RabbitAGENT: Privacy-Preserving AI Verification Network

End-to-End Encrypted AI Inference with Hybrid Cryptographic Attestation

Project Overview

RabbitAGENT is a decentralized framework combining Zero-Knowledge Proofs (ZKPs) and Trusted Execution Environments (TEEs) to enable privacy-preserving AI services. Our architecture provides cryptographic guarantees for both model integrity and data confidentiality across healthcare, finance, and enterprise applications.

Key Features

• Hybrid Verification
  TEE-based real-time validation (18ms) + ZKP post-hoc auditing (Groth16, 480ms/proof)
• Enterprise Runtime
  INT8 quantization with ≤0.9% FP32 accuracy loss (ONNX benchmark)
• Cross-Platform Trust
  Certified execution across Intel SGX/ARM TrustZone/AMD SEV
• Regulatory Compliance
  Automated GDPR Article 35 & HIPAA §164.312 controls

Architecture

rabbitagent/ ├── security/ │ ├── enclaves/ # TEE implementations │ └── zk-circuits/ # Groth16 ML circuits ├── model-training/ # Federated learning core ├── local-inference/ # Privacy-preserving runtime ├── proof-aggregation/ # Batch ZKP optimization └── evm-connector/ # On-chain verification

Getting Started

Prerequisites

• x86_64/ARMv8.3+ hardware with TEE support
• NVIDIA GPU (CUDA 12.0+) for ZKP acceleration
• Rust 1.70+ & Python 3.11+