Ambient Computing Architecture

import asyncio
import numpy as np
from typing import Dict, List, Optional, Tuple, Any
from dataclasses import dataclass
from enum import Enum
import json
import time
from collections import defaultdict, deque

class ContextType(Enum):
    LOCATION = "location"
    TEMPORAL = "temporal"
    SOCIAL = "social"
    ENVIRONMENTAL = "environmental"
    ACTIVITY = "activity"
    BIOMETRIC = "biometric"

class DeviceCapability(Enum):
    DISPLAY = "display"
    AUDIO = "audio"
    SENSOR = "sensor"
    ACTUATOR = "actuator"
    COMPUTE = "compute"
    NETWORK = "network"

@dataclass
class ContextData:
    context_type: ContextType
    value: Any
    confidence: float
    timestamp: float
    source_device: str
    metadata: Dict[str, Any] = None

@dataclass
class UserIntent:
    intent_id: str
    probability: float
    context_triggers: List[ContextType]
    required_actions: List[Dict[str, Any]]
    estimated_completion_time: float

@dataclass
class AmbientDevice:
    device_id: str
    device_type: str
    capabilities: List[DeviceCapability]
    location: Tuple[float, float, float]  # x, y, z coordinates
    is_active: bool
    battery_level: Optional[float] = None
    processing_load: float = 0.0
    network_quality: float = 1.0

class AmbientOrchestrator:
    def __init__(self, context_window_minutes: int = 60):
        self.devices: Dict[str, AmbientDevice] = {}
        self.context_history: Dict[str, deque] = defaultdict(
            lambda: deque(maxlen=1000)
        )
        self.user_models: Dict[str, UserBehaviorModel] = {}
        self.active_automations: Dict[str, AutomationRule] = {}
        self.context_window = context_window_minutes * 60  # Convert to seconds
        
    async def register_device(self, device: AmbientDevice):
        """Register a new ambient device in the ecosystem"""
        self.devices[device.device_id] = device
        
        # Initialize device-specific context tracking
        await self.initialize_device_context(device)
        
        print(f"Registered ambient device: {device.device_id}")
    
    async def process_context_update(self, user_id: str, context_data: ContextData):
        """Process incoming context data and trigger appropriate responses"""
        # Store context data
        self.context_history[user_id].append(context_data)
        
        # Update user behavior model
        if user_id not in self.user_models:
            self.user_models[user_id] = UserBehaviorModel(user_id)
        
        await self.user_models[user_id].update_context(context_data)
        
        # Predict user intents
        predicted_intents = await self.predict_user_intents(user_id)
        
        # Execute appropriate automations
        for intent in predicted_intents:
            if intent.probability > 0.7:  # High confidence threshold
                await self.execute_intent_automation(user_id, intent)
    
    async def predict_user_intents(self, user_id: str) -> List[UserIntent]:
        """Predict user intents based on current and historical context"""
        if user_id not in self.user_models:
            return []
        
        user_model = self.user_models[user_id]
        current_context = self.get_current_context(user_id)
        
        # Generate intent predictions
        intents = []
        
        # Analyze temporal patterns
        temporal_intents = await self.analyze_temporal_patterns(user_id, current_context)
        intents.extend(temporal_intents)
        
        # Analyze location-based patterns
        location_intents = await self.analyze_location_patterns(user_id, current_context)
        intents.extend(location_intents)
        
        # Analyze activity patterns
        activity_intents = await self.analyze_activity_patterns(user_id, current_context)
        intents.extend(activity_intents)
        
        # Sort by probability and return top predictions
        intents.sort(key=lambda x: x.probability, reverse=True)
        return intents[:5]  # Return top 5 predictions
    
    async def execute_intent_automation(self, user_id: str, intent: UserIntent):
        """Execute automation actions for predicted user intent"""
        try:
            # Select optimal devices for actions
            selected_devices = await self.select_optimal_devices(
                intent.required_actions, user_id
            )
            
            # Execute actions across selected devices
            automation_tasks = []
            for action in intent.required_actions:
                device_id = selected_devices.get(action['capability'])
                if device_id:
                    task = self.execute_device_action(device_id, action)
                    automation_tasks.append(task)
            
