Trust consensus model construction method, trust computing platform architecture, and network security protection method and system

Abstract

The present invention relates to the field of network security technology, and in particular to a trust consensus model construction method, a trust computing platform architecture, and a network security protection method and system, which collects descriptions of the legal behaviors of each network entity as defined behaviors of each network entity; obtains the self-certification of each network entity's own security in the network operation environment and the judgment of the target network entity's security by other network entities during the interaction between the target network entity and other network entities; based on the self-certification of the network entity's own security and the judgment of its security by other network entities, a consensus algorithm is used to reach a trust consensus on the trustworthiness of the network entity. The present invention can make trust judgments and responses from the perspectives of computing environment trustworthiness, network connection and data communication security, and privacy computing, provide more comprehensive security support, and use trust consensus technology in multiple dimensions to make continuous and dynamic trust judgments on the verified entity based on defined behaviors, with more stable accuracy, and can explore security risks outside the known threat intelligence library.

Description

Technical Field

[0001] The present invention relates to the field of network security technology, and in particular to a trust consensus model construction method, a trusted computing method based on the trust consensus model, a network attack immunity method, a data privacy protection method and a trust computing platform architecture, and to a network security protection method and system based on the trust computing platform architecture. Background Art

[0002] The core goal of the trusted computing platform architecture proposed by the international organization TCG is to ensure the integrity of the platform (firmware, operating system, application). The technical means to achieve this are mainly trusted measurement and trusted verification. Trusted measurement uses the trusted root as the starting point to measure the integrity of the components in the platform boot process in sequence, and trusted verification verifies the measurement results. TPM (Trusted Platform Module) is the core component of the architecture, responsible for providing secure storage for keys, certificates, and measurement results, as well as providing basic cryptographic algorithm support for trusted verification, key and certificate generation and other functions. The active immune trusted computing 3.0 platform architecture proposed by domestic scholars and the Trusted Computing Industry Alliance uses the Trusted Platform Control Module (TPCM) as the trusted root, uses the national standard password protection mechanism, integrates trusted computing technology with access control, can timely identify "self" and "non-self" components, prohibit unauthorized behavior, and prevent known and unknown virus attacks, further ensuring the trusted security of the computing environment. Both TPM and TPCM are based on cryptography and are based on trusted chain construction technology, so that attackers cannot use the defects and vulnerabilities in the system to perform illegal operations on the system, so that the platform computing environment can achieve active immunity to virus attacks.

[0003] In the increasingly complex network computing environment, the above two architectures still have many shortcomings and deficiencies: TPM and TPCM currently focus on solving the unilateral proof of platform security integrity and platform trust. However, the computing process in the actual network environment involves multiple parties, and the trust requirements are multi-dimensional and multi-faceted. It is necessary to comprehensively consider the different security requirements and trust satisfaction of each participant, including the platform's own security proof, security supervision during the computing process, security assurance during network connection and data communication, and privacy protection during data computing. The above two architectures do not propose specific solutions to the above problems. Trust in security is a much more complex judgment and calculation process than the platform's trusted chain, and usually requires the judge to perform trust calculation and judgment based on evidence provided by multiple parties. The above two architectures are only based on the unilateral signature and integrity proof of the platform, which is still insufficient for actual complex network scenarios. Summary of the invention

[0004] To this end, the present invention provides a trust consensus model construction method, a trust computing platform architecture, and a network security protection method and system. Aiming at the security and trust issues of the computing platform in an actual network environment, the present invention takes into account the security concerns and trust needs of different participants such as the platform, network, data, application, and supervision from multiple dimensions such as the trustworthiness of the platform computing environment, network connection and data security, and data privacy computing, so as to more comprehensively ensure the security and credibility of the platform.

[0005] According to the design scheme provided by the present invention, a trust consensus model construction method is provided, comprising:

[0006] Collecting descriptions of legal behaviors of each network entity as defined behaviors of each network entity;

[0007] Obtain the self-certification of the security of each network entity in the network operation environment and the judgment of the security of the target network entity by other network entities during the interaction between the target network entity and other network entities;

[0008] Based on the network entity's self-certification of its own security and the judgment of other network entities on its security, a consensus algorithm is used to reach a trust consensus on the trustworthiness of the network entity.

