# Track 1: Conservative epsilon
EPSILON_PRODUCTION = 1.0  # Strong privacy
DELTA = 1e-5

# Applied to all aggregate queries
def add_noise(value: float, sensitivity: float) -> float:
    return value + laplace.rvs(scale=sensitivity/EPSILON_PRODUCTION)

# Minimum group size for any data release
K_ANONYMITY_THRESHOLD = 5

# Enforce in queries
def validate_query_result(results: List) -> bool:
    return len(results) >= K_ANONYMITY_THRESHOLD

# For collective operations without revealing members
class PSIProtocol:
    def __init__(self):
        self.hash_functions = 3
        self.false_positive_rate = 0.01
    
    def compute_intersection(self, set_a: Set, set_b: Set) -> int:
        # Returns size without revealing elements
        bloom_a = self.create_bloom_filter(set_a)
        bloom_b = self.create_bloom_filter(set_b)
        return self.estimate_intersection_size(bloom_a, bloom_b)

# Fine-grained control
@require_attributes({
    "track": "production",
    "consent": True,
    "age": ">= 18",
    "region": "allowed_regions"
})
def access_sensitive_feature():
    pass

# Separate databases
PRODUCTION_DB = "postgresql://prod_host/mnemosyne"
RESEARCH_DB = "postgresql://research_host/mnemosyne_research"

# Never cross-connect
assert not can_connect(PRODUCTION_DB, RESEARCH_DB)

class ConsentManager:
    def request_consent(self, user_id: str, experiment: str) -> bool:
        consent = {
            "user_id": user_id,
            "experiment": experiment,
            "timestamp": datetime.utcnow(),
            "ip_address": hash(request.ip),  # Hashed for privacy
            "details": self.get_experiment_details(experiment),
            "risks": self.get_risks(experiment),
            "benefits": self.get_benefits(experiment),
            "data_usage": self.get_data_usage(experiment),
            "duration": "6 months",
            "revocable": True
        }
        return self.store_consent(consent)

@require_consent("behavioral_tracking")
@require_track("research")
def track_experimental_behavior(user_id: str, action: Dict):
    # Only executes with valid consent
    pass

# Track 2: Stronger anonymization
def anonymize_research_data(data: Dict) -> Dict:
    # Remove direct identifiers
    data.pop('user_id', None)
    data.pop('email', None)
    
    # Generalize quasi-identifiers
    data['age'] = generalize_age(data.get('age'))
    data['location'] = generalize_location(data.get('location'))
    
    # Add noise to sensitive attributes
    data['score'] = add_laplace_noise(data.get('score'), epsilon=0.5)
    
    return data

@audit_log
def sensitive_operation(user_id: str, action: str):
    # Automatically logged:
    # - Timestamp
    # - User ID (hashed)
    # - Action
    # - Result
    # - IP address (hashed)
    # - Track mode
    pass

class AnomalyDetector:
    def __init__(self):
        self.baseline = self.load_baseline()
        self.threshold = 3.0  # Standard deviations
    
    def check_request(self, request: Request) -> bool:
        features = self.extract_features(request)
        anomaly_score = self.calculate_anomaly_score(features)
        
        if anomaly_score > self.threshold:
            self.alert_security_team(request, anomaly_score)
            return False
        return True

RATE_LIMITS = {
    "api": "100/minute",
    "auth": "5/minute",
    "research": "10/minute",
    "export": "1/hour"
}