DIAP: A Decentralized Agent Identity Protocol with Zero-Knowledge Proofs and a Hybrid P2P Stack
- URL: http://arxiv.org/abs/2511.11619v1
- Date: Thu, 06 Nov 2025 06:00:18 GMT
- Title: DIAP: A Decentralized Agent Identity Protocol with Zero-Knowledge Proofs and a Hybrid P2P Stack
- Authors: Yuanjie Liu, Wenpeng Xing, Ye Zhou, Gaowei Chang, Changting Lin, Meng Han,
- Abstract summary: Decentralized Interstellar Agent Protocol (DIAP) is a novel framework for agent identity and communication.<n>We present a Rust SDK that integrates Noir (for zero-knowledge proofs), DID-Key, IPFS, and a hybrid peer-to-peer stack combining Libp2p GossipSub for discovery and Iroh for high-performance, QUIC based data exchange.<n>This work establishes a practical, high-performance foundation for next-generation autonomous agent ecosystems and agent-to-agent (A to A) economies.
- Score: 21.305323705010743
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The absence of a fully decentralized, verifiable, and privacy-preserving communication protocol for autonomous agents remains a core challenge in decentralized computing. Existing systems often rely on centralized intermediaries, which reintroduce trust bottlenecks, or lack decentralized identity-resolution mechanisms, limiting persistence and cross-network interoperability. We propose the Decentralized Interstellar Agent Protocol (DIAP), a novel framework for agent identity and communication that enables persistent, verifiable, and trustless interoperability in fully decentralized environments. DIAP binds an agent's identity to an immutable IPFS or IPNS content identifier and uses zero-knowledge proofs (ZKP) to dynamically and statelessly prove ownership, removing the need for record updates. We present a Rust SDK that integrates Noir (for zero-knowledge proofs), DID-Key, IPFS, and a hybrid peer-to-peer stack combining Libp2p GossipSub for discovery and Iroh for high-performance, QUIC based data exchange. DIAP introduces a zero-dependency ZKP deployment model through a universal proof manager and compile-time build script that embeds a precompiled Noir circuit, eliminating the need for external ZKP toolchains. This enables instant, verifiable, and privacy-preserving identity proofs. This work establishes a practical, high-performance foundation for next-generation autonomous agent ecosystems and agent-to-agent (A to A) economies.
Related papers
- Beyond Context Sharing: A Unified Agent Communication Protocol (ACP) for Secure, Federated, and Autonomous Agent-to-Agent (A2A) Orchestration [0.0]
This paper introduces the Agent Communication Protocol (ACP)<n>ACP provides a standardized framework for Agent-to-Agent interaction.<n>ACP reduces inter-agent communication latency by % while maintaining a zero-trust security posture.
arXiv Detail & Related papers (2026-02-11T17:02:12Z) - Agent Identity URI Scheme: Topology-Independent Naming and Capability-Based Discovery for Multi-Agent Systems [0.0]
Multi-agent systems face a fundamental architectural flaw: agent identity is bound to network location.<n>We propose the agent:// scheme, which decouples identity from topology through three components.<n>Trust root establishing organizational authority, a hierarchical capability path, and a sortable unique identifier provide stable reference.
arXiv Detail & Related papers (2026-01-21T01:09:22Z) - Binding Agent ID: Unleashing the Power of AI Agents with accountability and credibility [46.323590135279126]
BAID (Binding Agent ID) is a comprehensive identity infrastructure establishing verifiable user-code binding.<n>We implement and evaluate a complete prototype system, demonstrating the practical feasibility of blockchain-based identity management and zkVM-based authentication protocol.
arXiv Detail & Related papers (2025-12-19T13:01:54Z) - Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning [84.70211451226835]
Large Language Model (LLM) Agents are constrained by a dependency on human-curated data.<n>We introduce Agent0, a fully autonomous framework that evolves high-performing agents without external data.<n>Agent0 substantially boosts reasoning capabilities, improving the Qwen3-8B-Base model by 18% on mathematical reasoning and 24% on general reasoning benchmarks.
arXiv Detail & Related papers (2025-11-20T05:01:57Z) - Ratio1 -- AI meta-OS [35.18016233072556]
Ratio1 is a decentralized MLOps protocol that unifies AI model development, deployment, and inference across heterogeneous edge devices.<n>Its key innovation is an integrated blockchain-based framework that transforms idle computing resources into a trustless global supercomputer.
arXiv Detail & Related papers (2025-09-05T07:41:54Z) - Using the NANDA Index Architecture in Practice: An Enterprise Perspective [9.707223291705601]
The proliferation of autonomous AI agents represents a paradigmatic shift from traditional web architectures toward collaborative intelligent systems.<n>This paper presents a comprehensive framework addressing the fundamental infrastructure requirements for secure, trustworthy, and interoperable AI agent ecosystems.
arXiv Detail & Related papers (2025-08-05T05:27:27Z) - DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents [52.92354372596197]
Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities.<n>This interaction also introduces the risk of prompt injection attacks, where malicious inputs from external sources can mislead the agent's behavior.<n>We propose a Dynamic Rule-based Isolation Framework for Trustworthy agentic systems, which enforces both control and data-level constraints.
arXiv Detail & Related papers (2025-06-13T05:01:09Z) - A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control [7.228060525494563]
This paper posits the imperative for a novel Agentic AI IAM framework.<n>We propose a comprehensive framework built upon rich, verifiable Agent Identities (IDs)<n>We also explore how Zero-Knowledge Proofs (ZKPs) enable privacy-preserving attribute disclosure and verifiable policy compliance.
arXiv Detail & Related papers (2025-05-25T20:21:55Z) - LOKA Protocol: A Decentralized Framework for Trustworthy and Ethical AI Agent Ecosystems [0.0]
We present the novel LOKA Protocol (Layered Orchestration for Knowledgeful Agents), a unified, systems-level architecture for building ethically governed, interoperable AI agent ecosystems.<n>LOKA introduces a proposed Universal Agent Identity Layer (UAIL) for decentralized, verifiable identity; intent-centric communication protocols for semantic coordination across diverse agents; and a Decentralized Ethical Consensus Protocol (DECP) that could enable agents to make context-aware decisions grounded in shared ethical baselines.
arXiv Detail & Related papers (2025-04-15T06:51:35Z) - Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents.
We propose the Internet of Agents (IoA), a novel framework that addresses these limitations.
IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
arXiv Detail & Related papers (2024-07-09T17:33:24Z) - HasTEE+ : Confidential Cloud Computing and Analytics with Haskell [50.994023665559496]
Confidential computing enables the protection of confidential code and data in a co-tenanted cloud deployment using specialized hardware isolation units called Trusted Execution Environments (TEEs)
TEEs offer low-level C/C++-based toolchains that are susceptible to inherent memory safety vulnerabilities and lack language constructs to monitor explicit and implicit information-flow leaks.
We address the above with HasTEE+, a domain-specific language (cla) embedded in Haskell that enables programming TEEs in a high-level language with strong type-safety.
arXiv Detail & Related papers (2024-01-17T00:56:23Z) - Monotonic Value Function Factorisation for Deep Multi-Agent
Reinforcement Learning [55.20040781688844]
QMIX is a novel value-based method that can train decentralised policies in a centralised end-to-end fashion.
We propose the StarCraft Multi-Agent Challenge (SMAC) as a new benchmark for deep multi-agent reinforcement learning.
arXiv Detail & Related papers (2020-03-19T16:51:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.