Activity 11: On-Device AI for Privacy

interface DeviceConstraints {
  availableRAM: number; // MB
  processingPower: number; // relative scale
  batteryCapacity: number; // mAh
  storageSpace: number; // MB available
  thermalLimits: ThermalLimit;
  powerBudget: PowerBudget;
}

interface PrivacySensitiveTask {
  taskType: 'text_analysis' | 'image_recognition' | 'voice_processing' | 'behavior_prediction';
  sensitivityLevel: 'low' | 'medium' | 'high' | 'critical';
  accuracyRequirement: number; // minimum acceptable accuracy
  latencyRequirement: number; // maximum acceptable latency ms
  privacyRequirements: PrivacyRequirement[];
}

class OnDeviceMLEngine {
  constructor(
    private modelOptimizer: ModelOptimizer,
    private deviceProfiler: DeviceProfiler,
    private privacyGuard: PrivacyGuard
  ) {}

  // TODO: Implement model quantization and compression
  async optimizeModelForDevice(
    baseModel: MLModel,
    deviceConstraints: DeviceConstraints,
    accuracyThreshold: number
  ): Promise<OptimizedMLModel> {
    // Quantize model weights to reduce memory usage
    // Prune unnecessary connections and layers
    // Apply knowledge distillation for model compression
    // Optimize for specific device architectures (ARM, GPU)
    // Your implementation here
  }

  // TODO: Create adaptive inference system
  async createAdaptiveInference(
    modelVariants: MLModelVariant[],
    currentDeviceState: DeviceState,
    taskUrgency: TaskUrgency
  ): Promise<AdaptiveInferenceSystem> {
    // Switch between model complexities based on device state
    // Use lighter models when battery is low
    // Employ progressive inference for better user experience
    // Cache frequent predictions to reduce computation
    // Your implementation here
  }

  // TODO: Privacy-preserving feature extraction
  async extractPrivacyPreservingFeatures(
    rawData: SensitiveData,
    taskRequirements: PrivacySensitiveTask,
    privacyBudget: PrivacyBudget
  ): Promise<PrivateFeatureVector> {
    // Apply differential privacy to feature extraction
    // Use homomorphic encryption for sensitive computations
    // Implement secure multi-party computation where needed
    // Ensure no raw data leakage in processed features
    // Your implementation here
  }
}

class FederatedLearningSystem {
  // TODO: Secure aggregation protocol
  async implementSecureAggregation(
    localModelUpdates: LocalModelUpdate[],
    aggregationProtocol: AggregationProtocol,
    privacyParameters: FederatedPrivacyParams
  ): Promise<AggregatedModelUpdate> {
    // Cryptographically secure model parameter aggregation
    // Differential privacy in gradient sharing
    // Byzantine-robust aggregation against malicious participants
    // Zero-knowledge proofs for model update authenticity
    // Your implementation here
  }

  // TODO: Local training with privacy guarantees
  async trainLocalModelPrivately(
    localData: LocalUserData,
    globalModel: GlobalModel,
    privacyConstraints: PrivacyConstraint[]
  ): Promise<PrivateLocalModelUpdate> {
    // Train only on user device, never expose raw data
    // Apply differential privacy to gradient computations
    // Use secure aggregation protocols
    // Implement client-side data validation and cleaning
    // Your implementation here
  }

  // TODO: Decentralized model coordination
  async coordinateDecentralizedLearning(
    peerDevices: PeerDevice[],
    learningObjectives: LearningObjective[],
    trustNetwork: TrustNetwork
  ): Promise<DecentralizedLearningNetwork> {
    // Peer-to-peer model sharing without central authority
    // Reputation-based trust system for model quality
    // Byzantine fault tolerance in distributed training
    // Cultural bias detection and mitigation across regions
    // Your implementation here
  }
}

interface PersonalizationContext {
  culturalBackground: CulturalBackground;
  languagePreferences: LanguagePreference[];
  accessibilityNeeds: AccessibilityNeed[];
  usagePatterns: UsagePattern[];
  privacyPreferences: PrivacyPreference[];
  sensitivityLevel: DataSensitivityLevel;
}

class PrivatePersonalizationEngine {
  // TODO: Local preference learning
  async learnUserPreferencesLocally(
    userInteractions: UserInteraction[],
    personalizationGoals: PersonalizationGoal[],
    privacyBudget: PrivacyBudget
  ): Promise<LocalUserModel> {
    // Learn patterns without storing personal identifiers
    // Use differential privacy in preference extraction
    // Implement forgetting mechanisms for outdated preferences
    // Respect user control over personalization level
    // Your implementation here
  }

  // TODO: Culturally sensitive adaptation
  async adaptCulturallyAndPrivately(
    userCulturalContext: PersonalizationContext,
    availableAdaptations: CulturalAdaptation[],
    privacyConstraints: PrivacyConstraint[]
  ): Promise<CulturallyAdaptedExperience> {
    // Personalize without cultural stereotyping
    // Respect individual variations within cultural groups
    // Protect cultural identity information from exposure
    // Enable user control over cultural personalization
    // Your implementation here
  }

  // TODO: Anonymous recommendation system
  async generateAnonymousRecommendations(
    localUserModel: LocalUserModel,
    availableContent: Content[],
    similarityMetrics: AnonymousSimilarityMetric[]
  ): Promise<PrivateRecommendations> {
    // Generate recommendations without revealing user identity
    // Use homomorphic encryption for similarity computations
    // Implement k-anonymity in recommendation generation
    // Provide transparency in recommendation reasoning
    // Your implementation here
  }
}

class PrivateVoiceProcessing {
  // TODO: On-device speech recognition
  async processVoiceLocally(
    audioInput: AudioInput,
    languageModels: LocalLanguageModel[],
    privacySettings: VoicePrivacySettings
  ): Promise<PrivateSpeechResult> {
    // Convert speech to text entirely on device
    // Support multiple languages and accents
    // Handle background noise and poor audio quality
    // Never store or transmit raw audio data
    // Your implementation here
  }

