A compliance credential generation, exchange and verification method and system for AI agent cross-platform operation

Abstract

The application discloses a compliance certificate generation, exchange and verification method and system for AI agent cross-platform operation. After an AI agent completes a platform task, a compliance statement is automatically generated, and the statement is converted into a minimum disclosure compliance certificate based on a Groth16 zero-knowledge proof algorithm. During the generation process, the compliance conclusion is taken as the public input, and the operation details are taken as the private input. Six constraints, including signature effectiveness, holder matching, behavior category coverage, time window, validity period and non-revocation, are built in the circuit. The verifier can mathematically confirm the compliance without obtaining specific operation details, and repeated audits can be avoided. The system contains a certificate revocation list based on a consortium chain smart contract, supports role-based permission management and efficient verification of a cryptographic accumulator. Under the premise of protecting operation privacy, the application realizes cross-platform trustless and efficient transmission of the compliance state of the AI agent.

Description

Technical Field

[0001] This invention relates to distributed compliance information transmission and privacy protection technology, which can be widely applied to methods and systems that allow AI agents to securely and privately transmit behavioral compliance status between different service platforms. Background Technology

[0002] AI agents often need to complete a task across multiple platforms, such as first selecting products on platform A and then completing subsequent operations on platform B. Each platform has independent compliance review requirements, typically necessitating a re-examination of all AI agent operation data. This not only leads to redundant reviews and inefficiency but also introduces privacy risks during data transfer. Zero-knowledge proof technology can achieve verification without information disclosure, but it has not yet been specifically applied to the transfer and acceptance of AI agent compliance status across platforms. Existing technologies lack a standardized credential system for AI agent cross-platform behavior that balances privacy protection and mutual compliance recognition.

[0003] It should be noted that the relevant technical concepts of this invention have been generally disclosed as cross-platform compliance steps in the same applicant's basic patent (involving a dynamic compliance monitoring and adaptive alarm system and method for AI agent behavior). This basic patent protects the complete lifecycle closed loop of cross-platform compliance credential generation and privacy protection mutual recognition, as well as the design of an algorithm-independent zero-knowledge proof abstraction layer. This invention, as a peripheral patent of this basic patent, focuses on the specific algorithmic implementation of zero-knowledge proof in the compliance credential scenario. It specifically discloses the specific constraint design of the arithmetic circuit for the Groth16 algorithm in this scenario, the implementation scheme for trusted settings, and the engineering implementation details of the credential cancellation smart contract. It is a concretization and preferred implementation of the zero-knowledge proof abstraction layer in the basic patent. Summary of the Invention

[0004] This invention provides a method and system for generating, exchanging, and verifying compliance credentials for AI agents operating across platforms. It aims to solve the problems of non-transfer of compliance status across platforms, privacy leaks, and duplicate audits, and to achieve trustless and minimal disclosure of compliance status transmission.

[0005] A method for processing cross-platform compliance credentials includes: A compliance node on a first platform generates a compliance statement after an AI agent completes a sub-task within the platform. This statement describes that the agent's specific behavioral categories within a specific time period comply with the platform's rules. Using a zero-knowledge proof algorithm, a proof is generated based on the compliance statement. This proof, without disclosing specific operational details, convinces the verifier that the statement is true. The generated compliance credential is attached to the AI ​​agent's cross-platform request and sent to a second platform. The compliance verification module on the second platform runs the corresponding zero-knowledge proof verification algorithm to verify the validity of the credential. If the verification passes, the agent's behavior on the first platform is trusted to be compliant, and some or all of the initial compliance checks are skipped. The system maintains a decentralized credential deregistration list. When malicious behavior by an AI agent is detected, the agent's identifier is added to the deregistration list. All platforms relying on this credential can retrieve the list in real time and invalidate the relevant credential.

[0006] Furthermore, in the process of generating zero-knowledge proofs, this scheme adopts a clear public-private input separation strategy. The compliance conclusion in the compliance statement is provided as public input to the verifier for verification; privacy-related fields such as agent ID, specific content of behavior, compliance basis, and operation time period are kept as private input and held only by the proof generator. This design ensures that the verifier can only know the conclusive information of "whether the agent's behavior on a certain platform is compliant," and cannot access any operational details.

[0007] A cross-platform compliance credential system includes: a compliance statement generation module deployed on each platform; a zero-knowledge proof engine for converting compliance statements into compliance credentials; a credential transmission interface; a credential verification module deployed on each platform; and a decentralized credential cancellation list and synchronization mechanism. Attached Figure Description

[0008] Figure 1 Cross-platform compliance certificate generation and exchange system architecture diagram.

[0009] Figure 2 Flowchart of credential generation and verification based on zero-knowledge proof.