            # Execute all actions concurrently
            results = await asyncio.gather(*automation_tasks, return_exceptions=True)
            
            # Log automation execution
            await self.log_automation_execution(user_id, intent, results)
            
        except Exception as e:
            print(f"Automation execution failed for intent {intent.intent_id}: {e}")
    
    async def select_optimal_devices(self, required_actions: List[Dict[str, Any]], 
                                   user_id: str) -> Dict[str, str]:
        """Select optimal devices for executing required actions"""
        selected_devices = {}
        user_location = await self.get_user_location(user_id)
        
        for action in required_actions:
            required_capability = DeviceCapability(action['capability'])
            
            # Find candidate devices with required capability
            candidates = [
                device for device in self.devices.values()
                if required_capability in device.capabilities and device.is_active
            ]
            
            if not candidates:
                continue
            
            # Score devices based on multiple factors
            best_device = None
            best_score = -1
            
            for device in candidates:
                score = self.calculate_device_score(
                    device, user_location, action
                )
                
                if score > best_score:
                    best_score = score
                    best_device = device
            
            if best_device:
                selected_devices[action['capability']] = best_device.device_id
        
        return selected_devices
    
    def calculate_device_score(self, device: AmbientDevice, 
                             user_location: Tuple[float, float, float],
                             action: Dict[str, Any]) -> float:
        """Calculate suitability score for device to execute action"""
        score = 0.0
        
        # Distance factor (closer is better)
        if user_location:
            distance = np.linalg.norm(
                np.array(device.location) - np.array(user_location)
            )
            distance_score = max(0, 1 - (distance / 50.0))  # Normalize to 50m range
            score += distance_score * 0.3
        
        # Battery level factor
        if device.battery_level is not None:
            score += (device.battery_level / 100.0) * 0.2
        
        # Processing load factor (lower is better)
        load_score = max(0, 1 - device.processing_load)
        score += load_score * 0.2
        
        # Network quality factor
        score += device.network_quality * 0.2
        
        # Device type preference for action
        type_preference = self.get_device_type_preference(
            device.device_type, action['capability']
        )
        score += type_preference * 0.1
        
        return score
    
    def get_device_type_preference(self, device_type: str, capability: str) -> float:
        """Get device type preference score for specific capability"""
        preferences = {
            'smart_speaker': {'audio': 1.0, 'display': 0.2},
            'smart_display': {'display': 1.0, 'audio': 0.8},
            'smart_light': {'actuator': 1.0},
            'smart_thermostat': {'actuator': 0.9, 'sensor': 0.7},
            'smartphone': {'display': 0.7, 'audio': 0.6, 'sensor': 0.8}
        }
        
        return preferences.get(device_type, {}).get(capability, 0.5)
    
    async def analyze_temporal_patterns(self, user_id: str, 
                                      current_context: Dict[str, ContextData]) -> List[UserIntent]:
        """Analyze temporal patterns to predict time-based intents"""
        intents = []
        current_time = time.time()
        hour_of_day = int((current_time % (24 * 3600)) // 3600)
        day_of_week = int((current_time // (24 * 3600)) % 7)
        
        # Morning routine intent
        if 6 <= hour_of_day <= 9:
            intent = UserIntent(
                intent_id="morning_routine",
                probability=0.8,
                context_triggers=[ContextType.TEMPORAL, ContextType.ACTIVITY],
                required_actions=[
                    {'capability': 'display', 'action': 'show_weather', 'priority': 1},
                    {'capability': 'audio', 'action': 'play_news', 'priority': 2},
                    {'capability': 'actuator', 'action': 'adjust_lights', 'priority': 1}
                ],
                estimated_completion_time=300  # 5 minutes
            )
            intents.append(intent)
        
        # Evening wind-down intent
        if 20 <= hour_of_day <= 23:
            intent = UserIntent(
                intent_id="evening_winddown",
                probability=0.7,
                context_triggers=[ContextType.TEMPORAL, ContextType.ENVIRONMENTAL],
                required_actions=[
                    {'capability': 'actuator', 'action': 'dim_lights', 'priority': 1},
                    {'capability': 'actuator', 'action': 'adjust_temperature', 'priority': 2},
                    {'capability': 'audio', 'action': 'play_ambient', 'priority': 3}
                ],
                estimated_completion_time=180  # 3 minutes
            )
            intents.append(intent)
        
        return intents
    
    async def analyze_location_patterns(self, user_id: str,
                                      current_context: Dict[str, ContextData]) -> List[UserIntent]:
        """Analyze location patterns to predict location-based intents"""
        intents = []
        
