excessively increased user interaction friction while improving security, thereby achieving comprehensive and systematic control over multiple parameters. This ensures that the system can not only respond to risks but also continuously evaluate the overall effectiveness of its own control behavior, avoiding excessive damage to other dimensions in order to improve one indicator.

[0034] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A membership authentication system based on blockchain and zero-knowledge proofs, characterized in that: include: The multidimensional trust parameter modeling unit is used to collect on-chain state data, client interaction data, and ecological environment data. The Identity Sovereignty Trustworthiness Assessment Unit receives on-chain state data, client interaction data, and ecosystem data collected by the Multidimensional Trust Parameter Modeling Unit, and generates a comprehensive trustworthiness index based on the data. The risk warning unit is used to receive the comprehensive credibility index and perform discrimination processing based on the comprehensive credibility index to generate risk warning signals or normal operation signals; The closed-loop feedback adaptive optimization control unit is used to dynamically adjust preset system parameters in response to risk warning signals.

2. The membership authentication system based on blockchain and zero-knowledge proof according to claim 1, characterized in that, The specific steps of the multidimensional trust parameter modeling unit are as follows: Collect the total number of active validator nodes and the distribution of staked equity in the blockchain network as part of the on-chain state data; Collect the time required for user devices to generate zero-knowledge proofs and the number of interaction steps to complete the authentication process, as client interaction data; The aggregation of developer community activity metrics and average response time for security vulnerabilities on code hosting platforms serves as ecosystem data.

3. The membership authentication system based on blockchain and zero-knowledge proof according to claim 1, characterized in that, The specific steps of the identity sovereignty credibility assessment unit are as follows: Decentralization index is determined based on on-chain state data; Determining the ecological resilience index based on ecological and environmental data; Cryptographic security scores are determined based on on-chain state data and the ecological resilience index. Determine the user interaction friction index based on client interaction data; A comprehensive credibility index is generated by combining the decentralization index, cryptographic security score, and user interaction friction index.

4. The membership authentication system based on blockchain and zero-knowledge proof according to claim 3, characterized in that, The process for determining the decentralization index is as follows: Obtain the total number of active verification nodes collected by the multidimensional trust parameter modeling unit; The Gini coefficient is calculated based on the distribution of pledged equity collected by the multidimensional trust parameter modeling unit. A decentralization index is generated by combining the total number of active validator nodes with the Gini coefficient.

5. The membership authentication system based on blockchain and zero-knowledge proof according to claim 3, characterized in that, The process for determining the ecological resilience index is as follows: Obtain developer community activity and average response time for security vulnerabilities collected by the multidimensional trust parameter modeling unit; Obtain the default system security vulnerability bounty program coverage; An ecosystem resilience index is generated by combining developer community activity, average response time for security vulnerabilities, and coverage of system security vulnerability bounty programs.

6. The membership authentication system based on blockchain and zero-knowledge proof according to claim 3, characterized in that, The process for determining the cryptographic security score is as follows: Obtain the theoretical security foundation score of the preset core cryptographic protocol; Obtain the audit status of on-chain smart contracts collected by the multi-dimensional trust parameter modeling unit; A cryptographic security score is generated by combining the theoretical security baseline score, audit status, and ecological resilience index.

7. The membership authentication system based on blockchain and zero-knowledge proof according to claim 3, characterized in that, The process for determining the user interaction friction index is as follows: Obtain the average time to generate zero-knowledge proofs and the number of interaction steps required to complete authentication, collected by the multidimensional trust parameter modeling unit; Obtain the preset cognitive load constant corresponding to the user key management scheme; By combining the average time to generate zero-knowledge proofs, the number of interaction steps, and the cognitive load constant, a user interaction friction index is generated.

8. The membership authentication system based on blockchain and zero-knowledge proof according to claim 1, characterized in that, The specific steps of the risk warning unit are as follows: Obtain the overall credibility index; The rate of change is obtained by calculating the first difference based on the comprehensive credibility index; The comprehensive credibility index is compared and analyzed with the preset early warning threshold, and the rate of change is compared and analyzed with the preset negative threshold of the rate of change. A risk warning signal is generated when the overall credibility index is lower than the preset warning threshold or the rate of change is lower than the preset negative threshold of the rate of change; otherwise, a normal operation signal is generated.

9. The membership authentication system based on blockchain and zero-knowledge proof according to claim 1, characterized in that, The process for determining the preset system parameters through dynamic adjustment is as follows: Determine whether the overall credibility index is lower than the preset safety threshold; When the overall credibility index is lower than the preset safety threshold, the deviation between the overall credibility index and the preset safety threshold is calculated. The system's preset baseline parameters are then adjusted based on the deviation values ​​to obtain the adjusted system parameters.

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