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"""
Research Collaboration Framework for Cyber-LLM
Enables secure sharing of cybersecurity insights and collaborative research across organizations.
Author: Muzan Sano <[email protected]>
"""
import asyncio
import json
import logging
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple, Any, Set, Union, Callable
from dataclasses import dataclass, asdict, field
from enum import Enum
from abc import ABC, abstractmethod
import hashlib
import hmac
import base64
from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import rsa, padding
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
from cryptography.hazmat.backends import default_backend
import redis
import yaml
from pathlib import Path
import uuid
from ..utils.logging_system import CyberLLMLogger
from .online_learning import LearningEvent, LearningEventType
# Configure logging
logger = CyberLLMLogger(__name__).get_logger()
class CollaborationType(Enum):
"""Types of research collaboration"""
THREAT_INTELLIGENCE_SHARING = "threat_intelligence_sharing"
ATTACK_PATTERN_ANALYSIS = "attack_pattern_analysis"
DEFENSE_STRATEGY_DEVELOPMENT = "defense_strategy_development"
VULNERABILITY_RESEARCH = "vulnerability_research"
INCIDENT_CASE_STUDIES = "incident_case_studies"
TOOL_BENCHMARKING = "tool_benchmarking"
DATASET_SHARING = "dataset_sharing"
class ParticipantRole(Enum):
"""Roles in research collaboration"""
COORDINATOR = "coordinator" # Manages collaboration
CONTRIBUTOR = "contributor" # Contributes data/insights
VALIDATOR = "validator" # Validates findings
OBSERVER = "observer" # Read-only access
ANALYST = "analyst" # Analyzes shared data
class SensitivityLevel(Enum):
"""Data sensitivity levels for sharing"""
PUBLIC = "public" # Publicly shareable
CONSORTIUM = "consortium" # Share within trusted consortium
BILATERAL = "bilateral" # Share between two organizations
INTERNAL = "internal" # Internal use only
CLASSIFIED = "classified" # Highly sensitive, restricted
@dataclass
class ResearchInsight:
"""Structure for research insights"""
insight_id: str
title: str
description: str
collaboration_type: CollaborationType
sensitivity_level: SensitivityLevel
# Content
findings: Dict[str, Any]
evidence: List[Dict[str, Any]]
methodology: Dict[str, Any]
# Metadata
contributor_org: str
contributors: List[str]
created_at: datetime
updated_at: datetime
version: str
# Validation
validation_status: str = "pending" # pending, validated, disputed
validators: List[str] = field(default_factory=list)
validation_feedback: List[Dict[str, Any]] = field(default_factory=list)
# Privacy
anonymized: bool = False
data_retention_days: Optional[int] = None
access_log: List[Dict[str, Any]] = field(default_factory=list)
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for serialization"""
return {
'insight_id': self.insight_id,
'title': self.title,
'description': self.description,
'collaboration_type': self.collaboration_type.value,
'sensitivity_level': self.sensitivity_level.value,
'findings': self.findings,
'evidence': self.evidence,
'methodology': self.methodology,
'contributor_org': self.contributor_org,
'contributors': self.contributors,
'created_at': self.created_at.isoformat(),
'updated_at': self.updated_at.isoformat(),
'version': self.version,
'validation_status': self.validation_status,
'validators': self.validators,
'validation_feedback': self.validation_feedback,
'anonymized': self.anonymized,
'data_retention_days': self.data_retention_days,
'access_log': self.access_log
}
@dataclass
class CollaborationParticipant:
"""Research collaboration participant"""
participant_id: str
organization: str
name: str
email: str
role: ParticipantRole
public_key: str
# Capabilities and interests
expertise_areas: List[str]
research_interests: List[CollaborationType]
data_sharing_policy: Dict[str, Any]
