Christian Nennemann
3c3d7e649f
Each draft gets 2 illustrative figures: - ABVP: architecture components + verification workflow - ATD: example DAG structure + execution state transitions - HITL: primitive framework overview + approval workflow sequence - AEM/PPALP: federated learning architecture + aggregation flow - RARP: cross-domain architecture + two-phase rollback protocol - APAE: layered architecture + cross-domain provenance tracking Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
893 lines
45 KiB
Plaintext
893 lines
45 KiB
Plaintext
Internet-Draft lake
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Intended status: standards-track March 2026
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Expires: September 05, 2026
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Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments
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draft-agent-ecosystem-agent-learning-protocol-00
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Abstract
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Federated learning enables distributed AI agents to
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collaboratively improve models without centralizing training data.
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However, existing federated architectures lack comprehensive
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privacy guarantees for multi-tenant environments where agents from
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different organizations must learn together while preventing data
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leakage. This specification defines the Privacy-Preserving Agent
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Learning Protocol (PPALP) that combines differential privacy,
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secure aggregation, and zero-knowledge proofs to enable federated
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agent learning with formal privacy guarantees. PPALP provides
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mechanisms for privacy-preserving model updates, tenant isolation
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enforcement, and cryptographic verification of privacy compliance.
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The protocol supports heterogeneous agent types while maintaining
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backward compatibility with existing federated learning
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frameworks. This work addresses critical privacy gaps in federated
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agent architectures and provides implementable solutions for
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secure cross-organizational AI collaboration.
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Status of This Memo
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This Internet-Draft is submitted in full conformance with the
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provisions of BCP 78 and BCP 79.
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This document is intended to have standards-track status.
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Distribution of this memo is unlimited.
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Terminology
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The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
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NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED",
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"MAY", and "OPTIONAL" in this document are to be interpreted as
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described in BCP 14 [RFC2119] [RFC8174] when, and only when, they
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appear in all capitals, as shown here.
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Privacy Budget
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A quantitative measure of the privacy cost incurred by
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releasing information about a dataset through differential
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privacy mechanisms
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Tenant Isolation
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The property that agent learning activities of one
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organizational tenant cannot infer private information about
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another tenant's data or models
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Secure Aggregation
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A cryptographic protocol that allows computation of aggregate
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statistics over distributed data without revealing individual
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contributions
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Gradient Inversion Attack
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An attack where an adversary attempts to reconstruct training
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data from shared gradient information in federated learning
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Privacy Compliance Verifier
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A system component that cryptographically verifies adherence to
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differential privacy guarantees and tenant isolation
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requirements
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Federated Coordinator
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The central entity responsible for orchestrating privacy-
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preserving model aggregation across multiple tenants in
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federated learning
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Table of Contents
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1. Introduction ................................................ 3
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2. Terminology ................................................. 4
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3. Problem Statement ........................................... 5
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4. Privacy-Preserving Agent Learning Architecture .............. 6
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5. Differential Privacy for Agent Model Updates ................ 7
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6. Secure Multi-Tenant Federated Aggregation ................... 8
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7. Privacy Compliance and Verification ......................... 9
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8. Protocol Message Formats and Flows .......................... 10
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9. Security Considerations ..................................... 11
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10. IANA Considerations ......................................... 12
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11. References .................................................. 13
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1. Introduction
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The rapid deployment of AI agents across organizational boundaries
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has created an urgent need for collaborative learning mechanisms
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that preserve privacy while enabling knowledge sharing.
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Traditional centralized machine learning approaches require data
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consolidation, which violates privacy regulations and
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organizational policies in multi-tenant environments where
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competing organizations must collaborate. Federated learning
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addresses data centralization concerns but existing protocols lack
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comprehensive privacy guarantees necessary for cross-
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organizational AI agent deployments. Current federated learning
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frameworks are vulnerable to gradient inversion attacks [draft-
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kale-agntcy-federated-privacy], membership inference attacks, and
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cross-tenant data reconstruction, making them unsuitable for
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privacy-sensitive multi-tenant agent learning scenarios.
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Multi-tenant federated agent learning presents unique challenges
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beyond traditional federated learning contexts. AI agents often
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process behavioral data, decision patterns, and strategic
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information that require stronger privacy protections than
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conventional machine learning datasets. The heterogeneous nature
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of agent types across organizations complicates model aggregation
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while maintaining meaningful learning outcomes. Existing privacy-
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preserving techniques such as differential privacy and secure
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aggregation, when applied independently, fail to provide
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comprehensive protection against sophisticated attacks targeting
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federated agent systems. Furthermore, regulatory compliance
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requirements such as GDPR's right to erasure and data processing
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transparency mandates necessitate cryptographic verification
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mechanisms that current federated learning protocols do not
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provide.
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This specification introduces the Privacy-Preserving Agent
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Learning Protocol (PPALP) to address critical privacy gaps in
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federated agent architectures while enabling secure cross-
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organizational AI collaboration. PPALP combines differential
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privacy mechanisms specifically calibrated for agent model
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parameters, secure multi-party computation for cross-tenant
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aggregation, and zero-knowledge proofs for cryptographic
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verification of privacy compliance. The protocol builds upon the
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Privacy-Preserving Reference Architecture defined in [draft-kale-
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agntcy-federated-privacy] and extends federated averaging
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mechanisms to support heterogeneous agent types with formal
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privacy guarantees. PPALP integrates with existing federated
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learning frameworks through standardized APIs while providing
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backward compatibility for legacy deployments.
