What is Post-Quantum Cryptography?

Post-quantum cryptography focuses on developing cryptographic systems that are secure against both quantum and classical computers. This involves analyzing cryptographic schemes, simulating quantum attacks, and ensuring compliance with security standards.

Features

Cryptographic Analysis

Analyze quantum-resistant cryptographic schemes including lattice-based encryption and multivariate signatures.

Attack Simulation

Test resistance against quantum attacks including Grover’s algorithm and Shor’s algorithm.

Performance Testing

Benchmark key generation, encryption/decryption speeds, and throughput metrics.

Security Assessment

Evaluate implementation security including side-channel resistance and hardening measures.

Compliance Validation

Verify compliance with NIST PQC, Common Criteria, and FIPS standards.

Quick Start

1

Install Package

First, install the PraisonAI Agents package:

pip install praisonaiagents
2

Set API Key

Set your OpenAI API key as an environment variable in your terminal:

export OPENAI_API_KEY=your_api_key_here
3

Create a file

Create a new file app.py with the basic setup:

from praisonaiagents import Agent, Task, PraisonAIAgents
import time
from typing import Dict, List
import asyncio

def analyze_cryptographic_scheme():
    """Simulates cryptographic scheme analysis"""
    schemes = [
        {
            "name": "lattice_based",
            "type": "encryption",
            "key_size": 2048,
            "security_level": "AES-256 equivalent",
            "parameters": {
                "lattice_dimension": 1024,
                "modulus": "q = 2^32 - 1",
                "error_distribution": "discrete_gaussian"
            }
        },
        {
            "name": "multivariate",
            "type": "signature",
            "key_size": 4096,
            "security_level": "SHA-384 equivalent",
            "parameters": {
                "variables": 128,
                "equations": 128,
                "field_size": "2^8"
            }
        }
    ]
    return schemes[int(time.time()) % 2]

def simulate_quantum_attacks(scheme: Dict):
    """Simulates quantum attack scenarios"""
    attacks = {
        "grover_search": {
            "effectiveness": "reduced",
            "quantum_speedup": "quadratic",
            "estimated_qubits": 2048,
            "time_complexity": "O(2^n/2)"
        },
        "shor_algorithm": {
            "effectiveness": "ineffective",
            "quantum_speedup": "none",
            "vulnerability": "resistant",
            "classical_hardness": "maintained"
        },
        "quantum_hidden_subgroup": {
            "effectiveness": "partial",
            "applicability": "limited",
            "mitigation": "parameter_adjustment"
        }
    }
    return attacks

def benchmark_performance(scheme: Dict):
    """Benchmarks cryptographic performance"""
    benchmarks = {
        "key_generation": {
            "time": 0.5 + (time.time() % 1) / 10,  # seconds
            "memory": 128 + (int(time.time()) % 64)  # MB
        },
        "encryption_speed": {
            "small_message": 0.001 + (time.time() % 1) / 1000,  # seconds
            "large_message": 0.1 + (time.time() % 1) / 10  # seconds
        },
        "decryption_speed": {
            "small_message": 0.002 + (time.time() % 1) / 1000,
            "large_message": 0.15 + (time.time() % 1) / 10
        },
        "throughput": {
            "encryption": 100 + (int(time.time()) % 50),  # MB/s
            "decryption": 80 + (int(time.time()) % 40)   # MB/s
        }
    }
    return benchmarks

def assess_implementation(scheme: Dict, benchmarks: Dict):
    """Assesses implementation security"""
    assessment = {
        "side_channel_resistance": {
            "timing_attacks": "protected",
            "power_analysis": "partial_vulnerability",
            "cache_attacks": "mitigated"
        },
        "implementation_hardening": {
            "constant_time": True,
            "memory_protection": True,
            "error_handling": "secure"
        },
        "compiler_optimizations": {
            "safety": "verified",
            "performance_impact": -0.05 + (time.time() % 1) / 10
        }
    }
    return assessment

def validate_compliance(scheme: Dict, assessment: Dict):
    """Validates compliance and standards"""
    compliance = {
        "nist_pqc": {
            "status": "submitted",
            "round": 3,
            "requirements_met": True
        },
        "common_criteria": {
            "evaluation_level": "EAL4+",
            "protection_profile": "compliant"
        },
        "fips_140_3": {
            "security_level": 3,
            "validation_status": "in_process"
        }
    }
    return compliance

# Create specialized agents
crypto_analyzer = Agent(
    name="Crypto Analyzer",
    role="Cryptographic Analysis",
    goal="Analyze cryptographic schemes",
    instructions="Evaluate quantum-resistant cryptographic schemes",
    tools=[analyze_cryptographic_scheme]
)

attack_simulator = Agent(
    name="Attack Simulator",
    role="Attack Simulation",
    goal="Simulate quantum attacks",
    instructions="Test resistance against quantum attacks",
    tools=[simulate_quantum_attacks]
)

performance_benchmarker = Agent(
    name="Performance Benchmarker",
    role="Performance Testing",
    goal="Benchmark cryptographic performance",
    instructions="Measure performance metrics",
    tools=[benchmark_performance]
)

implementation_assessor = Agent(
    name="Implementation Assessor",
    role="Implementation Assessment",
    goal="Assess implementation security",
    instructions="Evaluate implementation security",
    tools=[assess_implementation]
)

compliance_validator = Agent(
    name="Compliance Validator",
    role="Compliance Validation",
    goal="Validate standards compliance",
    instructions="Verify compliance with standards",
    tools=[validate_compliance]
)

# Create workflow tasks
analysis_task = Task(
    name="analyze_crypto",
    description="Analyze cryptographic scheme",
    expected_output="Cryptographic analysis",
    agent=crypto_analyzer,
    is_start=True,
    next_tasks=["simulate_attacks", "benchmark_performance"]
)

attack_task = Task(
    name="simulate_attacks",
    description="Simulate quantum attacks",
    expected_output="Attack simulation results",
    agent=attack_simulator,
    next_tasks=["assess_implementation"]
)

benchmark_task = Task(
    name="benchmark_performance",
    description="Benchmark performance",
    expected_output="Performance benchmarks",
    agent=performance_benchmarker,
    next_tasks=["assess_implementation"]
)

assessment_task = Task(
    name="assess_implementation",
    description="Assess implementation",
    expected_output="Implementation assessment",
    agent=implementation_assessor,
    context=[attack_task, benchmark_task],
    next_tasks=["validate_compliance"]
)

compliance_task = Task(
    name="validate_compliance",
    description="Validate compliance",
    expected_output="Compliance validation",
    agent=compliance_validator,
    task_type="decision",
    condition={
        "compliant": "",  # End workflow if compliant
        "non_compliant": ["assess_implementation"]  # Reassess if non-compliant
    }
)

# Create workflow
workflow = PraisonAIAgents(
    agents=[crypto_analyzer, attack_simulator, performance_benchmarker,
            implementation_assessor, compliance_validator],
    tasks=[analysis_task, attack_task, benchmark_task,
           assessment_task, compliance_task],
    process="workflow",
    verbose=True
)

async def main():
    print("\nStarting Quantum-Resistant Cryptography Validation Workflow...")
    print("=" * 50)
    
    # Run workflow
    results = await workflow.astart()
    
    # Print results
    print("\nCryptography Validation Results:")
    print("=" * 50)
    for task_id, result in results["task_results"].items():
        if result:
            print(f"\nTask: {task_id}")
            print(f"Result: {result.raw}")
            print("-" * 50)

if __name__ == "__main__":
    asyncio.run(main())

Next Steps

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