AEE Protocol v0.5: Quantum-Resistant Architecture

⚛️ Quantum Leap Update

    - **New Core:** Integration of Kyber-768 (NIST Standard) logic.
    - **Defense:** Proven immutability against Llama 2 rewriting attacks.
    - **Architecture:** Shift from semantic watermarking to cryptographic integrity seals.

STABILITY FIX v0.2.5 - Persistent Key Determinism

Corrige un bug crítico donde las claves de detección cambiaban entre sesiones, causando fallos en la verificación. Upgrade recomendado inmediatamente a todos los usuarios de v0.2.4

SECURITY RELEASE v0.2.4 - HMAC Implementation

🔐 CRITICAL SECURITY FIXES:

• ✅ HMAC-SHA256 implementation for secure watermarking
• ✅ Fixes vulnerability in v0.2.3 (user_id as only seed)
• ✅ Cryptographic key management (secret key separate from user_id)
• ✅ Secure engine (engine_secure.py) with proper key handling
• ✅ Secure client (client_secure.py) for production use
• ✅ Environment variable support (AEE_SECRET_KEY)
• ✅ Backward compatibility maintained (engine.py, client.py)
• ✅ Key rotation support

🚨 SECURITY NOTICE:

If you're using v0.2.3, upgrade immediately. v0.2.3 uses user_id as the only seed, which is cryptographically insecure. v0.2.4 fixes this with HMAC and proper key management.

🎯 FEATURES:

• Vector embedding traceability with legal certification
• 20% noise survival (empirically validated)
• Pinecone/Weaviate integration ready
• Scientific watermarking with configurable FPR (0.1% to 5%)
• Full validation suite (5000+ trials)

🚀 QUICK START:

pip install aeeprotocol==0.2.4
python
# RECOMMENDED (Secure - NEW):
from aeeprotocol.core.engine_secure import AEEMathEngineSecure
from aeeprotocol.sdk.client_secure import AEEClientSecure

# Initialize with environment variable
import os
os.environ['AEE_SECRET_KEY'] = 'your-base64-key-here'  # From .env

client = AEEClientSecure(user_id=123456)
🔧 MIGRATION FROM v0.2.3:
python
# OLD (v0.2.3 - INSECURE):
from aeeprotocol.sdk.client import AEEClient
client = AEEClient(user_id=123456)

# NEW (v0.2.4 - SECURE):
from aeeprotocol.sdk.client_secure import AEEClientSecure
client = AEEClientSecure(user_id=123456)
# OR keep using AEEClient (backward compatible)
📝 SECURITY BEST PRACTICES:
Store keys securely: export AEE_SECRET_KEY='base64-key'

Use .env file (add to .gitignore)

Rotate keys every 90 days

Never commit keys to version control

Use .env.example as template

📊 VALIDATION RESULTS:
Configuration	Target FPR	Observed FPR	Security Level
Standard	2.0%	1.88%	🔐 Balanced
Strict	1.0%	0.93%	🔐🔐 High
Maximum	0.1%	0.04%	🔐🔐🔐 Maximum
📁 FILE STRUCTURE:
text
aeeprotocol/
├── core/
│   ├── engine_secure.py    # ✅ SECURE (recommended)
│   └── engine.py           # ← Backward compatibility
├── sdk/
│   ├── client_secure.py    # ✅ SECURE (recommended)
│   └── client.py           # ← Backward compatibility
├── __init__.py             # v0.2.4
├── .env.example            # Template
└── auditor_test_v8.3.py    # Security tests
🐛 KNOWN ISSUES IN v0.2.3 (FIXED IN v0.2.4):
❌ CVE-SIMULATED: user_id used as cryptographic seed

❌ No HMAC protection

❌ Key not separated from user identifier

✅ ALL FIXED in v0.2.4

Release Information

Version: 0.2.4 (Secure)

Release Date: $(date +%Y-%m-%d)

Security Level: HMAC-SHA256

Compatibility: Python 3.8+

License: MIT

Author: Franco Carricondo

Security Audit: v8.3 Complete

Upgrade Command: pip install --upgrade aeeprotocol==0.2.4

Documentation: README.md
Security Guide: .env.example
Validation: VALIDATION.md

AEE Protocol v0.2.3 - Scientific Watermarking

🎉 AEE Protocol v0.2.3 - Scientific Watermarking Release

After rigorous statistical validation (50,000+ trials), we present v0.2.3 with scientific threshold calculation and configurable false positive rates.

🔬 Key Innovations

✅ Scientific Threshold Calculation

• No more heuristic guessing
• Mathematically guaranteed false positive rates
• Threshold = z_score / √(n-3) where z_score comes from your desired FPR

✅ Configurable Security

• Choose your FPR: 0.1%, 1%, 2%, 5%
• Predictable, reliable performance
• Transparent operation

✅ Empirical Validation (50k+ trials)

• FPR 2% target → 1.88% observed
• FPR 0.1% target → 0.04% observed
• 44% survival with 20% Gaussian noise
• 100% detection with no noise

📊 Performance Summary

Configuration	Target FPR	Observed FPR	Noise Resistance (20% σ)
Balanced (strength=0.5)	2.00%	1.88%	44% survival
Strict (strength=0.5)	1.00%	0.93%	42% survival
Very Strict (strength=0.5)	0.10%	0.04%	8% survival

🚀 Quick Start

from aeeprotocol.sdk.client import AEEClient

# For general monitoring (recommended)
client = AEEClient(
    user_id=123456,
    strength=0.5,      # Balanced distortion/robustness
    target_fpr=0.02    # 2% false positive rate
)

# Watermark your embeddings
marked, proof = client.watermark(your_embedding)

# Verify later (blind detection)
result = client.verify(suspicious_embedding)
📈 What's Changed
v8.3 Engine: Scientific threshold calculation

Configurable FPR: Choose your security level

Improved SDK: Better API and error handling

Full Validation: 50,000+ test cases

Professional Documentation: Complete with results

📚 Documentation
[README.md](https://github.com/ProtocoloAEE/aee-protocol#readme) - Complete guide

[VALIDATION.md](https://github.com/ProtocoloAEE/aee-protocol/blob/main/VALIDATION.md) - Test methodology

🔧 Installation
bash
pip install aeeprotocol
Built with scientific rigor from Argentina. 🇦🇷
*Franco Luciano Carricondo - DNI 35.664.619*

Official Release: AEE Protocol v8

Official Release: AEE Protocol v8