# Status
status: str = "active" # active, suspended, inactive
joined_at: datetime = field(default_factory=datetime.now)
last_active: Optional[datetime] = None
# Metrics
contributions_count: int = 0
validations_count: int = 0
reputation_score: float = 0.0
@dataclass
class CollaborationProject:
"""Research collaboration project"""
project_id: str
name: str
description: str
collaboration_type: CollaborationType
# Management
coordinator: str # participant_id
participants: List[str] # participant_ids
created_at: datetime
deadline: Optional[datetime]
# Configuration
sensitivity_level: SensitivityLevel
data_sharing_rules: Dict[str, Any]
validation_requirements: Dict[str, Any]
# Status
status: str = "active" # active, completed, suspended
progress: float = 0.0
# Content
insights: List[str] = field(default_factory=list) # insight_ids
deliverables: List[Dict[str, Any]] = field(default_factory=list)
class SecureCollaborationProtocol:
"""Secure communication protocol for research collaboration"""
def __init__(self, private_key_path: str, public_key_path: str):
self.private_key = self._load_private_key(private_key_path)
self.public_key = self._load_public_key(public_key_path)
# Key registry for participants
self.participant_keys: Dict[str, Any] = {}
def _load_private_key(self, key_path: str):
"""Load private key from file"""
try:
with open(key_path, 'rb') as f:
return serialization.load_pem_private_key(
f.read(), password=None, backend=default_backend()
)
except FileNotFoundError:
logger.warning(f"Private key not found at {key_path}, generating new key")
return self._generate_key_pair(key_path)
def _load_public_key(self, key_path: str):
"""Load public key from file"""
try:
with open(key_path, 'rb') as f:
return serialization.load_pem_public_key(
f.read(), backend=default_backend()
)
except FileNotFoundError:
return self.private_key.public_key()
def _generate_key_pair(self, private_key_path: str):
"""Generate new RSA key pair"""
private_key = rsa.generate_private_key(
public_exponent=65537,
key_size=2048,
backend=default_backend()
)
# Save private key
private_pem = private_key.private_bytes(
encoding=serialization.Encoding.PEM,
format=serialization.PrivateFormat.PKCS8,
encryption_algorithm=serialization.NoEncryption()
)
with open(private_key_path, 'wb') as f:
f.write(private_pem)
# Save public key
public_key = private_key.public_key()
public_pem = public_key.public_bytes(
encoding=serialization.Encoding.PEM,
format=serialization.PublicFormat.SubjectPublicKeyInfo
)
public_key_path = private_key_path.replace('private', 'public')
with open(public_key_path, 'wb') as f:
f.write(public_pem)
logger.info(f"Generated new key pair: {private_key_path}")
return private_key
def encrypt_data(self, data: Dict[str, Any], recipient_public_key: str) -> str:
"""Encrypt data for specific recipient"""
try:
# Serialize data
data_json = json.dumps(data, default=str).encode('utf-8')
# Load recipient's public key
recipient_key = serialization.load_pem_public_key(
recipient_public_key.encode(), backend=default_backend()
)
# Encrypt with recipient's public key
encrypted_data = recipient_key.encrypt(
data_json,
padding.OAEP(
mgf=padding.MGF1(algorithm=hashes.SHA256()),
algorithm=hashes.SHA256(),
label=None
)
)
# Sign with our private key
signature = self.private_key.sign(
encrypted_data,
padding.PSS(
mgf=padding.MGF1(hashes.SHA256()),
salt_length=padding.PSS.MAX_LENGTH
),
hashes.SHA256()
)
# Combine encrypted data and signature
payload = {
'encrypted_data': base64.b64encode(encrypted_data).decode(),
'signature': base64.b64encode(signature).decode(),
'timestamp': datetime.now().isoformat()
}
return base64.b64encode(json.dumps(payload).encode()).decode()
except Exception as e:
logger.error(f"Encryption failed: {str(e)}")
raise
def decrypt_data(self, encrypted_payload: str, sender_public_key: str) -> Dict[str, Any]:
"""Decrypt data from sender"""