        # Check for location context
        location_context = current_context.get(ContextType.LOCATION)
        if not location_context:
            return intents
        
        location = location_context.value
        
        # Home arrival intent
        if location.get('type') == 'home' and location.get('transition') == 'entering':
            intent = UserIntent(
                intent_id="home_arrival",
                probability=0.9,
                context_triggers=[ContextType.LOCATION],
                required_actions=[
                    {'capability': 'actuator', 'action': 'turn_on_lights', 'priority': 1},
                    {'capability': 'actuator', 'action': 'adjust_temperature', 'priority': 2},
                    {'capability': 'display', 'action': 'show_summary', 'priority': 3}
                ],
                estimated_completion_time=120  # 2 minutes
            )
            intents.append(intent)
        
        # Office arrival intent
        if location.get('type') == 'office' and location.get('transition') == 'entering':
            intent = UserIntent(
                intent_id="work_mode",
                probability=0.8,
                context_triggers=[ContextType.LOCATION, ContextType.TEMPORAL],
                required_actions=[
                    {'capability': 'display', 'action': 'show_calendar', 'priority': 1},
                    {'capability': 'actuator', 'action': 'adjust_desk_lighting', 'priority': 2}
                ],
                estimated_completion_time=60  # 1 minute
            )
            intents.append(intent)
        
        return intents
    
    async def analyze_activity_patterns(self, user_id: str,
                                      current_context: Dict[str, ContextData]) -> List[UserIntent]:
        """Analyze activity patterns to predict activity-based intents"""
        intents = []
        
        # Check for activity context
        activity_context = current_context.get(ContextType.ACTIVITY)
        if not activity_context:
            return intents
        
        activity = activity_context.value
        
        # Exercise intent
        if activity.get('type') in ['running', 'walking', 'gym']:
            intent = UserIntent(
                intent_id="exercise_mode",
                probability=0.85,
                context_triggers=[ContextType.ACTIVITY, ContextType.BIOMETRIC],
                required_actions=[
                    {'capability': 'audio', 'action': 'play_workout_music', 'priority': 1},
                    {'capability': 'display', 'action': 'show_fitness_metrics', 'priority': 2}
                ],
                estimated_completion_time=3600  # 1 hour
            )
            intents.append(intent)
        
        # Focus/work intent
        if activity.get('type') in ['working', 'studying', 'reading']:
            intent = UserIntent(
                intent_id="focus_mode",
                probability=0.75,
                context_triggers=[ContextType.ACTIVITY, ContextType.ENVIRONMENTAL],
                required_actions=[
                    {'capability': 'audio', 'action': 'enable_noise_cancellation', 'priority': 1},
                    {'capability': 'display', 'action': 'minimize_notifications', 'priority': 2},
                    {'capability': 'actuator', 'action': 'optimize_lighting', 'priority': 3}
                ],
                estimated_completion_time=7200  # 2 hours
            )
            intents.append(intent)
        
        return intents
    
    def get_current_context(self, user_id: str) -> Dict[ContextType, ContextData]:
        """Get current context data for user"""
        current_context = {}
        recent_threshold = time.time() - 300  # Last 5 minutes
        
        if user_id not in self.context_history:
            return current_context
        
        # Get most recent context data for each type
        for context_data in reversed(self.context_history[user_id]):
            if context_data.timestamp < recent_threshold:
                break
                
            if context_data.context_type not in current_context:
                current_context[context_data.context_type] = context_data
        
        return current_context
    
    async def get_user_location(self, user_id: str) -> Optional[Tuple[float, float, float]]:
        """Get current user location"""
        current_context = self.get_current_context(user_id)
        location_context = current_context.get(ContextType.LOCATION)
        
        if location_context and 'coordinates' in location_context.value:
            coords = location_context.value['coordinates']
            return (coords['x'], coords['y'], coords.get('z', 0))
        
        return None
    
    async def execute_device_action(self, device_id: str, action: Dict[str, Any]):
        """Execute action on specific device"""
        device = self.devices.get(device_id)
        if not device:
            raise ValueError(f"Device {device_id} not found")
        