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The protocol's contribution to AI agent standardization includes
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formal privacy accounting mechanisms that track privacy budget
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consumption across multiple learning rounds, tenant isolation
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enforcement that prevents cross-organizational information
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leakage, and cryptographic audit trails that enable regulatory
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compliance verification. PPALP addresses the critical need for
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standardized privacy-preserving protocols in federated agent
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learning by providing implementable solutions that balance
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collaborative learning benefits with stringent privacy
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requirements. The specification defines concrete message formats,
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communication flows, and security mechanisms necessary for
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production deployment of privacy-preserving federated agent
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learning systems in multi-tenant environments where traditional
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approaches fail to provide adequate protection.
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2. Terminology
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This document defines terminology for privacy-preserving federated
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learning protocols in multi-tenant environments. The key words
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"MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD",
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"SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
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"OPTIONAL" in this document are to be interpreted as described in
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BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
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capitals, as shown here.
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Agent: An autonomous software entity capable of learning and
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adapting its behavior through machine learning mechanisms, as
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defined in [draft-berlinai-vera]. In the context of this
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specification, agents participate in federated learning to improve
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their models collaboratively.
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Federated Agent Learning: A distributed machine learning paradigm
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where multiple AI agents collaboratively train shared models
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without centralizing their training data. Each agent maintains
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local data while contributing to global model improvement through
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privacy-preserving parameter sharing mechanisms.
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Privacy Budget: A quantitative measure of the privacy cost
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incurred by releasing information about a dataset through
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differential privacy mechanisms. The privacy budget, typically
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denoted as epsilon (ε), bounds the maximum privacy loss across all
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learning iterations and MUST be carefully managed to maintain
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formal privacy guarantees throughout the federated learning
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process.
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Differential Privacy: A mathematical framework that provides
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formal privacy guarantees by adding calibrated noise to
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computation outputs. In federated agent learning, differential
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privacy mechanisms ensure that the participation of any individual
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data point cannot be inferred from the released model parameters
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or gradients.
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Tenant: An organizational entity participating in federated
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learning with its own collection of agents, data, and privacy
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requirements. Tenants represent distinct administrative domains
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that require isolation guarantees while participating in
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collaborative learning protocols.
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Tenant Isolation: The property that agent learning activities of
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one organizational tenant cannot infer private information about
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another tenant's data or models. Tenant isolation MUST be
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maintained through cryptographic mechanisms and differential
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privacy guarantees to prevent cross-organizational data leakage.
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Secure Aggregation: A cryptographic protocol that allows
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computation of aggregate statistics over distributed data without
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revealing individual contributions. Secure aggregation protocols
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typically employ techniques such as homomorphic encryption or
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secure multiparty computation to ensure that only the aggregated
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result is revealed to participants.
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Federated Coordinator: The central entity responsible for
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orchestrating privacy-preserving model aggregation across multiple
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tenants in federated learning. The coordinator manages
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communication flows, enforces privacy policies, and coordinates
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the secure aggregation process while maintaining tenant isolation
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guarantees.
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Privacy Compliance Verifier: A system component that
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cryptographically verifies adherence to differential privacy
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guarantees and tenant isolation requirements. The verifier employs
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zero-knowledge proofs and audit mechanisms to ensure that all
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participants comply with established privacy policies without
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revealing sensitive information about their contributions.
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Gradient Inversion Attack: An attack where an adversary attempts
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to reconstruct training data from shared gradient information in
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federated learning. Such attacks exploit the information leakage
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inherent in gradient sharing and represent a primary threat that
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privacy-preserving protocols MUST defend against.
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Membership Inference Attack: An attack technique where an
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adversary attempts to determine whether a specific data point was
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used in training a machine learning model. In federated settings,
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these attacks can be used to infer information about individual
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agents' training data across tenant boundaries.
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Privacy Accounting: The process of tracking and managing privacy
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budget consumption across multiple learning rounds and operations.
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Privacy accounting mechanisms MUST ensure that the cumulative
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privacy loss remains within acceptable bounds throughout the
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entire federated learning process.
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Homomorphic Encryption: A cryptographic technique that allows
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computation on encrypted data without decrypting it first. In the
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context of secure aggregation, homomorphic encryption enables the
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computation of aggregate model parameters while keeping individual
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contributions encrypted.
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Zero-Knowledge Proof: A cryptographic protocol that allows one
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party to prove knowledge of information to another party without
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revealing the information itself. Zero-knowledge proofs are used
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in privacy compliance verification to demonstrate adherence to
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privacy requirements without exposing sensitive details about the
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verification process.