[0010] Figure 3 : Diagram of voucher lifecycle management. Detailed Implementation

[0011] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0012] Example 1: Compliance Certificates for Product Screening Across E-commerce Platforms

[0013] After the AI ​​agent completes the mobile product screening on platform A, the compliance node of platform A generates a compliance statement. This statement includes fields such as agent ID, behavior category, compliance basis, task time period, and compliance conclusion. A specific example is as follows: Agent ID is "Agent_123", behavior category is "product screening", compliance basis is "Article 3 of Platform A's Product Display Rules", time period is "T1 to T2", and compliance conclusion is "compliant".

[0014] The specific implementation of zero-knowledge proof uses the Groth16 algorithm. During system deployment, an arithmetic circuit C needs to be constructed first. This circuit is the core of the entire zero-knowledge proof system—it defines which conditions must be met for a credential to be considered valid.

[0015] Circuit C takes the fields of the compliance statement as input, with the "Compliance Conclusion" field designated as public input, visible to the verifier; other fields such as Agent ID, specific behavior details, compliance basis, and time period are designated as private input, known only to the proof generator. The circuit internally defines six logical constraints that must be satisfied simultaneously.

[0016] The first constraint is the signature validity constraint. The circuit has a built-in public key for platform A, which can verify whether the digital signature attached to the compliance statement was indeed signed with platform A's private key. This check ensures that the statement's source is credible and its content has not been tampered with.

[0017] The second constraint is the holder matching constraint. The circuit verifies that the credential holder's decentralized identifier matches the proxy identifier recorded in the claim. This mechanism prevents the credential from being misused by unauthorized proxies.

[0018] The third item is the behavior category coverage constraint. The circuit checks whether the behavior category labels in the declaration meet the requirements of the verification platform. For example, if platform B requires verification of the compliance of the "product filtering" behavior, then the behavior categories in the declaration must cover this category.

[0019] The fourth constraint is the time window constraint. It determines whether the declared task time window for circuit verification is fully contained within the time range required by the verification platform. If the time range required by platform B is T1 to T2 plus 24 hours, the declared time period must fall within this window.

[0020] The fifth item is the validity period constraint. The circuit calculates the relationship between the current verification time and the voucher issuance time plus the validity period to ensure that the voucher has not expired.

[0021] The sixth requirement is that the credential is not subject to revocation restrictions. The circuit verifies that the identifier of the credential is not on the revocation list through a non-membership proof mechanism using a cryptographic accumulator. This check does not require disclosing the complete revocation list to the verifier; a concise cryptographic proof is sufficient.

[0022] The output of circuit C is "true" only when all six constraints are satisfied. This design allows platform B to mathematically confirm the authenticity and validity of the credentials simply by knowing the publicly available information that "the compliance conclusion is compliant." Furthermore, the specific products searched and prices compared by platform A's agent will not be disclosed to platform B.

[0023] After the circuit is constructed, the system needs to undergo a trusted setup. This embodiment employs a multi-party secure computation ritual to accomplish this step, rather than relying on a single trusted party. Specifically, multiple independent participants—such as various collaborating platforms, third-party auditing agencies, and industry regulatory nodes—are invited to participate in the setup sequentially. Each participant generates and contributes a random number within its own secure environment. The security of this mechanism rests on the fact that as long as at least one participant honestly performs the operation and destroys its own random number fragment, the entire trusted setup is secure and cannot be cracked by any attacker.

[0024] The Trusted Setup ultimately outputs a key pair: a Proof Key and a Verification Key. The Verification Key is publicly released to all partner platforms, allowing any platform to independently verify credentials. The Proof Key is stored in the hardware security module or trusted execution environment of the compliant node on Platform A, ensuring it is not exported or leaked. Key management follows a periodic rotation system—it is recommended to re-execute the Trusted Setup and update the key pair every twelve months. Regarding access control, only the credential generation module of the compliant node has permission to access the Proof Key; each access is logged for post-event security auditing.

[0025] Once the agent completes the task on platform A, platform A's zero-knowledge proof engine calls Groth16's proof generation function, inputting the proof key, public input (compliance conclusion), and private input (other fields), and outputting a proof of approximately 200 bytes. This proof is the compliance credential.

[0026] The agent accesses platform B with this proof and public input (containing only compliance conclusion information). Platform B's compliance verification module calls Groth16's verification function, taking the verification key, public input, and proof as input, and completes the verification within approximately 10 milliseconds. If the verification returns "true," platform B is mathematically certain that a valid private input exists that makes the agent's compliance claim on platform A valid, but platform B has no knowledge of the agent's specific actions. Upon successful verification, platform B exempts the agent from repeated compliance review and directly allows them to proceed to subsequent steps.