[0009] As a trust consensus model construction method in the present invention, further, in the process of other network entities determining the security of the target network entity, other network entities are used to observe the network behavior of the current target network entity during the interaction process, and a security determination is made as to whether the behavior of the current network entity is compliant based on the defined behavior of the network entity.

[0010] As the trust consensus model construction method in the present invention, it further includes: updating the defined behaviors corresponding to the untrusted network entity according to the trust consensus result, and limiting the interaction behaviors between the untrusted network entity and other network entities.

[0011] Furthermore, the present invention also provides a trusted computing method based on a trust consensus model, comprising the following contents:

[0012] Taking the computing node integrity summary benchmark as the defined behavior of the computing node, and using the above-mentioned trust consensus model construction method to build a trust consensus model for computing node credibility determination;

[0013] Based on the constructed trust consensus model, the credibility of each computing node in the computing platform is judged, and the trusted computing nodes in the computing platform used for actual environment operation are updated according to the judgment results.

[0014] As a trusted computing method based on a trust consensus model in the present invention, further, the trustworthiness of each computing node in the computing platform is determined based on the trust consensus model, including: in the trust consensus model, the trustworthiness of the computing node is determined by using the integrity self-certification report of the computing node computing boot process and the integrity verification report of other computing nodes in the interaction process on whether the behavior of the computing node is compliant according to the defined behavior to determine the trustworthiness of the computing node, wherein the integrity self-certification report includes identity information verification data and data on whether the integrity summary of the computing node is consistent, and the integrity verification report includes the identity information data of the computing node, the identity information data of other computing nodes in the interaction process, the defined behavior data of the computing node and the data on whether the behavior is compliant.

[0015] Furthermore, the present invention also provides a network attack immunity method based on a trust consensus model, comprising the following contents:

[0016] Taking the legitimate network behavior of the network entity as the defined behavior of the network entity, and using the above-mentioned trust consensus model construction method to construct a trust consensus model for determining the credibility of the network entity behavior;

[0017] By collecting network entity logs and using the trust consensus model to determine the credibility of network entities, the behavior of untrusted network entities is prevented based on the determination results.

[0018] Furthermore, the present invention also provides a data privacy protection method based on a trust consensus model, comprising:

[0019] The technical measures corresponding to the privacy computing level of the participants and the computing behaviors specified in the interactive business process are taken as defined behaviors, and the trust consensus model of the credibility of the multi-party behaviors participating in the interaction of the service platform is constructed using the above-mentioned trust consensus model construction method;

[0020] By collecting the business logs of each participant and using the trust consensus model to determine the credibility of the behavior of each party, untrusted parties are prevented from accessing the data based on the determination results.

[0021] Furthermore, the present invention also provides a trust computing platform architecture, comprising: a trusted computing module for judging the trustworthiness of an entity through a trust consensus model constructed by the above-mentioned trust consensus model construction method, a network attack immunity module and a privacy computing module, wherein:

[0022] The trusted computing module determines the trusted status of computing nodes in the computing environment based on the trust consensus model, and blocks the interaction of untrusted computing nodes based on the determination results;

[0023] The network attack immunity module determines the credibility of network connection entities based on the trust consensus model and prevents untrustworthy entity behaviors based on the determination results;

[0024] The privacy computing module determines the credibility of the participants in the data privacy computing platform based on the trust consensus model, and prevents untrustworthy entities from using data based on the judgment results.

[0025] Furthermore, the present invention also provides a network security protection method based on a trusted computing platform architecture, which is implemented based on the above-mentioned trusted computing platform architecture. The implementation process includes:

[0026] Through network logs and monitoring of the behaviors of each network entity, the self-verification data of each network entity and the external verification data of other network entities on abnormalities in their interactive behaviors are collected;

[0027] For self-certified data and other-certified data, the trust consensus model is used to make trust judgments on the behavior of network entities, and the behavior of untrusted network entities is restricted or prevented based on the trust judgment results.

[0028] Furthermore, the present invention also provides a network security protection system based on a trusted computing platform architecture, which is implemented based on the above-mentioned trusted computing platform architecture and includes: a data collection unit and a trust determination unit, wherein:

[0029] A data collection unit, used to collect self-certification data of each network entity's own integrity and other-certification data of other network entities' judgment of abnormal interaction behavior through network logs and behavior monitoring of each network entity;

[0030] The trust judgment unit is used to make trust judgments on the behavior of network entities based on self-certified data and other-certified data using a trust consensus model, and to restrict or prevent untrusted network entity behavior based on the trust judgment results.