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3. Problem Statement
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Federated agent learning in multi-tenant environments faces
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fundamental privacy leakage risks that existing protocols fail to
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adequately address. The core problem arises when AI agents from
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different organizations (tenants) participate in collaborative
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learning while sharing gradient updates or model parameters that
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inadvertently reveal sensitive information about their private
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training data. Unlike traditional federated learning scenarios
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with homogeneous participants, multi-tenant environments amplify
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privacy risks due to potentially adversarial relationships between
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tenants and the heterogeneous nature of agent architectures across
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organizational boundaries.
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The threat model for privacy-preserving federated agent learning
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encompasses three primary attack vectors that adversaries MAY
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exploit to compromise tenant data privacy. First, gradient
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inversion attacks allow malicious participants to reconstruct
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approximations of private training samples from shared gradient
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information, particularly effective when batch sizes are small or
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when attackers can observe multiple training rounds. Second,
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membership inference attacks enable adversaries to determine
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whether specific data points were used in another tenant's
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training dataset by analyzing model behavior or parameter updates.
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Third, cross-tenant data reconstruction attacks leverage
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correlations between multiple tenants' shared information to infer
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sensitive attributes about datasets belonging to other
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organizational participants, violating tenant isolation
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requirements.
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The privacy leakage problem is quantified through formal privacy
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metrics that MUST account for composition across multiple learning
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rounds and tenant interactions. Let ε represent the privacy budget
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expended per tenant per learning round, and δ represent the
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probability of privacy violation. The cumulative privacy cost
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grows with each parameter sharing event, and without proper
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privacy accounting mechanisms, the total privacy loss (εtotal,
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δtotal) MAY exceed acceptable thresholds defined by organizational
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privacy policies. Traditional differential privacy mechanisms
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designed for single-organization scenarios provide insufficient
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guarantees in multi-tenant federated settings where privacy
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budgets must be allocated across tenant boundaries and learning
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rounds while maintaining utility for collaborative model
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improvement.
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Building upon the AI Agent Structured Threat Model framework
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defined in [draft-berlinai-vera], federated agent learning
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environments face additional risks including compromised privacy
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compliance verifiers that MAY provide false attestations of
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differential privacy adherence, tenant gateway failures that could
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leak cross-tenant information, and coordination attacks where
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multiple tenants collude to circumvent privacy protections. The
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threat model assumes honest-but-curious federated coordinators who
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follow protocol specifications but MAY attempt to infer private
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information from aggregated data, and potentially malicious
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tenants who MAY deviate from prescribed privacy-preserving
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mechanisms to gain competitive advantages through unauthorized
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data access.
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The problem statement also addresses the challenge of maintaining
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privacy guarantees while supporting heterogeneous agent
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architectures with varying computational capabilities, privacy
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requirements, and trust assumptions. Agents operating under
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different privacy regulations (such as GDPR, CCPA, or sector-
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specific requirements) MUST be able to participate in federated
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learning while maintaining compliance with their respective
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privacy obligations. The protocol MUST provide mechanisms for
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cryptographic verification of privacy compliance, audit trails for
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regulatory reporting, and graceful degradation when privacy
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budgets are exhausted, ensuring that collaborative learning can
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continue while respecting individual tenant privacy constraints
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and maintaining the overall utility of the federated learning
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process.
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4. Privacy-Preserving Agent Learning Architecture
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This section defines the Privacy-Preserving Agent Learning
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Protocol (PPALP) architecture, which integrates differential
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privacy, secure aggregation, and zero-knowledge proofs to enable
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federated learning across organizational boundaries while
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maintaining formal privacy guarantees. The architecture extends
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the Privacy-Preserving Reference Architecture framework [draft-
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kale-agntcy-federated-privacy] to specifically address agent
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learning scenarios in multi-tenant environments. PPALP employs a
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layered approach where privacy mechanisms are enforced at multiple
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architectural levels to provide defense-in-depth protection
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against information leakage.
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Tenant A Tenant B Tenant C
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+--------+ +--------+ +--------+
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| Agent | | Agent | | Agent |
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| Local | | Local | | Local |
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| Model | | Model | | Model |
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+---+----+ +---+----+ +---+----+
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v v v
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+---+----+ +---+----+ +---+----+
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| DP | | DP | | DP |
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| Noise | | Noise | | Noise |
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+---+----+ +---+----+ +---+----+
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+-------+-------+-------+------+
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+----+-----+ +----+------+
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| Secure | | Privacy |
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| Aggre- | | Compliance|
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| gation | | Verifier |
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+----------+ +-----------+
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Figure 1: PPALP Federated Learning Architecture
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The PPALP architecture defines four primary component roles that
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collectively ensure privacy-preserving federated learning
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operations. The Federated Coordinator serves as the central
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orchestration entity responsible for managing global model state,
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coordinating aggregation rounds, and maintaining privacy budget
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allocations across all participating tenants. Tenant Gateways act
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as intermediary control plane entities, extending the Agent
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Gateway Control Plane concept [draft-li-dmsc-mcps-agw] to enforce
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tenant-specific privacy policies and manage agent authentication
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within organizational boundaries. Privacy Compliance Verifiers
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operate as independent auditing entities that cryptographically
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verify adherence to differential privacy guarantees and tenant
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isolation requirements through zero-knowledge proofs.
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Participating Agents represent the distributed learning entities
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that contribute model updates while consuming privacy budget
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allocated by their respective tenants.