try:
# Decode payload
payload = json.loads(base64.b64decode(encrypted_payload).decode())
encrypted_data = base64.b64decode(payload['encrypted_data'])
signature = base64.b64decode(payload['signature'])
# Load sender's public key
sender_key = serialization.load_pem_public_key(
sender_public_key.encode(), backend=default_backend()
)
# Verify signature
sender_key.verify(
signature,
encrypted_data,
padding.PSS(
mgf=padding.MGF1(hashes.SHA256()),
salt_length=padding.PSS.MAX_LENGTH
),
hashes.SHA256()
)
# Decrypt data with our private key
decrypted_data = self.private_key.decrypt(
encrypted_data,
padding.OAEP(
mgf=padding.MGF1(algorithm=hashes.SHA256()),
algorithm=hashes.SHA256(),
label=None
)
)
return json.loads(decrypted_data.decode('utf-8'))
except Exception as e:
logger.error(f"Decryption failed: {str(e)}")
raise
def register_participant_key(self, participant_id: str, public_key: str):
"""Register participant's public key"""
self.participant_keys[participant_id] = public_key
logger.info(f"Registered public key for participant: {participant_id}")
class PrivacyPreservingAnalytics:
"""Privacy-preserving analytics for collaborative research"""
def __init__(self):
self.anonymization_functions = {
'k_anonymity': self._apply_k_anonymity,
'differential_privacy': self._apply_differential_privacy,
'homomorphic': self._apply_homomorphic_encryption
}
def anonymize_insight(self, insight: ResearchInsight, method: str = 'k_anonymity') -> ResearchInsight:
"""Anonymize research insight"""
if method not in self.anonymization_functions:
raise ValueError(f"Unsupported anonymization method: {method}")
try:
anonymized_insight = self.anonymization_functions[method](insight)
anonymized_insight.anonymized = True
logger.info(f"Applied {method} anonymization to insight: {insight.insight_id}")
return anonymized_insight
except Exception as e:
logger.error(f"Anonymization failed: {str(e)}")
raise
def _apply_k_anonymity(self, insight: ResearchInsight, k: int = 5) -> ResearchInsight:
"""Apply k-anonymity to insight"""
anonymized_insight = insight
# Remove direct identifiers
anonymized_insight.contributor_org = f"Organization_{hash(insight.contributor_org) % 1000}"
anonymized_insight.contributors = [f"Researcher_{i}" for i in range(len(insight.contributors))]
# Generalize sensitive fields in findings
if 'ip_addresses' in insight.findings:
ips = insight.findings['ip_addresses']
anonymized_insight.findings['ip_addresses'] = [
'.'.join(ip.split('.')[:2] + ['x', 'x']) for ip in ips
]
if 'timestamps' in insight.findings:
timestamps = insight.findings['timestamps']
anonymized_insight.findings['timestamps'] = [
ts[:10] for ts in timestamps # Keep only date, remove time
]
return anonymized_insight
def _apply_differential_privacy(self, insight: ResearchInsight, epsilon: float = 1.0) -> ResearchInsight:
"""Apply differential privacy to insight"""
import numpy as np
anonymized_insight = insight
# Add calibrated noise to numerical values
for key, value in insight.findings.items():
if isinstance(value, (int, float)):
# Add Laplace noise
sensitivity = 1.0 # Adjust based on data
scale = sensitivity / epsilon
noise = np.random.laplace(0, scale)
anonymized_insight.findings[key] = max(0, value + noise)
return anonymized_insight
def _apply_homomorphic_encryption(self, insight: ResearchInsight) -> ResearchInsight:
"""Apply homomorphic encryption to insight"""
# Simplified homomorphic encryption simulation
# In production, use libraries like Microsoft SEAL or IBM HElib
anonymized_insight = insight
# Encrypt numerical values
for key, value in insight.findings.items():
if isinstance(value, (int, float)):
# Simple encryption simulation (not real homomorphic encryption)
encrypted_value = f"HE_encrypted_{hash(str(value)) % 10000}"
anonymized_insight.findings[key] = encrypted_value
return anonymized_insight