[0031] Beneficial effects of the present invention:

[0032] Compared with the traditional single construction of a trusted computing environment based on a trusted boot chain, the present invention is more in line with the trust construction in actual scenarios; the trust computing platform architecture constructed by it performs trust judgment and response from the perspectives of computing environment trust, network connection and data communication security, and privacy computing, providing more comprehensive security support; and the architecture widely adopts trust consensus technology in multiple dimensions, relying on defined behaviors to perform continuous and dynamic trust judgment on the verified entity, and the trust judgment results are in turn used to update the defined behaviors, and the trust judgment is more accurate and stable; and it can detect security risks outside the known threat intelligence library. The trust computing platform architecture in this case supports a trusted computing environment based on trust consensus, uses self-certification and other certification to build a trusted computing platform, and supports a network attack immunity mechanism based on trust consensus. On the one hand, it protects the network connection and data transmission security of the network entity itself; on the other hand, it conducts continuous and dynamic trust judgments on its network behavior based on trust consensus technology to prevent it from bringing security risks to other network entities; it supports privacy computing based on trust consensus, uses multi-party data rights and security commitments to achieve privacy computing, protects data security during the computing process, and uses trust consensus technology to dynamically and in real time determine whether all parties comply with their security commitments; it can provide a reference for the construction of a trust computing platform and can be widely used in various cloud computing, edge computing, big data, data sharing, cloud-network integration, industrial control, Internet of Things and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a schematic diagram of the trust consensus model principle in the embodiment;

[0034] Figure 2 It is a schematic diagram of a trusted computing environment architecture based on trust consensus in an embodiment;

[0035] Figure 3 This is a schematic diagram of the network attack immunity mechanism architecture based on trust consensus in the embodiment;

[0036] Figure 4 This is a schematic diagram of a privacy computing architecture based on trust consensus in an embodiment;

[0037] Figure 5 This is a schematic diagram of the trusted architecture of the computing module in the embodiment;

[0038] Figure 6 This is a schematic diagram of the implementation principle of the office scene in the embodiment;

[0039] Figure 7 It is an illustration of the implementation principle of the cloud scenario in the embodiment. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present invention clearer and more understandable, the present invention is further described in detail below in conjunction with the accompanying drawings and technical solutions.

[0041] In view of the security and trust issues of computing platforms in actual network environments, an embodiment of the present invention provides a method for building a trust consensus model, including:

[0042] Collecting descriptions of legal behaviors of each network entity as defined behaviors of each network entity;

[0043] Obtain the self-certification of the security of each network entity in the network operation environment and the judgment of the security of the target network entity by other network entities during the interaction between the target network entity and other network entities;

[0044] Based on the network entity's self-certification of its own security and the judgment of other network entities on its security, a consensus algorithm is used to reach a trust consensus on the trustworthiness of the network entity.

[0045] Trust consensus is a comprehensive assessment of the network entity's own security status and its interaction with other entities to determine the entity's trustworthiness. Figure 1 As shown, the defined behavior is a description of the legal behavior that supports the realization of the function of the certified entity. Self-certification is the evidence of the certified entity's own security, and other certification is the judgment of the security of the certified entity by the observing entity that interacts with the certified entity. Behavior within the defined behavior is compliant behavior; behavior outside the defined behavior is non-compliant behavior. In the embodiment of this case, during the judgment process of the security of the target network entity by other network entities, other network entities are used to observe the network behavior of the current target network entity during the interaction process, and a security judgment is made on whether the behavior of the current network entity is compliant based on the defined behavior of the network entity. Furthermore, the defined behavior corresponding to the untrusted network entity can be updated based on the trust consensus result, and the interaction between the untrusted network entity and other network entities can be restricted.

[0046] like Figure 1 As shown in the figure, in the process of trust consensus using the constructed trust consensus model, the observing entity determines whether the behavior of the verified entity is compliant based on the defined behavior during the interaction process, and the judgment result is sent to the consensus algorithm as a foreign certificate. The consensus algorithm determines the credibility of the verified entity based on self-certification and all foreign certificates to establish a trust consensus. Based on the trust consensus, the defined behavior is updated. For example, if the trust consensus of a verified entity is untrustworthy, the defined behavior is updated to restrict its network interaction.