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The architectural design enforces strict tenant isolation through
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cryptographic boundaries and policy enforcement mechanisms. Each
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tenant maintains an isolated privacy budget that cannot be
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influenced by other tenants' learning activities, ensuring that
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cross-tenant privacy guarantees remain intact regardless of
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adversarial behavior by other participants. Tenant Gateways
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implement the Federated Policy Enforcement framework [draft-cui-
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dmsc-agent-cdi] to harmonize local privacy policies with global
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federated learning requirements while preventing policy conflicts
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that could compromise privacy guarantees. All inter-component
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communications MUST use TLS 1.3 [RFC8446] with mutual
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authentication based on X.509 certificates [RFC5280] to prevent
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network-level attacks.
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The protocol supports heterogeneous agent types through a
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standardized privacy-preserving model update interface that
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abstracts underlying model architectures while ensuring consistent
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privacy accounting. Agents MUST register their learning
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capabilities and privacy requirements with their tenant's gateway,
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which negotiates appropriate differential privacy parameters with
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the Federated Coordinator. The architecture maintains backward
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compatibility with existing federated learning frameworks by
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implementing standard aggregation interfaces that can be secured
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through the PPALP privacy mechanisms without requiring
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modifications to existing agent implementations.
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Privacy accounting occurs at multiple architectural layers to
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provide comprehensive protection against composition attacks and
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privacy budget exhaustion. The Federated Coordinator maintains
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global privacy budget state and implements composition theorems to
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track cumulative privacy expenditure across learning rounds.
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Tenant Gateways enforce local privacy policies and MAY implement
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additional privacy amplification techniques such as subsampling to
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reduce privacy costs for their constituent agents. Privacy
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Compliance Verifiers continuously monitor privacy budget
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consumption and generate cryptographic proofs of compliance that
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can be audited by external parties without revealing sensitive
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learning parameters or model information.
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5. Differential Privacy for Agent Model Updates
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This section specifies the differential privacy mechanisms that
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MUST be applied to agent model parameter updates to provide formal
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privacy guarantees in multi-tenant federated learning
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environments. All participating agents MUST implement differential
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privacy mechanisms that satisfy ε-differential privacy for a pre-
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negotiated privacy parameter ε, where smaller values of ε provide
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stronger privacy guarantees but may reduce model utility. The
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differential privacy implementation MUST use the Gaussian
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mechanism for continuous-valued parameters and the exponential
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mechanism for discrete-valued selections, calibrated to the global
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sensitivity of the learning algorithm as defined in [draft-kale-
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agntcy-federated-privacy].
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Privacy budget allocation MUST be managed across multiple learning
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rounds to prevent privacy depletion over time. Each tenant MUST
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maintain a privacy budget tracker that accounts for the cumulative
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privacy cost across all federated learning rounds, where the total
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privacy cost is bounded by composition theorems. The system MUST
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implement advanced composition using the moments accountant method
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or Rényi differential privacy to achieve tighter privacy bounds
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than basic composition would allow. Privacy budgets SHOULD be
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allocated proportionally to the expected utility gain from each
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learning round, with mechanisms for tenants to reserve budget for
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future high-priority learning tasks.
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Noise calibration for model parameter updates MUST be performed
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based on the L2 sensitivity of the gradient computation, which is
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bounded through gradient clipping techniques. Before applying
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differential privacy noise, gradients MUST be clipped to a maximum
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norm C, where C is negotiated during protocol initialization based
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on the trade-off between privacy, utility, and convergence
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requirements. The Gaussian noise added to each parameter update
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MUST have variance σ² = 2C²ln(1.25/δ)/ε² for (ε,δ)-differential
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privacy, where δ represents the probability of privacy failure and
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MUST be set to be cryptographically negligible (typically δ ≤
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10⁻⁶).
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The protocol MUST maintain detailed privacy accounting across
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learning rounds using a standardized privacy ledger format that
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records the privacy cost of each operation. This ledger MUST track
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per-tenant privacy expenditure, cross-tenant privacy interactions,
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and cumulative privacy debt according to the composition theorems.
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The privacy accounting system MUST provide cryptographic proofs of
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privacy budget compliance that can be verified by Privacy
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Compliance Verifiers as specified in Section 7. When a tenant's
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privacy budget is exhausted, the system MUST either terminate that
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tenant's participation or implement privacy budget renewal
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mechanisms with appropriate cooling-off periods to prevent privacy
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budget laundering attacks.
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The differential privacy implementation MUST be compatible with
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the secure aggregation protocols defined in Section 6, ensuring
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that privacy guarantees are maintained even when combined with
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cryptographic operations. The noise addition process MUST occur
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before any cryptographic encoding for secure aggregation, and the
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protocol MUST verify that the combination of differential privacy
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and secure aggregation does not introduce additional privacy
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vulnerabilities through cryptographic side channels or timing
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attacks. All differential privacy parameters, including ε, δ, and
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clipping bounds, MUST be negotiated during the protocol handshake
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phase and cryptographically committed to prevent manipulation
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during the learning process.