def compute_privacy_risk_score(self, insight: ResearchInsight) -> float:
"""Compute privacy risk score for insight"""
risk_score = 0.0
# Check for direct identifiers
if not insight.anonymized:
risk_score += 0.3
# Check sensitivity level
sensitivity_risk = {
SensitivityLevel.PUBLIC: 0.0,
SensitivityLevel.CONSORTIUM: 0.1,
SensitivityLevel.BILATERAL: 0.2,
SensitivityLevel.INTERNAL: 0.4,
SensitivityLevel.CLASSIFIED: 0.8
}
risk_score += sensitivity_risk.get(insight.sensitivity_level, 0.5)
# Check for PII in findings
pii_indicators = ['email', 'ip', 'username', 'id', 'address']
for indicator in pii_indicators:
if any(indicator in str(value).lower() for value in insight.findings.values()):
risk_score += 0.1
# Check data retention
if insight.data_retention_days is None:
risk_score += 0.1
return min(1.0, risk_score)
class CollaborationRepository:
"""Repository for managing collaboration data"""
def __init__(self, redis_host: str = "localhost", redis_port: int = 6379):
self.redis_client = redis.Redis(host=redis_host, port=redis_port, decode_responses=True)
# Data structures
self.participants: Dict[str, CollaborationParticipant] = {}
self.projects: Dict[str, CollaborationProject] = {}
self.insights: Dict[str, ResearchInsight] = {}
# Load existing data
self._load_data()
def _load_data(self):
"""Load existing data from Redis"""
try:
# Load participants
participant_ids = self.redis_client.smembers("collaboration:participants")
for pid in participant_ids:
data = self.redis_client.hget("collaboration:participant", pid)
if data:
self.participants[pid] = CollaborationParticipant(**json.loads(data))
# Load projects
project_ids = self.redis_client.smembers("collaboration:projects")
for proj_id in project_ids:
data = self.redis_client.hget("collaboration:project", proj_id)
if data:
self.projects[proj_id] = CollaborationProject(**json.loads(data))
# Load insights
insight_ids = self.redis_client.smembers("collaboration:insights")
for insight_id in insight_ids:
data = self.redis_client.hget("collaboration:insight", insight_id)
if data:
self.insights[insight_id] = ResearchInsight(**json.loads(data))
logger.info(f"Loaded {len(self.participants)} participants, "
f"{len(self.projects)} projects, {len(self.insights)} insights")
except Exception as e:
logger.error(f"Failed to load data from Redis: {str(e)}")
def save_participant(self, participant: CollaborationParticipant):
"""Save participant to repository"""
try:
self.participants[participant.participant_id] = participant
# Save to Redis
self.redis_client.sadd("collaboration:participants", participant.participant_id)
self.redis_client.hset(
"collaboration:participant",
participant.participant_id,
json.dumps(asdict(participant), default=str)
)
logger.info(f"Saved participant: {participant.participant_id}")
except Exception as e:
logger.error(f"Failed to save participant: {str(e)}")
raise
def save_project(self, project: CollaborationProject):
"""Save project to repository"""
try:
self.projects[project.project_id] = project
# Save to Redis
self.redis_client.sadd("collaboration:projects", project.project_id)
self.redis_client.hset(
"collaboration:project",
project.project_id,
json.dumps(asdict(project), default=str)
)
logger.info(f"Saved project: {project.project_id}")
except Exception as e:
logger.error(f"Failed to save project: {str(e)}")
raise
def save_insight(self, insight: ResearchInsight):
"""Save insight to repository"""
try:
self.insights[insight.insight_id] = insight
# Save to Redis
self.redis_client.sadd("collaboration:insights", insight.insight_id)
self.redis_client.hset(
"collaboration:insight",
insight.insight_id,
json.dumps(insight.to_dict())
)
# Update access log
access_entry = {
'action': 'save',
'timestamp': datetime.now().isoformat(),
'user': 'system'
}
insight.access_log.append(access_entry)
logger.info(f"Saved insight: {insight.insight_id}")
except Exception as e:
logger.error(f"Failed to save insight: {str(e)}")
raise
def get_participant(self, participant_id: str) -> Optional[CollaborationParticipant]:
"""Get participant by ID"""
return self.participants.get(participant_id)
def get_project(self, project_id: str) -> Optional[CollaborationProject]:
"""Get project by ID"""
return self.projects.get(project_id)
def get_insight(self, insight_id: str) -> Optional[ResearchInsight]:
"""Get insight by ID"""
insight = self.insights.get(insight_id)
if insight:
# Log access
access_entry = {
'action': 'access',
'timestamp': datetime.now().isoformat(),
'user': 'system'
}
insight.access_log.append(access_entry)
return insight
def search_insights(self,
collaboration_type: Optional[CollaborationType] = None,
sensitivity_level: Optional[SensitivityLevel] = None,
contributor_org: Optional[str] = None) -> List[ResearchInsight]:
"""Search insights by criteria"""
results = []
for insight in self.insights.values():
if (collaboration_type is None or insight.collaboration_type == collaboration_type) and \
(sensitivity_level is None or insight.sensitivity_level == sensitivity_level) and \
(contributor_org is None or insight.contributor_org == contributor_org):
results.append(insight)
return results
class ResearchCollaborationManager:
"""Main manager for research collaboration"""
def __init__(self,
organization_name: str,
private_key_path: str = "keys/collaboration_private.pem",
public_key_path: str = "keys/collaboration_public.pem"):
self.organization_name = organization_name
# Initialize components
self.security_protocol = SecureCollaborationProtocol(private_key_path, public_key_path)
self.privacy_analytics = PrivacyPreservingAnalytics()
self.repository = CollaborationRepository()
# Configuration
self.collaboration_config = self._load_collaboration_config()
logger.info(f"ResearchCollaborationManager initialized for: {organization_name}")
def _load_collaboration_config(self) -> Dict[str, Any]:
"""Load collaboration configuration"""
config_path = Path("configs/collaboration.yaml")
if config_path.exists():
with open(config_path, 'r') as f:
return yaml.safe_load(f)
else:
# Default configuration
default_config = {
'default_sensitivity_level': SensitivityLevel.CONSORTIUM.value,
'auto_validation_enabled': True,
'data_retention_days': 365,
'privacy_method': 'k_anonymity',
'min_validation_score': 0.8,
'collaboration_timeout_hours': 72
}
# Save default configuration
config_path.parent.mkdir(exist_ok=True)
with open(config_path, 'w') as f:
yaml.dump(default_config, f)
return default_config
async def create_collaboration_project(self,
name: str,
description: str,
collaboration_type: CollaborationType,
coordinator_id: str,
participants: List[str],
sensitivity_level: SensitivityLevel = SensitivityLevel.CONSORTIUM,
deadline: Optional[datetime] = None) -> str:
"""Create new collaboration project"""
project_id = f"proj_{uuid.uuid4().hex[:8]}"
project = CollaborationProject(
project_id=project_id,
name=name,
description=description,
collaboration_type=collaboration_type,
coordinator=coordinator_id,
participants=participants,
created_at=datetime.now(),
deadline=deadline,
sensitivity_level=sensitivity_level,
data_sharing_rules={
'anonymization_required': sensitivity_level != SensitivityLevel.PUBLIC,
'validation_required': True,
'retention_days': self.collaboration_config.get('data_retention_days', 365)
},
validation_requirements={
'min_validators': 2,
'min_score': self.collaboration_config.get('min_validation_score', 0.8)
}
)
self.repository.save_project(project)
logger.info(f"Created collaboration project: {project_id} - {name}")
return project_id
async def contribute_insight(self,
project_id: str,
title: str,
description: str,
findings: Dict[str, Any],
evidence: List[Dict[str, Any]],
methodology: Dict[str, Any],
contributor_id: str) -> str:
"""Contribute research insight to project"""