[0047] Furthermore, an embodiment of the present invention also provides a trusted computing method based on a trust consensus model, comprising the following contents:

[0048] Taking the computing node integrity summary benchmark as the defined behavior of the computing node, and using the above-mentioned trust consensus model construction method to build a trust consensus model for computing node credibility determination;

[0049] Based on the constructed trust consensus model, the credibility of each computing node in the computing platform is judged, and the trusted computing nodes in the computing platform used for actual environment operation are updated according to the judgment results.

[0050] A trusted computing environment based on trust consensus is a way to continuously and dynamically build trust in computing nodes based on trust consensus. Figure 2 As shown in the figure, the certified computing node provides the integrity proof of its boot process to prove itself. Other computing nodes judge whether the behavior of the certified computing node is compliant according to the defined behavior during the interaction with the certified computing node. The results of self-certification and other certification are sent to the computing environment trust consensus algorithm in the form of self-certification report and other certification report. The trust consensus judgment result of the certified computing node is obtained through algorithm logic judgment, and the defined behavior is updated according to the result. The trust judgment process is dynamic and continuous.

[0051] Furthermore, the credibility of each computing node in the computing platform is determined based on the trust consensus model, including: in the trust consensus model, the credibility of the computing node is determined by using the integrity self-certification report of the computing node computing boot process and the integrity verification report of other computing nodes in the interaction process on whether the behavior of the computing node is compliant according to the defined behavior to determine the credibility of the computing node, wherein the integrity self-certification report includes identity information verification data and data on whether the integrity summary of the computing node is consistent, and the integrity verification report includes the identity information data of the computing node, the identity information data of other computing nodes in the interaction process, the defined behavior data of the computing node and the compliance data of the behavior.

[0052] The self-certification report includes the integrity proof of the boot process of the certified computing node (including identity information, integrity summary, etc.). The integrity baseline is the integrity summary baseline value of the certified computing node. After the algorithm completes the identity information verification, it compares the integrity summary and the integrity baseline to determine whether they are consistent. The other-certification report includes the identity information of the certified computing node, the identity information of the observing computing node, the behavior description of the certified computing node, the judgment result (whether it is compliant), etc. All observing computing nodes that have business interactions with the certified computing node send other-certification reports to the algorithm.

[0053] Use the integrity proof of the certified computing node and the trust judgment results of all observing computing nodes based on defined behaviors to make continuous and dynamic trust judgments, improve the accuracy, stability and real-time performance of trust judgments, and effectively build a trusted computing environment.

[0054] Furthermore, the embodiment of the present invention also provides a network attack immunity method based on a trust consensus model, which includes the following contents:

[0055] Taking the legitimate network behavior of the network entity as the defined behavior of the network entity, and using the above-mentioned trust consensus model construction method to construct a trust consensus model for determining the credibility of the network entity behavior;

[0056] By collecting network entity logs and using the trust consensus model to determine the credibility of network entities, the behavior of untrusted network entities is prevented based on the determination results.

[0057] The network attack immunity mechanism based on trust consensus is a mechanism that protects the security of physical networks and data transmission and continuously and dynamically builds the trustworthiness of physical network behavior based on trust consensus. Figure 3 As shown in the figure, the certified network entity collects its own network logs and sends them to the network behavior trust consensus algorithm to achieve self-certification. After receiving the logs, the algorithm performs a log audit. The network entity is observed to determine whether the network behavior of the certified network entity conforms to the defined behavior (here, all are network behaviors, so they are also called defined network behaviors) during the interaction process, and the results are sent to the network behavior trust consensus algorithm to achieve other certification. The algorithm determines the trust consensus judgment result of the certified network entity based on the network log and all network behavior judgment results, and updates the defined network behavior based on the results. The trust judgment process is dynamic and continuous.

[0058] Provide network and data transmission security protection for entities by using network attack protection and data transmission protection. In network attack protection, the trust consensus model in this case solution can be used to monitor the entity's network connection based on the defined network behavior, allowing compliant behavior and blocking non-compliant behavior. When the attacker launches an attack, he is blocked because his behavior with the entity is non-compliant. In data transmission protection, cryptographic technologies such as encrypted transmission and signature authentication can be used to protect the encryption, integrity and availability of data and protect data transmission security. And use cryptographic policy management to negotiate and update cryptographic policies such as cryptographic algorithms, algorithm parameters, and keys, and replace the key in time before the end of the key life cycle or when the key is suspected of being attacked, and replace the cryptographic algorithm and algorithm parameters when necessary. Due to the constraints of defined network behavior, entities can only transmit data with specified objects, using specified services and cryptographic policies, further reducing security risks in data transmission.