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6. Secure Multi-Tenant Federated Aggregation
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This section defines the cryptographic protocols that enable
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secure aggregation of model parameters across tenant boundaries
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while preserving privacy guarantees established in Section 5. The
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secure aggregation protocol MUST ensure that no individual tenant
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can learn information about other tenants' model updates beyond
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what is revealed by the final aggregated result. The protocol
|
|
combines homomorphic encryption with secure multiparty computation
|
|
techniques to achieve this property while maintaining
|
|
computational efficiency suitable for practical federated learning
|
|
deployments.
|
|
|
|
Agent_i Aggregator Verifier
|
|
| | |
|
|
|--local grad--->| |
|
|
| + DP noise | |
|
|
| | |
|
|
| [...N agents contribute...] |
|
|
| | |
|
|
| |--aggregate---->|
|
|
| | model update |
|
|
| | |
|
|
| |<--ZK proof-----|
|
|
| | compliance |
|
|
|<--global upd---| |
|
|
| | |
|
|
|
|
Figure 2: Privacy-Preserving Aggregation Flow
|
|
|
|
The Multi-Tenant Federated Averaging mechanism serves as the
|
|
foundation for secure parameter aggregation. Each tenant's Privacy
|
|
Compliance Verifier MUST encrypt their differentially-private
|
|
model updates using a threshold homomorphic encryption scheme
|
|
before transmission to the Federated Coordinator. The encryption
|
|
MUST use a (t,n)-threshold scheme where t represents the minimum
|
|
number of tenants required for decryption and n represents the
|
|
total number of participating tenants. This ensures that no single
|
|
tenant or coordinator can decrypt individual updates, while still
|
|
enabling computation over encrypted parameters. The homomorphic
|
|
encryption scheme SHOULD support both addition and limited
|
|
multiplication operations to accommodate various neural network
|
|
architectures commonly used in agent learning systems.
|
|
|
|
The secure multiparty computation protocol proceeds in three
|
|
phases: commitment, computation, and verification. During the
|
|
commitment phase, each tenant generates a cryptographic commitment
|
|
to their encrypted model update using a computationally-hiding and
|
|
perfectly-binding commitment scheme. The Federated Coordinator
|
|
collects all commitments and broadcasts them to participating
|
|
tenants for verification. In the computation phase, tenants
|
|
collaboratively execute a secure aggregation protocol that
|
|
computes the weighted average of model parameters without
|
|
revealing individual contributions. The protocol MUST be secure
|
|
against up to t-1 malicious participants and SHOULD provide
|
|
robustness against participant dropout through verifiable secret
|
|
sharing techniques.
|
|
|
|
Verification mechanisms ensure correctness of the aggregation
|
|
process and detect potential manipulation attempts. Each
|
|
participating tenant MUST verify the integrity of the aggregation
|
|
using zero-knowledge proofs that demonstrate correct execution of
|
|
the secure computation protocol. The ZKP-based Agent Attestation
|
|
mechanism enables tenants to prove their compliance with the
|
|
differential privacy requirements specified in Section 5 without
|
|
revealing their actual noise parameters or model updates. These
|
|
proofs MUST be verified by the Federated Coordinator and recorded
|
|
in an immutable audit trail as specified in Section 7.
|
|
Additionally, the protocol includes range proofs to verify that
|
|
model parameter updates fall within expected bounds, preventing
|
|
gradient manipulation attacks that could compromise model
|
|
convergence or extract private information.
|
|
|
|
The aggregation protocol MUST support heterogeneous agent types by
|
|
normalizing model parameters to a common representation before
|
|
encryption and aggregation. Tenants with different model
|
|
architectures encode their updates using a standardized parameter
|
|
mapping that preserves semantic meaning while enabling secure
|
|
computation. The protocol SHOULD maintain backward compatibility
|
|
with existing federated learning frameworks by providing adapter
|
|
layers that translate between PPALP message formats defined in
|
|
Section 8 and legacy federated learning APIs. Performance
|
|
optimizations including parameter compression, selective
|
|
aggregation of model layers, and asynchronous update mechanisms
|
|
MAY be implemented provided they do not compromise the privacy
|
|
guarantees established by the differential privacy and secure
|
|
aggregation components.
|
|
|
|
7. Privacy Compliance and Verification
|
|
|
|
Privacy compliance in federated agent learning environments
|
|
requires cryptographic mechanisms that provide verifiable proof of
|
|
adherence to differential privacy guarantees while maintaining
|
|
audit capabilities for regulatory requirements. This section
|
|
defines the Privacy Compliance and Verification framework that
|
|
enables tenants to demonstrate privacy compliance without
|
|
revealing sensitive implementation details or compromising the
|
|
privacy of their learning processes. The framework combines zero-
|
|
knowledge proofs, selective disclosure attestations, and
|
|
cryptographic erasure mechanisms to provide comprehensive privacy
|
|
verification capabilities.