        # Simulate action execution
        print(f"Executing {action['action']} on {device_id}")
        
        # Update device load
        device.processing_load = min(1.0, device.processing_load + 0.1)
        
        # Simulate execution time
        await asyncio.sleep(0.1)
        
        return {'device_id': device_id, 'action': action['action'], 'status': 'completed'}
    
    async def log_automation_execution(self, user_id: str, intent: UserIntent, results: List):
        """Log automation execution for analytics and learning"""
        log_entry = {
            'user_id': user_id,
            'intent_id': intent.intent_id,
            'probability': intent.probability,
            'execution_time': time.time(),
            'results': [r for r in results if not isinstance(r, Exception)],
            'errors': [str(r) for r in results if isinstance(r, Exception)]
        }
        
        # In production, this would write to a logging system
        print(f"Automation log: {json.dumps(log_entry, indent=2)}")

class UserBehaviorModel:
    def __init__(self, user_id: str):
        self.user_id = user_id
        self.behavior_patterns = defaultdict(list)
        self.preference_weights = defaultdict(float)
    
    async def update_context(self, context_data: ContextData):
        """Update user behavior model with new context data"""
        self.behavior_patterns[context_data.context_type].append(context_data)
        
        # Update preference weights based on context confidence
        self.preference_weights[context_data.context_type] += context_data.confidence * 0.1

# Usage example
async def demonstrate_ambient_computing():
    orchestrator = AmbientOrchestrator()
    
    # Register ambient devices
    smart_speaker = AmbientDevice(
        device_id="living_room_speaker",
        device_type="smart_speaker",
        capabilities=[DeviceCapability.AUDIO],
        location=(10.0, 5.0, 1.0),
        is_active=True,
        battery_level=None  # Plugged in
    )
    
    smart_display = AmbientDevice(
        device_id="kitchen_display",
        device_type="smart_display", 
        capabilities=[DeviceCapability.DISPLAY, DeviceCapability.AUDIO],
        location=(15.0, 8.0, 1.2),
        is_active=True
    )
    
    await orchestrator.register_device(smart_speaker)
    await orchestrator.register_device(smart_display)
    
    # Simulate context updates
    location_context = ContextData(
        context_type=ContextType.LOCATION,
        value={'type': 'home', 'transition': 'entering', 'coordinates': {'x': 12.0, 'y': 6.0}},
        confidence=0.95,
        timestamp=time.time(),
        source_device="smartphone_123"
    )
    
    activity_context = ContextData(
        context_type=ContextType.ACTIVITY,
        value={'type': 'arriving_home', 'confidence': 0.9},
        confidence=0.9,
        timestamp=time.time(),
        source_device="smartphone_123"
    )
    
    # Process context and trigger automations
    await orchestrator.process_context_update("user_456", location_context)
    await orchestrator.process_context_update("user_456", activity_context)
    
    print("Ambient computing demonstration completed")

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

import crypto from 'crypto';
import { EventEmitter } from 'events';

interface PrivacyPolicy {
  userId: string;
  dataTypes: Set<string>;
  retentionPeriod: number; // days
  sharingPermissions: Map<string, boolean>;
  anonymizationLevel: 'none' | 'basic' | 'advanced' | 'differential';
  consentTimestamp: number;
}

interface SensorReading {
  deviceId: string;
  sensorType: string;
  value: any;
  timestamp: number;
  accuracy: number;
  isPersonal: boolean;
}

interface IntelligenceInsight {
  insightId: string;
  userId: string;
  category: string;
  confidence: number;
  actionRecommendation?: string;
  privacyLevel: 'public' | 'personal' | 'sensitive' | 'restricted';
  expirationTime: number;
}

class PrivacyPreservingIntelligence extends EventEmitter {
  private privacyPolicies: Map<string, PrivacyPolicy> = new Map();
  private encryptionKeys: Map<string, Buffer> = new Map();
  private federatedModels: Map<string, FederatedModel> = new Map();
  private differentialNoiseManager: DifferentialNoiseManager;

  constructor() {
    super();
    this.differentialNoiseManager = new DifferentialNoiseManager();
    this.initializePrivacyEngine();
  }

  async processPersonalData(
    userId: string,
    sensorReadings: SensorReading[]
  ): Promise<IntelligenceInsight[]> {
    try {
      // Check user privacy policy
      const policy = await this.getPrivacyPolicy(userId);
      if (!policy) {
        throw new Error('No privacy policy found for user');
      }