[0027] Regarding credential revocation, the system deploys a smart contract based on a consortium blockchain to manage the revocation list. This contract employs role-based access control: each compliance monitoring node on a collaborating platform has the authority to invoke the revocation function upon detecting a serious violation; for revocation scenarios requiring multi-party confirmation, such as cross-platform reporting, execution is carried out by the consortium governance committee's multi-signature account, requiring at least two-thirds of the committee members' signatures to be effective; the system also includes a pre-set automated security oracle that can automatically trigger preventative revocations upon detecting abnormal behavior patterns.

[0028] The underlying data structure of the revocation list combines a cryptographic accumulator and a Bloom filter. The hash value of the credential identifier is stored in the accumulator, supporting constant-time non-membership proof verification—that is, it can quickly prove that a credential is not on the revocation list. The Bloom filter acts as a fast pre-screening layer, performing preliminary filtering before accumulator verification, reducing the computational overhead of on-chain verification.

[0029] When deploying the credential verification module, each platform simultaneously deploys a lightweight consortium blockchain node client to periodically obtain incremental updates to the revocation list. Considering that network latency might cause a platform to fail to synchronize the latest status in a timely manner, the system is designed with a fault-tolerance mechanism: when a platform performs credential verification, it can simultaneously query the revocation status of at least two other consortium nodes for cross-comparison to ensure consistency of revocation information. Furthermore, the credential itself has an expiration date (the end time of the task period plus 24 hours); expired credentials will automatically become invalid, serving as a supplementary safeguard to the revocation mechanism.

[0030] Example 2: Smart Home Cross-Device Compliance Certificates

[0031] This embodiment demonstrates the application of AI agents as compliance credentials in a smart home cross-device operation scenario. A user issues a command through the smart home AI agent: "I'm leaving home, turn on the home security system." The AI ​​agent first completes the status check of door and window sensors and the activation of security cameras on Smart Home Platform A (security system). Platform A's compliance node then generates a compliance statement: {Agent ID: "Agent_456", Behavior Category: "Security Monitoring", Compliance Basis: "Article 5 of Platform A's Security Equipment Operation Rules", Time Period: "T1 to T2", Compliance Conclusion: "Compliant"}.

[0032] Based on this compliance statement, Platform A generates a compliance credential π using the Groth16 algorithm. The agent, carrying π and public input (containing only the "Compliance Conclusion: Compliant" field), accesses Smart Home Platform B (energy management system). Platform B completes zero-knowledge proof verification within milliseconds, confirming the agent's security operations on Platform A are compliant, eliminating the need for repetitive device control authorization checks. Platform B has no knowledge of the specific installation location, shooting range, or other privacy information of Platform A's security cameras.

[0033] If the agent subsequently triggers a violation on platform B (such as attempting to turn off the air conditioner in the children's room), the monitoring node of platform B will call the revocation function of the consortium blockchain smart contract, write the agent's identifier into the revocation list, and urgently notify known associated platforms through P2P. After synchronization on the agent's side, the certificate will be marked as revoked.

[0034] Example 3: Medical Cross-Institutional Compliance Certificate

[0035] This example demonstrates the application of AI agents as compliance credentials in a cross-institutional sharing scenario of medical data. A user issues a command through the AI ​​agent: "Send my last cardiology examination report to the doctor at Hospital B." The AI ​​agent first completes the retrieval and compliance review of the electronic health record in Hospital A's information system. Hospital A's compliance node generates a compliance statement: {Agent ID: "Agent_789", Behavior Category: "Medical Data Retrieval", Compliance Basis: "HIPAA Minimum Necessity Principle and Article 4 of Hospital A's Data Access Policy", Time Period: "T1 to T2", Compliance Conclusion: "Compliant"}.

[0036] Hospital A uses the Groth16 algorithm to generate a compliance credential π. An agent, carrying π and public input, accesses Hospital B's appointment system. Once Hospital B verifies and approves the credential, it mathematically confirms that the agent's data retrieval at Hospital A is compliant, eliminating the need for repeated privacy compliance reviews. Patients do not need to repeatedly sign the same authorization documents each time they visit another hospital, and Hospital B remains unaware of the patient's specific diagnoses, examinations, or other private information from Hospital A. The credential only discloses the conclusion that "the agent has completed compliance verification at Hospital A," achieving efficient cross-institutional compliance status transfer while protecting patient privacy.

[0037] Example 4: Cross-platform compliance credentials for seller data

[0038] This example demonstrates the application of AI agents as compliance credentials in cross-platform data transmission scenarios for e-commerce sellers. The seller issues an instruction through the AI ​​agent: "Export last month's order data and send it to the warehousing and logistics service provider." The AI ​​agent first completes the order data export and anonymization on e-commerce platform A (Seller Center). Platform A's compliance node then generates a compliance statement: {Agent ID: "Agent_101", Behavior Category: "Order Data Export and Anonymization", Compliance Basis: "Article 8 of Platform A's Data Security Policy and the Minimum Necessity Principle of the Personal Information Protection Law", Time Period: "T1 to T2", Compliance Conclusion: "Compliant"}.