[0059] Furthermore, an embodiment of the present invention also provides a data privacy protection method based on a trust consensus model, comprising:

[0060] The technical measures corresponding to the privacy computing level of the participants and the computing behaviors specified in the interactive business process are taken as defined behaviors, and the trust consensus model of the credibility of the multi-party behaviors participating in the interaction of the service platform is constructed using the above-mentioned trust consensus model construction method;

[0061] By collecting the business logs of each participant and using the trust consensus model to determine the credibility of the behavior of each party, untrusted parties are prevented from accessing the data based on the determination results.

[0062] Privacy computing based on trust consensus is an implementation of privacy computing. It uses the rights and security commitments of multiple roles to protect data privacy during the computing process, and determines whether a role's security commitment is fulfilled based on all other roles that interact with it. Figure 4 As shown in the figure, there are 8 roles, including data party, regulator, certifier, platform party, computing module, computing service center, service requester and other parties. The trust consensus model architecture is applied to the platform party. The certifier must have the privacy computing certification qualification; the platform party must have a certain privacy computing platform level; the computing module must have a certain privacy computing module level; the computing service center must have the privacy computing service qualification. Data party: an entity that produces or collects data, provides data to a third party, or provides data to a third party on behalf of others. Regulator: a third-party entity trusted by the data party that performs data security monitoring and management. Platform party: an entity that provides a secure computing environment for the computing module and supports it in processing data. Computing module: a software entity that processes the data party's data in the secure computing environment provided by the platform party. Authenticator: an entity that performs privacy computing certification on the computing module. Computing service center: an operating entity that provides computing services. Privacy computing platform level: the security technology level that the platform provided by the certifier meets after the static certification is passed. Privacy computing module level: the security technology level that the computing module provided by the certifier meets after the static certification is passed.

[0063] The goal of privacy computing is to ensure that no entity other than the data party can obtain the original data during the entire computing process; the computing module can only temporarily use the original data when performing calculations, but cannot save the original data or give the original data to other roles. In order to achieve this goal, both the platform and the module party promise to take technical measures in terms of memory security, storage security, network security, behavior monitoring, etc.; the computing service center also promises not to launch malicious attacks. Based on the trust in the regulator and the certifier, the data party ensures that both the platform and the module party are trustworthy, that is, both have taken corresponding technical measures and have not taken actions inconsistent with the goal (trying to obtain, save, or transfer the original data); ensure that the computing service center is trustworthy, that is, no malicious attacks are launched. In order to further limit the computing module's access to the original data, the data party only allows the computing module to use the data when there is a business request.

[0064] Trust consensus-based privacy computing uses a trust consensus model to determine whether each subject is trustworthy, that is, whether the commitment is fulfilled truthfully. In this embodiment, the trustworthiness of the computing module can be used as an example to introduce the implementation of the trust consensus model under this architecture. Figure 5The trusted architecture diagram of the computing module is shown. The defined behavior here refers to the technical measures corresponding to the privacy computing module level owned by the computing module and the computing module behavior specified by the interactive business process of all parties. The computing module sends its own business log as self-certification. The data party, platform party, and computing service center determine whether the behavior of the computing module is compliant based on the business process and privacy computing module level as external certification. The trust consensus algorithm (implemented by the regulator) collects business logs and compliance judgment results to make judgments on the computing module. The judgment results update the business process, such as restricting the use of data by the computing module. Figure 4 In the figure, 0: the data party, the platform party, and the computing module publish themselves as resources to the computing service center, and the computing service center matches them according to the conditions and requirements of the three parties. For example, if the data party provides financial data, it can only match the computing module that processes financial data. 1: The service requester issues a service request. 2: The computing service center allocates services (i.e., the platform party + computing module + service requester) and security policies (cryptographic algorithms, keys, etc.) to the service requester. At the same time, the security policy is synchronized to the computing module; the security policy and service are synchronized to the platform party (to facilitate the platform party to dynamically monitor the network communication behavior between the computing module and the service requester); and the service request is synchronized to the data party (the data party determines that there is a service request based on this, and the computing module can use the data). 3: The service requester requests data from the computing module. 4: The computing module performs calculations and feeds back the calculation results to the service requester.