|
|
|
|
The Privacy Compliance Verifier MUST implement zero-knowledge
|
|
proof protocols that allow federated agents to demonstrate
|
|
differential privacy adherence without revealing actual noise
|
|
values, data distributions, or model parameters. Following the SD-
|
|
JWT Privacy-Preserving Trust Proof pattern from [draft-drake-
|
|
email-tpm-attestation], the verifier SHALL use Selective
|
|
Disclosure JWT mechanisms [RFC7519] to enable privacy-preserving
|
|
attestation of compliance properties. Each participating agent
|
|
MUST generate cryptographic proofs that demonstrate: (1) proper
|
|
calibration of differential privacy noise according to the
|
|
allocated privacy budget, (2) correct implementation of the
|
|
Gaussian mechanism or equivalent noise generation, and (3)
|
|
adherence to composition theorems across multiple learning rounds.
|
|
These proofs MUST be constructed using non-interactive zero-
|
|
knowledge proof systems such as zk-SNARKs or zk-STARKs that
|
|
provide succinctness and efficient verification.
|
|
|
|
Audit trail mechanisms MUST maintain cryptographically verifiable
|
|
logs of all privacy-relevant operations while preserving the
|
|
confidentiality of sensitive learning data. The audit system SHALL
|
|
record privacy budget expenditures, aggregation operations, and
|
|
tenant isolation boundary enforcement without logging actual model
|
|
parameters or gradient values. Each audit entry MUST include a
|
|
timestamp, tenant identifier, privacy budget allocation, and
|
|
cryptographic hash of the differential privacy parameters used.
|
|
The audit trail MUST support selective disclosure to authorized
|
|
auditors while preventing unauthorized access to privacy-sensitive
|
|
operational details. Privacy budget accounting MUST be maintained
|
|
across all learning rounds with cryptographic commitments that
|
|
prevent double-spending of privacy allocations.
|
|
|
|
Cryptographic erasure capabilities MUST be implemented to address
|
|
regulatory requirements for data deletion and agent memory cleanup
|
|
as specified in [draft-gaikwad-aps-profile]. The system SHALL
|
|
support cryptographic erasure of agent memory associated with
|
|
specific user or agent identities through key destruction
|
|
mechanisms that render associated encrypted data permanently
|
|
inaccessible. When a deletion request is received, the Privacy
|
|
Compliance Verifier MUST coordinate the erasure of all federated
|
|
learning artifacts associated with the specified identity,
|
|
including gradient histories, model update records, and privacy
|
|
budget allocations. The erasure process MUST generate
|
|
cryptographic proofs of deletion that demonstrate the irreversible
|
|
destruction of the requested data across all participating
|
|
federated nodes.
|
|
|
|
Privacy compliance verification MUST support Privacy-Respecting
|
|
Capability Attestation mechanisms that enable verification of
|
|
agent privacy capabilities without revealing implementation-
|
|
specific details. Following patterns from [draft-huang-rats-
|
|
agentic-eat-cap-attest], agents MUST be able to attest to their
|
|
differential privacy implementation capabilities, secure
|
|
aggregation support, and tenant isolation enforcement without
|
|
disclosing proprietary algorithms or security configurations. The
|
|
attestation process SHALL use hardware-backed trust anchors where
|
|
available, with fallback to software-based attestation for
|
|
environments without trusted execution capabilities. All privacy
|
|
compliance proofs and attestations MUST be timestamped and include
|
|
validity periods to prevent replay attacks and ensure freshness of
|
|
compliance claims.
|
|
|
|
8. Protocol Message Formats and Flows
|
|
|
|
This section defines the concrete message formats, communication
|
|
flows, and API definitions for the Privacy-Preserving Agent
|
|
Learning Protocol (PPALP). All message exchanges MUST use JSON
|
|
format as specified in [RFC8259] with UTF-8 encoding. Protocol
|
|
implementations MUST support TLS 1.3 [RFC8446] for transport
|
|
security and MUST authenticate using OAuth 2.0 [RFC6749] bearer
|
|
tokens or X.509 certificates [RFC5280]. The protocol defines four
|
|
primary message categories: initialization messages for
|
|
establishing privacy parameters, update messages for transmitting
|
|
differentially private model parameters, aggregation messages for
|
|
secure multi-tenant computation, and verification messages for
|
|
privacy compliance validation.
|
|
|
|
The protocol message structure follows a common envelope format
|
|
with mandatory header fields. Each message MUST include a
|
|
"version" field specifying the PPALP protocol version, a "type"
|
|
field indicating the message category, a "tenantid" field for
|
|
tenant isolation enforcement, and a "timestamp" field using ISO
|
|
8601 format. Messages involving privacy budget allocation MUST
|
|
include a "privacybudget" object containing epsilon and delta
|
|
values as defined in Section 5. Security-critical messages such as
|
|
model updates and aggregation requests MUST include a "signature"
|
|
field containing a JWS signature [RFC7519] for message
|
|
authentication and integrity verification.
|
|
|
|
The federated learning session begins with a Privacy Parameter
|
|
Negotiation flow initiated by the Federated Coordinator. The
|
|
coordinator sends a "privacyinit" message specifying the global
|
|
privacy budget, differential privacy mechanism, and aggregation
|
|
method. Each Tenant Gateway responds with a "privacyaccept"
|
|
message confirming participation and local privacy constraints.
|
|
Model update flows use "modelupdate" messages containing encrypted
|
|
differentially private gradients, privacy proof attestations, and
|
|
secure aggregation shares. The Federated Coordinator processes
|
|
these through "aggregationrequest" and "aggregation_response"
|
|
messages that implement the secure multi-tenant aggregation
|
|
protocol defined in Section 6.