  // TODO: Voice biometric protection
  async protectVoiceBiometrics(
    voiceFeatures: VoiceFeature[],
    biometricProtocol: BiometricProtectionProtocol,
    anonymizationLevel: AnonymizationLevel
  ): Promise<ProtectedVoiceProcessing> {
    // Extract useful features while protecting voice print
    // Use voice conversion techniques for anonymization
    // Implement secure voice authentication without storage
    // Protect against voice replay and deepfake attacks
    // Your implementation here
  }

  // TODO: Multilingual voice understanding
  async processMultilingualVoice(
    multilingualInput: MultilingualAudioInput,
    culturalContext: CulturalVoiceContext[],
    privacyRequirements: MultilingualPrivacyRequirement[]
  ): Promise<CulturallyAwareVoiceResult> {
    // Understand code-switching between languages
    // Respect cultural voice communication patterns
    // Handle dialects and regional accents privately
    // Protect linguistic minority language patterns
    // Your implementation here
  }
}

class PrivateComputerVision {
  // TODO: Local image analysis and recognition
  async analyzeImagesLocally(
    imageInput: ImageInput,
    analysisRequirements: ImageAnalysisRequirement[],
    privacyProtections: ImagePrivacyProtection[]
  ): Promise<PrivateImageAnalysis> {
    // Object detection and recognition on device
    // Facial analysis without facial recognition storage
    // Text extraction and OCR locally
    // Scene understanding and context analysis
    // Your implementation here
  }

  // TODO: Differential privacy in image processing
  async applyDifferentialPrivacyToVision(
    imageFeatures: ImageFeature[],
    privacyBudget: PrivacyBudget,
    utilityRequirements: UtilityRequirement[]
  ): Promise<PrivateImageFeatures> {
    // Add calibrated noise to protect individual privacy
    // Maintain utility for aggregate analysis
    // Protect against membership inference attacks
    // Enable privacy-utility trade-off control
    // Your implementation here
  }

  // TODO: Secure visual content filtering
  async filterContentSecurely(
    visualContent: VisualContent[],
    contentPolicies: ContentPolicy[],
    culturalSensitivities: CulturalContentSensitivity[]
  ): Promise<SecureContentFiltering> {
    // Filter harmful content without exposing content to servers
    // Respect cultural differences in content appropriateness
    // Protect user privacy while ensuring safety
    // Enable user customization of filtering levels
    // Your implementation here
  }
}

class SecureLocalDataManager {
  // TODO: Zero-knowledge local storage
  async implementZeroKnowledgeStorage(
    sensitiveData: SensitiveUserData,
    encryptionKeys: EncryptionKey[],
    accessControlPolicies: AccessControlPolicy[]
  ): Promise<ZeroKnowledgeStorage> {
    // Client-side encryption with user-controlled keys
    // Forward secrecy for evolving data
    // Secure key derivation and management
    // Protection against physical device access
    // Your implementation here
  }

  // TODO: Secure data deletion and forgetting
  async implementSecureDataDeletion(
    dataToDelete: DataIdentifier[],
    deletionVerification: DeletionVerificationMethod,
    complianceRequirements: DataComplianceRequirement[]
  ): Promise<SecureDeletionResult> {
    // Cryptographically secure data deletion
    // Verification of complete data removal
    // Compliance with right-to-be-forgotten regulations
    // Secure handling of data remnants and caches
    // Your implementation here
  }

  // TODO: Decentralized backup without exposure
  async createPrivateBackupSystem(
    criticalData: CriticalUserData,
    backupStrategy: PrivateBackupStrategy,
    recoveryMechanisms: RecoveryMechanism[]
  ): Promise<PrivateBackupSystem> {
    // Distributed backup without exposing data to any single party
    // Secret sharing for critical data recovery
    // End-to-end encrypted cloud integration
    // Peer-to-peer backup networks
    // Your implementation here
  }
}

class PrivateAnalyticsEngine {
  // TODO: Differential privacy analytics
  async generateDifferentialPrivateInsights(
    userBehaviorData: UserBehaviorData[],
    analyticsQueries: AnalyticsQuery[],
    privacyBudgetAllocation: PrivacyBudgetAllocation
  ): Promise<PrivateAnalyticsResults> {
    // Generate population-level insights with privacy guarantees
    // Budget privacy expenditure across different analyses
    // Protect against database reconstruction attacks
    // Provide utility-privacy trade-off transparency
    // Your implementation here
  }

  // TODO: Federated analytics coordination
  async coordinateFederatedAnalytics(
    distributedDataSources: DistributedDataSource[],
    aggregationProtocols: SecureAggregationProtocol[],
    verificationMechanisms: VerificationMechanism[]
  ): Promise<FederatedAnalyticsResults> {
    // Coordinate analytics across multiple devices
    // Verify analytical results without exposing individual data
    // Detect and handle malicious participants
    // Maintain analytical accuracy while preserving privacy
    // Your implementation here
  }

  // TODO: Privacy-preserving A/B testing
  async conductPrivateABTesting(
    testVariants: TestVariant[],
    userSegments: AnonymousUserSegment[],
    effectMeasurements: PrivateEffectMeasurement[]
  ): Promise<PrivateABTestResults> {
    // Test product changes without exposing individual responses
    // Ensure statistical validity with privacy constraints
    // Handle selection bias in privacy-preserving manner
    // Enable ethical experimentation on vulnerable populations
    // Your implementation here
  }
}