[0065] Furthermore, an embodiment of the present invention also provides a trust computing platform architecture, comprising: a trusted computing module for judging the trustworthiness of an entity through a trust consensus model constructed by the above-mentioned trust consensus model construction method, a network attack immunity module, and a privacy computing module, wherein:

[0066] The trusted computing module determines the trusted status of computing nodes in the computing environment based on the trust consensus model, and blocks the interaction of untrusted computing nodes based on the determination results;

[0067] The network attack immunity module determines the credibility of network connection entities based on the trust consensus model and prevents untrustworthy entity behaviors based on the determination results;

[0068] The privacy computing module determines the credibility of the participants in the data privacy computing platform based on the trust consensus model, and prevents untrustworthy entities from using data based on the judgment results.

[0069] The trust computing platform architecture is a solution to the security needs of different participants, such as the security proof of the platform itself in the actual network environment, security supervision during data computing, security assurance during network connection and data communication, and privacy protection during data computing. In this case, the embodiment makes a trust judgment based on evidence from multiple parties. Through a trusted computing environment based on trust consensus, a network attack immunity mechanism based on trust consensus, and a set of privacy computing based on consensus, the trust and security issues of the computing platform are comprehensively considered from the perspectives of computing environment security, network security, and privacy computing to meet multi-dimensional trust and security needs. Trust consensus is introduced in building a trusted computing environment, trusted computing platform network behavior, and trusted privacy computing subjects. The trust judgment results of multiple observation entities on the certified subject based on defined behaviors are used as input to make a trust judgment on the certified subject. The minimum set of behaviors required to meet business needs is defined for the entity as the behavior allowed to be executed by the entity, and it is dynamically adjusted according to the results of the trust judgment, so as to identify risks in a timely manner and limit the impact of risks to the smallest possible range.

[0070] Furthermore, an embodiment of the present invention also provides a network security protection method based on a trusted computing platform architecture, which is implemented based on the above-mentioned trusted computing platform architecture. The implementation process includes:

[0071] Through network logs and monitoring of the behaviors of each network entity, the self-verification data of each network entity and the external verification data of other network entities on abnormalities in their interactive behaviors are collected;

[0072] For self-certified data and other-certified data, the trust consensus model is used to make trust judgments on the behavior of network entities, and the behavior of untrusted network entities is restricted or prevented based on the trust judgment results.

[0073] From multiple dimensions such as the trustworthiness of the platform computing environment, network connection and data security, and data privacy computing, taking into account the security concerns and trust needs of different participants such as the platform, network, data, application, and supervision, a multi-party consensus trust model is established, and a trusted computing environment certification based on trust consensus, a network attack immunity mechanism based on trust consensus, and privacy computing based on trust consensus are established. From a more comprehensive perspective, trust in the platform operating environment, trust in network connections, trust in data movement, and trust in data privacy are established. Through mechanisms such as multi-party security certification of the platform computing environment, multi-party security certification in the data computing process, and multi-party security certification in the network connection and data communication process, the security and trustworthiness of the platform are more comprehensively guaranteed.

[0074] Furthermore, an embodiment of the present invention also provides a network security protection system based on a trusted computing platform architecture, which is implemented based on the above-mentioned trusted computing platform architecture and includes: a data collection unit and a trust determination unit, wherein:

[0075] A data collection unit, used to collect self-certification data of each network entity's own integrity and other-certification data of other network entities' judgment of abnormal interaction behavior through network logs and behavior monitoring of each network entity;

[0076] The trust judgment unit is used to make trust judgments on the behavior of network entities based on self-certified data and other-certified data using a trust consensus model, and to restrict or prevent untrusted network entity behavior based on the trust judgment results.

[0077] To explain the effectiveness of this solution, the following is a further explanation based on the application in office scenarios and cloud scenarios:

[0078] The implementation method in the office scenario supports a trusted computing environment based on consensus trust and network attack immunity based on consensus trust, but does not support privacy computing based on consensus trust. It can effectively protect the computing environment security of various servers, computers and other computing platforms in the office network and the network security of the entire office network.