      // Filter data based on privacy preferences
      const allowedReadings = this.filterDataByPolicy(sensorReadings, policy);

      // Process data with privacy preservation
      const insights = await this.generatePrivateInsights(
        userId,
        allowedReadings,
        policy
      );

      // Apply differential privacy if required
      const privatizedInsights = await this.applyDifferentialPrivacy(
        insights,
        policy
      );

      // Log privacy-compliant analytics
      await this.logPrivacyCompliantAnalytics(userId, privatizedInsights);

      return privatizedInsights;
    } catch (error) {
      console.error('Privacy-preserving processing failed:', error);
      this.emit('privacyError', { userId, error });
      return [];
    }
  }

  private filterDataByPolicy(
    readings: SensorReading[],
    policy: PrivacyPolicy
  ): SensorReading[] {
    return readings.filter(reading => {
      // Check if data type is allowed
      if (!policy.dataTypes.has(reading.sensorType)) {
        return false;
      }

      // Check retention period
      const ageInDays = (Date.now() - reading.timestamp) / (1000 * 60 * 60 * 24);
      if (ageInDays > policy.retentionPeriod) {
        return false;
      }

      // Apply additional privacy filters
      return this.isDataAllowedByPrivacyLevel(reading, policy);
    });
  }

  private async generatePrivateInsights(
    userId: string,
    readings: SensorReading[],
    policy: PrivacyPolicy
  ): Promise<IntelligenceInsight[]> {
    const insights: IntelligenceInsight[] = [];

    // Use federated learning for pattern recognition
    const behaviorPatterns = await this.analyzeBehaviorPatternsFederated(
      userId,
      readings
    );

    // Generate activity insights
    const activityInsights = await this.generateActivityInsights(
      userId,
      readings,
      policy
    );
    insights.push(...activityInsights);

    // Generate environmental insights
    const environmentInsights = await this.generateEnvironmentalInsights(
      userId,
      readings,
      policy
    );
    insights.push(...environmentInsights);

    // Generate wellness insights with privacy preservation
    const wellnessInsights = await this.generateWellnessInsights(
      userId,
      readings,
      policy
    );
    insights.push(...wellnessInsights);

    return insights;
  }

  private async analyzeBehaviorPatternsFederated(
    userId: string,
    readings: SensorReading[]
  ): Promise<any> {
    // Get or create federated model for this user
    let model = this.federatedModels.get(userId);
    if (!model) {
      model = new FederatedModel(userId);
      this.federatedModels.set(userId, model);
    }

    // Train model locally without sharing raw data
    const localUpdate = await model.trainLocal(readings);

    // Participate in federated averaging (privacy-preserving)
    await this.participateInFederatedAveraging(userId, localUpdate);

    return model.getBehaviorPatterns();
  }

  private async generateActivityInsights(
    userId: string,
    readings: SensorReading[],
    policy: PrivacyPolicy
  ): Promise<IntelligenceInsight[]> {
    const insights: IntelligenceInsight[] = [];

    // Analyze movement patterns
    const movementReadings = readings.filter(r => r.sensorType === 'accelerometer');
    if (movementReadings.length > 0) {
      const activityLevel = this.calculateActivityLevel(movementReadings);
      
      insights.push({
        insightId: this.generateInsightId(),
        userId,
        category: 'activity',
        confidence: 0.85,
        actionRecommendation: this.getActivityRecommendation(activityLevel),
        privacyLevel: policy.anonymizationLevel === 'none' ? 'personal' : 'public',
        expirationTime: Date.now() + (24 * 60 * 60 * 1000) // 24 hours
      });
    }

    // Analyze sleep patterns (if permitted)
    if (policy.dataTypes.has('sleep')) {
      const sleepQuality = await this.analyzeSleepPatterns(readings);
      if (sleepQuality) {
        insights.push({
          insightId: this.generateInsightId(),
          userId,
          category: 'sleep',
          confidence: sleepQuality.confidence,
          actionRecommendation: sleepQuality.recommendation,
          privacyLevel: 'personal',
          expirationTime: Date.now() + (7 * 24 * 60 * 60 * 1000) // 7 days
        });
      }
    }

    return insights;
  }

  private async generateEnvironmentalInsights(
    userId: string,
    readings: SensorReading[],
    policy: PrivacyPolicy
  ): Promise<IntelligenceInsight[]> {
    const insights: IntelligenceInsight[] = [];