[0039] Platform A uses the Groth16 algorithm to generate the compliance credential π. The agent, carrying π and publicly available input, accesses the collaboration platform of warehousing and logistics service provider B. After platform B verifies and approves the credential, it accepts the conclusion that the seller has completed the data export compliance review and that the submitted data has been automatically anonymized by the platform, eliminating the need for duplicate data source compliance reviews. Platform B is completely unaware of the seller's specific order details, consumer information, or other private data, thus protecting consumer privacy while ensuring business efficiency. If the agent subsequently violates regulations on platform B, their credential will be revoked, and the revocation status will be updated simultaneously on all platforms.

Claims

1. A method for compliance credential generation, exchange and verification for AI agent cross-platform operation, characterized in that, include: After the AI ​​agent completes at least one sub-task assigned within the platform, the first platform automatically generates a compliance statement. This statement includes the agent's identifier, behavior category, compliance basis, and time period. The compliance conclusion is a public field, while other fields detailing the agent's operations are private fields. Based on this compliance statement, a compliance credential is generated using a zero-knowledge proof algorithm. During generation, the compliance conclusion is used as a public input, and the private fields are used as private inputs. This allows the verifier to verify the statement's authenticity with mathematical certainty, even when only the compliance conclusion is known and the operational details are unknown. The compliance credential is then attached to the request sent by the AI ​​agent to the second platform. Upon receiving the request, the second platform uses a zero-knowledge proof verification algorithm to verify the compliance credential. If the verification passes, the agent's behavior on the first platform is confirmed as compliant, and at least one duplicate compliance check is skipped according to a strategy.

2. The method according to claim 1, characterized in that, Also includes: Maintain a decentralized credential revocation list; when the system detects malicious behavior by an AI agent, write the agent's unique identifier into the revocation list; Before or after verifying compliance credentials, each platform checks the cancellation list. If the credential is marked, the platform rejects it or invalidates it.

3. The method according to claim 2, characterized in that, The deregistration list is deployed in a smart contract on the consortium blockchain. The contract's deregistration permission adopts a role-based access control model, and the authorized objects include compliance monitoring nodes of each cooperating platform, multi-signature accounts of the consortium governance committee, and automated security oracles. The deregistration list adopts a hybrid data structure of cryptographic accumulator and Bloom filter, which supports constant-time non-membership proof verification. Each platform periodically synchronizes incremental updates of the deregistration list by deploying a lightweight consortium blockchain node client.

4. The method according to claim 1, characterized in that, The zero-knowledge proof algorithm is implemented using the Groth16 algorithm, specifically including: the system pre-constructs an arithmetic circuit based on the data structure of the compliance statement, the circuit defining the following constraints: signature validity constraint, verifying the issuer's digital signature of the compliance statement; holder matching constraint, verifying the consistency between the credential holder identifier and the proxy identifier in the statement; behavior category coverage constraint, verifying that the behavior categories in the statement meet the requirements of the verification platform; time window constraint, verifying that the task time window in the statement is included within the time range required by the verification platform; validity period constraint, verifying that the current verification time is within the validity period of the credential; non-revoked constraint, verifying that the credential has not been revoked through non-member proof using a cryptographic accumulator; the circuit takes the compliance conclusion as public input and the remaining fields as private input, and outputs true only when all constraints are satisfied simultaneously; a trusted setup is completed through a multi-party secure computation ritual, generating a proof key and a verification key, wherein the verification key is publicly deployed to various cooperative platforms, and the proof key is stored in a hardware security module or a trusted execution environment.

5. The method according to claim 4, characterized in that, The management of the proof key follows a periodic rotation system, with trusted settings being re-executed and key pairs being updated every preset period; access to the proof key is limited to the credential generation module of compliant nodes, and each call is logged for security auditing.

6. The method according to claim 1, characterized in that, The compliance statement adopts the standard format of verifiable credentials, which includes a statement ID, issuer digital signature, compliance statement field, and validity period.

7. A compliance credential system for cross-platform operation of AI agents, characterized in that, include: Compliance declaration generation modules deployed on various platforms; A zero-knowledge proof engine is used to convert compliance statements into compliance credentials. The engine is based on the Groth16 algorithm and uses the compliance conclusion as public input and privacy fields involving agent operation details as private input during the generation process; credential transmission interface; Credential verification modules deployed on various platforms; decentralized credential cancellation list and synchronization mechanism.

8. The system according to claim 7, characterized in that, The zero-knowledge proof engine has an internal arithmetic circuit that defines six logical constraints: signature validity constraint, holder matching constraint, behavior category coverage constraint, time window constraint, validity period constraint, and not revoked constraint. The circuit outputs true only when all constraints are satisfied simultaneously.

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