[0079] See also Figure 6 As shown, the self-certification module and the other-certification module are modules that implement self-certification and other-certification respectively in a trusted computing environment based on trust consensus. The network behavior observation module is a module that observes the behavior of the certified network entity and determines whether it is a compliant behavior in the network attack immunity mechanism based on trust consensus. The ZTC client component includes a self-certification module, a other-certification module, a network log collection module, a network attack protection module, a data transmission protection module, and a network behavior observation module. In the trust consensus server, servers for computing environment trust consensus algorithms and network behavior trust consensus algorithms can be deployed. Defined behaviors are maintained by the trust consensus server. The consensus server synchronizes a copy of the defined behavior to the newly connected computing platform, and synchronizes the latest copy of the defined behavior to all computing platforms in the network when the defined behavior is updated. OA / ERP / financial databases can be deployed on OA servers / ERP servers / financial servers, or they can be deployed separately on servers that support ZTC client components.

[0080] Whether it is a computer (internal computer, external computer), or various gateways and servers, they all provide integrity proof to the trust consensus server through the self-certification module. The trust consensus server verifies its integrity proof, and only the computing platform that passes the verification can participate in the office network and apply OA, ERP, financial and other services. The other certification module in each computing platform observes the behavior of other computing platforms in the office network based on the defined behavior, and reports abnormalities to the trust consensus server in a timely manner. The trust consensus server makes trust judgments on all computing platforms in the network based on the self-certification reports and other certification reports sent by different computing platforms. Once an abnormal computing platform is found, the defined behavior is updated immediately to restrict or prohibit its behavior; after the abnormality is eliminated, the defined behavior is updated and its behavior is restored.

[0081] The network log collection module and network behavior observation module send the network logs and network behavior judgment results of each computing platform to the trust consensus server in real time to make trust judgments on the computing platform network behavior, and update the defined network behavior according to the judgment results. The network attack protection module protects the computing platform from network attacks based on the defined network behavior, and only compliant behaviors are allowed. The data transmission protection module protects the secure transmission of various OA data, ERP data and financial data.

[0082] The implementation method in the cloud scenario supports a trust computing environment based on consensus trust, network attack immunity based on consensus trust, and privacy computing based on consensus trust. VPC, for Virtual Private Cloud, is a collection of resources that runs on a public cloud and isolates a portion of public cloud resources for a certain user and provides this user with private use. In the application server cluster, there is a collection of application servers that have deployed ZTC client components. In the database cluster, the cluster of databases used by each application server can be deployed on the application server or separately on a dedicated server that supports ZTC client components. In the computing server cluster, in addition to supporting ZTC client components, there is also a collection of computing platforms that support privacy computing based on trust consensus. The computing server cluster can be set up separately to support privacy computing, or it can be supported on the basis of the existing application server cluster. The former method is recommended.

[0083] The implementation method in the cloud scenario deploys a trusted computing environment based on trust consensus and a network attack immunity mechanism based on trust consensus, which is the same as the implementation method in the office scenario. Here we only introduce the deployment of the privacy computing scenario based on trust consensus. Figure 7 As shown in the figure, the data party, regulator, certifier, platform party, computing module party, and computing service center all have their own VPCs, which are deployed on the cloud; the service requester can be on the cloud or off the cloud. The ZTC client component is deployed on the platform party's VPC server and gateway; the computing server cluster has a certain privacy computing platform level. The computing module provided by the computing module party has a certain privacy computing module level. The certifier has the privacy computing certification qualification. The computing service center has the privacy computing service qualification.

[0084] The data party, platform party and computing module publish their own data, platform and computing module as resources to the computing service center. The computing service center matches them according to the qualifications and matching conditions provided by the data party, platform party and computing module to form a service that can be used by the service requester. The computing module is loaded on the platform, and the data party encrypts the data and transmits it to the computing module, which is saved in the computing platform in ciphertext form. The service requester requests the service from the computing service center, and the computing service center allocates the appropriate platform, computing module and resources to the service requester and accesses them. After the computing module completes the calculation, it returns the result to the service requester.

[0085] Throughout the entire process, various technical measures have been taken by all parties to ensure that the original data of the data party is not obtained by other roles outside the data party, and that the computing module is only used for computing and cannot be saved, transmitted or used for other purposes. Trust consensus technology is used to determine whether all parties have accurately implemented the regulations. The technical measures such as memory security, transmission security, storage security, network security, and behavioral security taken by the platform and the computing module are specified by the privacy computing platform level, statically authenticated by the certifier, dynamically audited by the regulator, and interactive data provided by the data party, the computing module, and the computing service center, to ensure that the platform protects the original data from being leaked from multiple angles.