    // Analyze air quality impact
    const airQualityReadings = readings.filter(r => r.sensorType === 'air_quality');
    if (airQualityReadings.length > 0) {
      const averageAQI = airQualityReadings.reduce((sum, r) => sum + r.value, 0) / airQualityReadings.length;
      
      insights.push({
        insightId: this.generateInsightId(),
        userId,
        category: 'environment',
        confidence: 0.9,
        actionRecommendation: this.getAirQualityRecommendation(averageAQI),
        privacyLevel: 'public', // Environmental data is generally not personal
        expirationTime: Date.now() + (12 * 60 * 60 * 1000) // 12 hours
      });
    }

    // Analyze lighting conditions
    const lightReadings = readings.filter(r => r.sensorType === 'light_sensor');
    if (lightReadings.length > 0) {
      const lightingInsight = await this.analyzeLightingPatterns(lightReadings);
      if (lightingInsight) {
        insights.push({
          insightId: this.generateInsightId(),
          userId,
          category: 'lighting',
          confidence: lightingInsight.confidence,
          actionRecommendation: lightingInsight.recommendation,
          privacyLevel: 'personal',
          expirationTime: Date.now() + (4 * 60 * 60 * 1000) // 4 hours
        });
      }
    }

    return insights;
  }

  private async generateWellnessInsights(
    userId: string,
    readings: SensorReading[],
    policy: PrivacyPolicy
  ): Promise<IntelligenceInsight[]> {
    const insights: IntelligenceInsight[] = [];

    // Only process wellness data if explicitly permitted
    if (!policy.dataTypes.has('biometric')) {
      return insights;
    }

    // Analyze heart rate variability (with privacy protection)
    const heartRateReadings = readings.filter(r => r.sensorType === 'heart_rate');
    if (heartRateReadings.length > 0) {
      const stressLevel = await this.calculateStressLevel(heartRateReadings);
      
      // Apply differential privacy to sensitive health data
      const noisyStressLevel = this.differentialNoiseManager.addNoise(
        stressLevel,
        'wellness',
        policy.anonymizationLevel
      );
      
      insights.push({
        insightId: this.generateInsightId(),
        userId,
        category: 'wellness',
        confidence: Math.max(0.5, 0.9 - this.differentialNoiseManager.getNoiseLevel()),
        actionRecommendation: this.getWellnessRecommendation(noisyStressLevel),
        privacyLevel: 'sensitive',
        expirationTime: Date.now() + (2 * 60 * 60 * 1000) // 2 hours
      });
    }

    return insights;
  }

  private async applyDifferentialPrivacy(
    insights: IntelligenceInsight[],
    policy: PrivacyPolicy
  ): Promise<IntelligenceInsight[]> {
    if (policy.anonymizationLevel === 'none') {
      return insights;
    }

    return insights.map(insight => {
      const privacyBudget = this.calculatePrivacyBudget(insight.privacyLevel);
      
      // Add calibrated noise to confidence scores
      const noisyConfidence = this.differentialNoiseManager.addLaplaceNoise(
        insight.confidence,
        privacyBudget
      );

      return {
        ...insight,
        confidence: Math.max(0, Math.min(1, noisyConfidence)),
        // Remove personally identifiable elements if high privacy required
        actionRecommendation: policy.anonymizationLevel === 'differential' 
          ? this.anonymizeRecommendation(insight.actionRecommendation)
          : insight.actionRecommendation
      };
    });
  }

  private async participateInFederatedAveraging(
    userId: string,
    localUpdate: ModelUpdate
  ): Promise<void> {
    // In a real implementation, this would participate in
    // secure aggregation protocol with other users
    console.log(`User ${userId} participating in federated learning update`);
    
    // Simulate privacy-preserving aggregation
    const aggregationResult = await this.performSecureAggregation(localUpdate);
    