|
|
|
|
Privacy compliance verification occurs through dedicated message
|
|
flows involving the Privacy Compliance Verifier component. Agents
|
|
MUST send "privacyproof" messages containing zero-knowledge proofs
|
|
of differential privacy adherence for each model update. The
|
|
verifier responds with "compliancestatus" messages indicating
|
|
verification success or failure with specific error codes. Audit
|
|
trail generation uses "audit_log" messages that record privacy
|
|
budget consumption, aggregation participation, and compliance
|
|
verification events. All audit messages MUST be cryptographically
|
|
signed and include tamper-evident sequence numbers for regulatory
|
|
compliance.
|
|
|
|
The protocol defines standardized error handling through HTTP
|
|
status codes [RFC9110] and structured error responses. Privacy
|
|
budget exhaustion MUST return HTTP 429 with a
|
|
"privacybudgetexceeded" error code and remaining budget
|
|
information. Tenant isolation violations MUST return HTTP 403 with
|
|
detailed violation descriptions for security monitoring.
|
|
Implementation-specific extensions MAY add custom message fields
|
|
using the "extensions" object within the message envelope,
|
|
provided they do not compromise privacy guarantees or tenant
|
|
isolation requirements. Reference implementations MUST provide
|
|
JSON Schema validation files and OpenAPI specifications for all
|
|
message formats to ensure interoperability across different PPALP
|
|
implementations.
|
|
|
|
Example message flows demonstrate typical protocol interactions
|
|
including session establishment, multi-round federated learning
|
|
with privacy budget management, and graceful session termination
|
|
with privacy compliance verification. The appendix includes
|
|
complete JSON Schema definitions for all message types, sample
|
|
message exchanges showing proper privacy parameter negotiation,
|
|
and error handling examples covering common failure scenarios such
|
|
as aggregation timeouts and privacy proof validation failures.
|
|
|
|
9. Security Considerations
|
|
|
|
PPALP addresses multiple security threats present in multi-tenant
|
|
federated learning environments through defense-in-depth
|
|
mechanisms. The protocol MUST implement protections against
|
|
gradient inversion attacks [RFC8446] by ensuring differential
|
|
privacy noise addition occurs before any parameter sharing, with
|
|
privacy budgets calculated according to composition theorems
|
|
defined in Section 5. Membership inference attacks are mitigated
|
|
through tenant isolation enforcement, where each tenant's Privacy
|
|
Compliance Verifier cryptographically verifies that cross-tenant
|
|
information leakage remains below specified epsilon-delta bounds.
|
|
Model poisoning attacks are addressed through secure aggregation
|
|
protocols that include Byzantine-fault-tolerant mechanisms and
|
|
zero-knowledge proofs of honest participation.
|
|
|
|
Key management in PPALP follows a hierarchical trust model where
|
|
the Federated Coordinator maintains root cryptographic
|
|
credentials, while each tenant operates independent key
|
|
hierarchies for their agent populations. Tenant Gateways MUST
|
|
establish mutually authenticated channels using TLS 1.3 [RFC8446]
|
|
with certificate pinning, and rotate encryption keys according to
|
|
the privacy budget consumption rate to ensure forward secrecy. The
|
|
protocol supports both pre-shared keys for closed consortium
|
|
deployments and public key infrastructure integration [RFC5280]
|
|
for open multi-tenant scenarios. Cross-tenant cryptographic
|
|
isolation MUST be maintained such that compromise of one tenant's
|
|
keys cannot affect the privacy guarantees of other tenants'
|
|
learning processes.
|
|
|
|
Operational security considerations for multi-tenant deployments
|
|
include audit logging of all privacy budget expenditures, secure
|
|
deletion mechanisms for ephemeral cryptographic material, and
|
|
side-channel attack mitigation. The protocol MUST log all
|
|
differential privacy parameter selections, aggregation events, and
|
|
privacy compliance verification results to enable post-hoc
|
|
security analysis while ensuring these logs themselves do not leak
|
|
private information. Network-level protections SHOULD include
|
|
traffic analysis resistance through padding and timing
|
|
obfuscation, particularly for Agent Gateway intercommunication as
|
|
specified in [draft-han-rtgwg-agent-gateway-intercomm-framework].
|
|
Deployments MUST implement secure boot and attestation mechanisms
|
|
for Privacy Compliance Verifiers to prevent tampering with privacy
|
|
enforcement logic.
|
|
|
|
The protocol provides resistance to collusion attacks through
|
|
cryptographic commitment schemes and verifiable privacy budget
|
|
accounting across multiple learning rounds. When tenant subsets
|
|
attempt to correlate their local model updates to infer
|
|
information about non-colluding tenants, the secure aggregation
|
|
protocol ensures that only differentially private aggregate
|
|
statistics are revealed. However, the protocol assumes honest-but-
|
|
curious adversaries and MAY NOT provide protection against active
|
|
adversaries who deviate from the protocol specification.
