ai-debug-local-mcp/dist/utils/ai-feedback-privacy-aggregator.d.ts

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/**
 * AI Feedback Privacy Aggregator
 *
 * Privacy-preserving data aggregation for AI feedback collection that enables
 * valuable analytics while protecting user privacy through differential privacy,
 * k-anonymity, and data minimization techniques.
 */
import { AIFeedbackEntry } from './ai-feedback-collector.js';
export interface PrivacyConfig {
    enableDifferentialPrivacy: boolean;
    epsilonValue: number;
    kAnonymityThreshold: number;
    enableDataMinimization: boolean;
    retentionPeriodDays: number;
    allowedAggregations: ('count' | 'average' | 'distribution' | 'trends')[];
}
export interface AggregatedMetrics {
    timestamp: number;
    aggregationType: string;
    privacyLevel: 'minimal' | 'standard' | 'strict';
    metrics: {
        totalSessions: number;
        averageSatisfaction: number;
        satisfactionDistribution: Record<string, number>;
        agentPerformance: Record<string, {
            usageCount: number;
            averageRating: number;
            anonymizedFeedback: string[];
        }>;
        frameworkMetrics: Record<string, {
            sessionCount: number;
            averageComplexity: string;
            successRate: number;
        }>;
        trendIndicators: {
            satisfactionTrend: 'improving' | 'stable' | 'declining';
            usageTrend: 'increasing' | 'stable' | 'decreasing';
            qualityTrend: 'improving' | 'stable' | 'declining';
        };
    };
    privacyMetadata: {
        noiseAdded: boolean;
        kAnonymityMet: boolean;
        dataMinimized: boolean;
        originalEntryCount: number;
        aggregatedEntryCount: number;
    };
}
export interface PrivacyReport {
    complianceLevel: 'compliant' | 'warning' | 'violation';
    privacyRisks: string[];
    recommendedActions: string[];
    dataRetentionStatus: {
        totalEntries: number;
        expiredEntries: number;
        retainedEntries: number;
    };
    anonymizationEffectiveness: {
        score: number;
        vulnerabilities: string[];
        improvements: string[];
    };
}
/**
 * Privacy-Preserving AI Feedback Aggregator
 */
export declare class AIFeedbackPrivacyAggregator {
    private config;
    private privacyBudgetUsed;
    private aggregationHistory;
    constructor(config?: Partial<PrivacyConfig>);
    /**
     * Aggregate feedback data with privacy preservation
     */
    aggregateWithPrivacy(feedbackData: AIFeedbackEntry[], aggregationType?: 'daily' | 'weekly' | 'monthly'): Promise<AggregatedMetrics>;
    /**
     * Generate privacy compliance report
     */
    generatePrivacyReport(feedbackData: AIFeedbackEntry[]): PrivacyReport;
    /**
     * Export privacy-compliant dataset
     */
    exportPrivacyCompliantDataset(feedbackData: AIFeedbackEntry[], exportLevel?: 'public' | 'research' | 'internal'): {
        dataset: any[];
        privacyGuarantees: string[];
        limitations: string[];
    };
    private applyPrivacyFilters;
    private checkKAnonymity;
    private applyDataMinimization;
    private calculateBaseMetrics;
    private addDifferentialPrivacyNoise;
    private generateLaplaceNoise;
    private hashValue;
    private generalizeTextArray;
    private getSatisfactionBucket;
    private groupByAgent;
    private groupByFramework;
    private getMostCommonComplexity;
    private extractAnonymizedFeedback;
    private determinePrivacyLevel;
    private updatePrivacyBudget;
    private assessReidentificationRisk;
    private calculateAnonymizationScore;
    private applyMaximalPrivacy;
    private applyResearchPrivacy;
    private applyInternalPrivacy;
}
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