project = self.repository.get_project(project_id)
if not project:
raise ValueError(f"Project not found: {project_id}")
contributor = self.repository.get_participant(contributor_id)
if not contributor:
raise ValueError(f"Contributor not found: {contributor_id}")
insight_id = f"insight_{uuid.uuid4().hex[:8]}"
insight = ResearchInsight(
insight_id=insight_id,
title=title,
description=description,
collaboration_type=project.collaboration_type,
sensitivity_level=project.sensitivity_level,
findings=findings,
evidence=evidence,
methodology=methodology,
contributor_org=contributor.organization,
contributors=[contributor.name],
created_at=datetime.now(),
updated_at=datetime.now(),
version="1.0",
data_retention_days=project.data_sharing_rules.get('retention_days')
)
# Apply privacy protection if required
if project.data_sharing_rules.get('anonymization_required', False):
privacy_method = self.collaboration_config.get('privacy_method', 'k_anonymity')
insight = self.privacy_analytics.anonymize_insight(insight, privacy_method)
# Compute privacy risk
privacy_risk = self.privacy_analytics.compute_privacy_risk_score(insight)
if privacy_risk > 0.7:
logger.warning(f"High privacy risk detected for insight: {insight_id} (risk: {privacy_risk:.2f})")
self.repository.save_insight(insight)
# Add insight to project
project.insights.append(insight_id)
self.repository.save_project(project)
# Update contributor metrics
contributor.contributions_count += 1
contributor.last_active = datetime.now()
self.repository.save_participant(contributor)
logger.info(f"Contributed insight: {insight_id} to project: {project_id}")
return insight_id
async def validate_insight(self,
insight_id: str,
validator_id: str,
validation_score: float,
feedback: str) -> bool:
"""Validate research insight"""
insight = self.repository.get_insight(insight_id)
if not insight:
raise ValueError(f"Insight not found: {insight_id}")
validator = self.repository.get_participant(validator_id)
if not validator:
raise ValueError(f"Validator not found: {validator_id}")
# Add validation feedback
validation_feedback = {
'validator_id': validator_id,
'validator_name': validator.name,
'score': validation_score,
'feedback': feedback,
'timestamp': datetime.now().isoformat()
}
insight.validation_feedback.append(validation_feedback)
insight.validators.append(validator_id)
# Update validation status
if len(insight.validators) >= 2: # Minimum validators met
avg_score = sum(vf['score'] for vf in insight.validation_feedback) / len(insight.validation_feedback)
min_score = self.collaboration_config.get('min_validation_score', 0.8)
if avg_score >= min_score:
insight.validation_status = "validated"
logger.info(f"Insight {insight_id} validated with score: {avg_score:.2f}")
else:
insight.validation_status = "disputed"
logger.warning(f"Insight {insight_id} disputed with score: {avg_score:.2f}")
self.repository.save_insight(insight)
# Update validator metrics
validator.validations_count += 1
validator.last_active = datetime.now()
self.repository.save_participant(validator)
return insight.validation_status == "validated"
async def share_insight_securely(self,
insight_id: str,
recipient_ids: List[str]) -> Dict[str, str]:
"""Share insight securely with specific recipients"""
insight = self.repository.get_insight(insight_id)
if not insight:
raise ValueError(f"Insight not found: {insight_id}")
shared_data = {}
for recipient_id in recipient_ids:
recipient = self.repository.get_participant(recipient_id)
if not recipient:
logger.warning(f"Recipient not found: {recipient_id}")
continue
try:
# Encrypt insight for recipient
encrypted_payload = self.security_protocol.encrypt_data(
insight.to_dict(),
recipient.public_key
)
shared_data[recipient_id] = encrypted_payload
logger.info(f"Encrypted insight {insight_id} for recipient: {recipient_id}")
except Exception as e:
logger.error(f"Failed to encrypt for {recipient_id}: {str(e)}")
return shared_data
def generate_collaboration_report(self, project_id: str) -> Dict[str, Any]:
"""Generate comprehensive collaboration report"""
project = self.repository.get_project(project_id)
if not project:
raise ValueError(f"Project not found: {project_id}")
# Collect project insights
project_insights = []
for insight_id in project.insights:
insight = self.repository.get_insight(insight_id)
if insight:
project_insights.append(insight)
# Calculate metrics
total_insights = len(project_insights)
validated_insights = len([i for i in project_insights if i.validation_status == "validated"])
disputed_insights = len([i for i in project_insights if i.validation_status == "disputed"])
pending_insights = total_insights - validated_insights - disputed_insights
# Participant statistics
participant_contributions = {}
for insight in project_insights:
org = insight.contributor_org
participant_contributions[org] = participant_contributions.get(org, 0) + 1
# Validation statistics
validation_scores = []
for insight in project_insights:
if insight.validation_feedback:
avg_score = sum(vf['score'] for vf in insight.validation_feedback) / len(insight.validation_feedback)
validation_scores.append(avg_score)
avg_validation_score = sum(validation_scores) / len(validation_scores) if validation_scores else 0.0
return {
'project_info': {
'project_id': project.project_id,
'name': project.name,
'collaboration_type': project.collaboration_type.value,
'status': project.status,
'created_at': project.created_at.isoformat(),
'participants_count': len(project.participants)
},
'insight_statistics': {
'total_insights': total_insights,
'validated_insights': validated_insights,
'disputed_insights': disputed_insights,
'pending_insights': pending_insights,
'validation_rate': validated_insights / total_insights if total_insights > 0 else 0.0
},
'validation_metrics': {
'average_validation_score': avg_validation_score,
'total_validations': sum(len(i.validators) for i in project_insights),
'unique_validators': len(set(v for i in project_insights for v in i.validators))
},
'participant_contributions': participant_contributions,
'collaboration_effectiveness': {
'insights_per_participant': total_insights / len(project.participants) if project.participants else 0.0,
'validation_coverage': len([i for i in project_insights if i.validators]) / total_insights if total_insights > 0 else 0.0
}
}
def get_collaboration_statistics(self) -> Dict[str, Any]:
"""Get overall collaboration statistics"""
total_participants = len(self.repository.participants)
total_projects = len(self.repository.projects)
total_insights = len(self.repository.insights)
# Active projects
active_projects = len([p for p in self.repository.projects.values() if p.status == "active"])
# Recent activity (last 30 days)
thirty_days_ago = datetime.now() - timedelta(days=30)
recent_insights = len([
i for i in self.repository.insights.values()
if i.created_at >= thirty_days_ago
])
# Collaboration types distribution
collab_type_dist = {}
for project in self.repository.projects.values():
ct = project.collaboration_type.value
collab_type_dist[ct] = collab_type_dist.get(ct, 0) + 1
return {
'overview': {
'total_participants': total_participants,
'total_projects': total_projects,
'total_insights': total_insights,
'active_projects': active_projects
},
'recent_activity': {
'insights_last_30_days': recent_insights,
'activity_rate': recent_insights / 30.0
},
'collaboration_distribution': collab_type_dist,
'organization': self.organization_name
}
# Factory function
def create_research_collaboration_manager(organization_name: str, **kwargs) -> ResearchCollaborationManager:
"""Create research collaboration manager with configuration"""
return ResearchCollaborationManager(organization_name, **kwargs)
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