    // Update local model with aggregated results
    const userModel = this.federatedModels.get(userId);
    if (userModel) {
      await userModel.updateWithAggregatedParams(aggregationResult);
    }
  }

  private async performSecureAggregation(update: ModelUpdate): Promise<any> {
    // Implement secure multiparty computation for aggregation
    // This is a simplified placeholder
    return {
      aggregatedWeights: update.weights,
      participantCount: 1,
      privacyBudgetUsed: 0.1
    };
  }

  private calculatePrivacyBudget(privacyLevel: string): number {
    const budgets = {
      'public': 10.0,      // High privacy budget (less noise)
      'personal': 1.0,     // Medium privacy budget
      'sensitive': 0.1,    // Low privacy budget (more noise)
      'restricted': 0.01   // Very low privacy budget (most noise)
    };
    
    return budgets[privacyLevel] || 1.0;
  }

  private anonymizeRecommendation(recommendation?: string): string | undefined {
    if (!recommendation) return undefined;
    
    // Apply k-anonymity and generalization
    return recommendation
      .replace(/d{1,2}:d{2}/g, 'XX:XX') // Remove specific times
      .replace(/d+/g, 'N')               // Replace numbers with 'N'
      .replace(/(Monday|Tuesday|Wednesday|Thursday|Friday|Saturday|Sunday)/g, 'WEEKDAY');
  }

  private async getPrivacyPolicy(userId: string): Promise<PrivacyPolicy | null> {
    return this.privacyPolicies.get(userId) || null;
  }

  private isDataAllowedByPrivacyLevel(
    reading: SensorReading,
    policy: PrivacyPolicy
  ): boolean {
    // Implement privacy level checks
    if (reading.isPersonal && policy.anonymizationLevel === 'none') {
      return false; // Don't process personal data without anonymization
    }
    
    return true;
  }

  async updatePrivacyPolicy(userId: string, policy: Partial<PrivacyPolicy>): Promise<void> {
    const existingPolicy = this.privacyPolicies.get(userId);
    
    const updatedPolicy: PrivacyPolicy = {
      userId,
      dataTypes: policy.dataTypes || existingPolicy?.dataTypes || new Set(),
      retentionPeriod: policy.retentionPeriod || existingPolicy?.retentionPeriod || 30,
      sharingPermissions: policy.sharingPermissions || existingPolicy?.sharingPermissions || new Map(),
      anonymizationLevel: policy.anonymizationLevel || existingPolicy?.anonymizationLevel || 'basic',
      consentTimestamp: Date.now()
    };
    
    this.privacyPolicies.set(userId, updatedPolicy);
    
    // Notify about policy update
    this.emit('policyUpdated', { userId, policy: updatedPolicy });
  }

  // Helper methods
  private calculateActivityLevel(readings: SensorReading[]): number {
    // Simplified activity level calculation
    const totalMovement = readings.reduce((sum, reading) => {
      const magnitude = Math.sqrt(
        reading.value.x * reading.value.x +
        reading.value.y * reading.value.y +
        reading.value.z * reading.value.z
      );
      return sum + magnitude;
    }, 0);
    
    return Math.min(10, totalMovement / readings.length);
  }

  private getActivityRecommendation(activityLevel: number): string {
    if (activityLevel < 3) {
      return 'Consider taking a short walk to increase activity';
    } else if (activityLevel > 8) {
      return 'Great activity level! Consider some recovery time';
    }
    return 'Maintain your current activity level';
  }

  private async calculateStressLevel(heartRateReadings: SensorReading[]): Promise<number> {
    // Simplified stress calculation based on heart rate variability
    if (heartRateReadings.length < 2) return 0;
    
    const hrv = this.calculateHeartRateVariability(heartRateReadings);
    return Math.max(0, Math.min(10, (100 - hrv) / 10));
  }

  private calculateHeartRateVariability(readings: SensorReading[]): number {
    const intervals = readings.slice(1).map((reading, i) => 
      Math.abs(reading.value - readings[i].value)
    );
    
    const meanInterval = intervals.reduce((sum, interval) => sum + interval, 0) / intervals.length;
    return meanInterval;
  }

  private getWellnessRecommendation(stressLevel: number): string {
    if (stressLevel > 7) {
      return 'Consider relaxation techniques or breathing exercises';
    } else if (stressLevel < 3) {
      return 'Stress levels appear normal';
    }
    return 'Monitor stress levels and practice self-care';
  }