[0086] Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0087] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0088] The units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person of ordinary skill in the art may use different methods to implement the described functions for each specific application, but such implementation is not considered to be beyond the scope of the present invention.

[0089] Those skilled in the art will appreciate that all or part of the steps in the above method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits, and accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or in the form of software function modules. The present invention is not limited to any specific form of combination of hardware and software.

[0090] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A trusted computing platform architecture, characterized in that: It includes: a trust consensus model construction module, a trusted computing module that judges the trustworthiness of entities through the trust consensus model, a network attack immunity module, and a privacy computing module. The trust consensus model construction module collects the description of the legal behavior of each network entity as the defined behavior of each network entity; obtains the self-certification of each network entity on its own security in the network operation environment and the judgment of other network entities on the security of the target network entity during the interaction between the target network entity and other network entities; based on the self-certification of the network entity's own security and the judgment of other network entities on its security, the consensus algorithm is used to build a trust consensus model; The trusted computing module uses the computing node integrity summary benchmark as the defined behavior of the computing node, determines the trusted state of the computing node in the computing environment based on the trust consensus model, and updates the trusted computing nodes used for actual environment operation in the computing platform according to the determination results and prevents the interaction behavior of untrusted computing nodes; The network attack immunity module regards the legitimate network behavior of the network entity as the defined behavior of the network entity, determines the credibility of the network connection entity based on the trust consensus model, and blocks the behavior of untrustworthy entities based on the determination results; The privacy computing module defines the technical measures corresponding to the privacy computing level of the participants and the computing behaviors specified in the interactive business process as defined behaviors, collects the business logs of each participant, determines the credibility of the participants of the data privacy computing platform based on the trust consensus model, and prevents untrustworthy subjects from using data based on the determination results; The trusted computing module, network attack immunity module and privacy computing module are used to comprehensively consider the trustworthiness of computing platform entities from the dimensions of computing environment security, security in network connection and data communication processes, and privacy protection in data computing processes. Based on the trustworthiness calculation results, the minimum set of behaviors allowed to be executed by entities in the computing platform and required to meet business needs are dynamically adjusted to limit the scope of the impact of trusted risks.

2. The trusted computing platform architecture according to claim 1, characterized in that: In the process of determining the security of the target network entity, other network entities use other network entities to observe the network behavior of the current target network entity during the interaction process, and make a security determination on whether the behavior of the current network entity is compliant based on the defined behavior of the network entity.

3. The trusted computing platform architecture according to claim 1, characterized in that: It also includes: updating the defined behaviors corresponding to the untrusted network entity according to the trust consensus result, and limiting the interaction between the untrusted network entity and other network entities.

4. The trusted computing platform architecture according to claim 1, characterized in that: The credibility of each computing node in the computing platform is determined based on a trust consensus model, including: in the trust consensus model, the credibility of the computing node is determined by using the integrity self-certification report of the computing node computing boot process and the integrity verification report of other computing nodes in the interaction process on whether the behavior of the computing node is compliant according to the defined behavior to determine the credibility of the computing node, wherein the integrity self-certification report includes identity information verification data and data on whether the integrity summary of the computing node is consistent, and the integrity verification report includes the identity information data of the computing node, the identity information data of other computing nodes in the interaction process, the defined behavior data of the computing node and the compliance data of the behavior.

5. A network security protection method based on a trusted computing platform architecture, characterized in that: Based on the trust computing platform architecture described in claim 1, the implementation process includes: Through network logs and monitoring of the behaviors of each network entity, the self-verification data of each network entity and the external verification data of other network entities on abnormalities in their interactive behaviors are collected; For self-certified data and other-certified data, the trust consensus model is used to make trust judgments on the behavior of network entities, and the behavior of untrusted network entities is restricted or prevented based on the trust judgment results.

6. A network security protection system based on a trusted computing platform architecture, characterized in that: The trust computing platform architecture according to claim 1 is implemented, comprising: a data collection unit and a trust determination unit, wherein: A data collection unit, used to collect self-certification data of each network entity's own integrity and other-certification data of other network entities' judgment of abnormal interaction behavior through network logs and behavior monitoring of each network entity; The trust judgment unit is used to make trust judgments on the behavior of network entities based on self-certified data and other-certified data using a trust consensus model, and to restrict or prevent untrusted network entity behavior based on the trust judgment results.

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