|
|
Deployments requiring protection against malicious adversaries
|
|
SHOULD implement additional verification mechanisms and consider
|
|
reduced privacy parameters to account for adaptive attacks over
|
|
multiple learning epochs.
|
|
|
|
10. IANA Considerations
|
|
|
|
This document defines new protocol elements that require
|
|
registration with the Internet Assigned Numbers Authority (IANA)
|
|
to ensure interoperability and avoid conflicts with existing
|
|
protocols. The Privacy-Preserving Agent Learning Protocol (PPALP)
|
|
introduces novel message types, error codes, and protocol
|
|
identifiers that extend existing federated learning and privacy-
|
|
preserving computation frameworks as defined in [draft-kale-
|
|
agntcy-federated-privacy].
|
|
|
|
IANA SHALL establish a new registry titled "Privacy-Preserving
|
|
Agent Learning Protocol (PPALP) Parameters" under the "AI Agent
|
|
Protocol Parameters" registry group. This registry SHALL contain
|
|
three sub-registries: "PPALP Message Types", "PPALP Error Codes",
|
|
and "PPALP Privacy Compliance Identifiers". The PPALP Message
|
|
Types registry SHALL include entries for differential privacy
|
|
update messages (DPUPDATE), secure aggregation requests
|
|
(SECAGGREQ), secure aggregation responses (SECAGGRESP), privacy
|
|
budget allocation messages (PRIVBUDGET), tenant isolation
|
|
verification messages (TENANTVERIFY), and zero-knowledge proof
|
|
submission messages (ZKPSUBMIT). Each message type SHALL be
|
|
assigned a unique 16-bit identifier following the allocation
|
|
policies specified in [RFC8259] for JSON-based protocol
|
|
extensions.
|
|
|
|
The PPALP Error Codes registry SHALL define error conditions
|
|
specific to privacy-preserving federated learning operations.
|
|
Required error codes include PRIVACYBUDGETEXCEEDED (4001),
|
|
TENANTISOLATIONVIOLATION (4002),
|
|
DIFFERENTIALPRIVACYVERIFICATIONFAILED (4003),
|
|
SECUREAGGREGATIONTIMEOUT (4004), INSUFFICIENTPRIVACYPARAMETERS
|
|
(4005), and ZKPVERIFICATION_FAILED (4006). These error codes
|
|
extend the HTTP status code space as defined in [RFC9110] and
|
|
SHALL follow the expert review allocation policy with designated
|
|
experts appointed from the privacy-preserving computation and
|
|
federated learning communities.
|
|
|
|
The PPALP Privacy Compliance Identifiers registry SHALL contain
|
|
standardized identifiers for privacy mechanisms, verification
|
|
schemes, and compliance frameworks used within the protocol. This
|
|
registry SHALL include identifiers for differential privacy
|
|
mechanisms (DPGAUSSIAN, DPLAPLACE), secure aggregation protocols
|
|
(SECAGGBFV, SECAGGCKKS), and zero-knowledge proof systems
|
|
(ZKPBULLETPROOFS, ZKPPLONK). Registration policies for this
|
|
registry SHALL follow the Specification Required policy as defined
|
|
in [RFC2119], with specifications requiring formal privacy
|
|
analysis and security proofs. Updates to these registries SHALL be
|
|
coordinated with the broader AI agent protocol standardization
|
|
efforts referenced in [draft-berlinai-vera] to ensure consistency
|
|
across related privacy-preserving agent communication protocols.
|
|
|
|
11. References
|
|
|
|
11.1. Normative References
|
|
|
|
[RFC 2119]
|
|
RFC 2119
|
|
|
|
[RFC 8174]
|
|
RFC 8174
|
|
|
|
[RFC 8259]
|
|
RFC 8259
|
|
|
|
[RFC 7519]
|
|
RFC 7519
|
|
|
|
[RFC 8446]
|
|
RFC 8446
|
|
|
|
[draft-kale-agntcy-federated-privacy]
|
|
draft-kale-agntcy-federated-privacy
|
|
|
|
11.2. Informative References
|
|
|
|
[RFC 6749]
|
|
RFC 6749
|
|
|
|
[RFC 9110]
|
|
RFC 9110
|
|
|
|
[RFC 5280]
|
|
RFC 5280
|
|
|
|
[draft-berlinai-vera]
|
|
draft-berlinai-vera
|
|
|
|
[draft-huang-acme-scalable-agent-enrollment]
|
|
draft-huang-acme-scalable-agent-enrollment
|
|
|
|
[draft-messous-eat-ai]
|
|
draft-messous-eat-ai
|
|
|
|
[draft-li-dmsc-mcps-agw]
|
|
draft-li-dmsc-mcps-agw
|
|
|
|
[draft-gaikwad-aps-profile]
|
|
draft-gaikwad-aps-profile
|
|
|
|
[draft-drake-email-tpm-attestation]
|
|
draft-drake-email-tpm-attestation
|
|
|
|
[draft-huang-rats-agentic-eat-cap-attest]
|
|
draft-huang-rats-agentic-eat-cap-attest
|
|
|
|
|
|
Author's Address
|
|
|
|
Generated by IETF Draft Analyzer
|
|
Family: agent-ecosystem
|
|
2026-03-04
|