  private generateInsightId(): string {
    return crypto.randomBytes(16).toString('hex');
  }

  private initializePrivacyEngine(): void {
    console.log('Privacy-preserving intelligence engine initialized');
  }

  private async logPrivacyCompliantAnalytics(
    userId: string,
    insights: IntelligenceInsight[]
  ): Promise<void> {
    // Log only anonymized analytics
    const anonymizedLog = {
      userHash: crypto.createHash('sha256').update(userId).digest('hex').substring(0, 8),
      insightCount: insights.length,
      categories: [...new Set(insights.map(i => i.category))],
      timestamp: Date.now()
    };
    
    console.log('Privacy-compliant analytics:', anonymizedLog);
  }
}

class DifferentialNoiseManager {
  private epsilon: number = 1.0; // Privacy budget parameter

  addNoise(value: number, category: string, level: string): number {
    const sensitivity = this.getSensitivity(category);
    const scale = sensitivity / this.getEpsilon(level);
    const noise = this.sampleLaplaceNoise(scale);
    return value + noise;
  }

  addLaplaceNoise(value: number, epsilon: number): number {
    const scale = 1.0 / epsilon;
    return value + this.sampleLaplaceNoise(scale);
  }

  private sampleLaplaceNoise(scale: number): number {
    // Sample from Laplace distribution
    const u = Math.random() - 0.5;
    return -scale * Math.sign(u) * Math.log(1 - 2 * Math.abs(u));
  }

  private getSensitivity(category: string): number {
    const sensitivities = {
      'wellness': 2.0,
      'activity': 1.0,
      'environment': 0.5,
      'default': 1.0
    };
    return sensitivities[category] || sensitivities['default'];
  }

  private getEpsilon(level: string): number {
    const epsilons = {
      'none': Infinity,
      'basic': 10.0,
      'advanced': 1.0,
      'differential': 0.1
    };
    return epsilons[level] || 1.0;
  }

  getNoiseLevel(): number {
    return 1.0 / this.epsilon;
  }
}

class FederatedModel {
  private userId: string;
  private localWeights: Map<string, number> = new Map();
  private behaviorPatterns: any = {};

  constructor(userId: string) {
    this.userId = userId;
  }

  async trainLocal(data: SensorReading[]): Promise<ModelUpdate> {
    // Simulate local model training
    const update = {
      weights: new Map(this.localWeights),
      gradients: new Map(),
      sampleCount: data.length
    };

    // Update behavior patterns locally
    this.updateBehaviorPatterns(data);

    return update;
  }

  async updateWithAggregatedParams(aggregationResult: any): Promise<void> {
    // Update local model with federated results
    this.localWeights = new Map(aggregationResult.aggregatedWeights);
  }

  getBehaviorPatterns(): any {
    return this.behaviorPatterns;
  }

  private updateBehaviorPatterns(data: SensorReading[]): void {
    // Update local behavior patterns without sharing raw data
    const activityData = data.filter(r => r.sensorType === 'accelerometer');
    if (activityData.length > 0) {
      this.behaviorPatterns.activity = this.analyzeActivityPatterns(activityData);
    }
  }

  private analyzeActivityPatterns(data: SensorReading[]): any {
    // Local analysis of activity patterns
    return {
      averageActivity: data.reduce((sum, r) => sum + r.value, 0) / data.length,
      peakHours: this.findPeakActivityHours(data),
      lastUpdated: Date.now()
    };
  }

  private findPeakActivityHours(data: SensorReading[]): number[] {
    const hourlyActivity = new Array(24).fill(0);
    
    data.forEach(reading => {
      const hour = new Date(reading.timestamp).getHours();
      hourlyActivity[hour] += reading.value;
    });

    // Return top 3 most active hours
    return hourlyActivity
      .map((activity, hour) => ({ hour, activity }))
      .sort((a, b) => b.activity - a.activity)
      .slice(0, 3)
      .map(item => item.hour);
  }
}

interface ModelUpdate {
  weights: Map<string, number>;
  gradients: Map<string, number>;
  sampleCount: number;
}

export { PrivacyPreservingIntelligence, PrivacyPolicy, SensorReading, IntelligenceInsight };