62:I[707087,["/_next/static/immutable/chunks/3arlv5bvukoo2.js","/_next/static/immutable/chunks/39v64hmfyo-sr.js","/_next/static/immutable/chunks/2nvof3-29tvc3.js","/_next/static/immutable/chunks/3tjobt77z_9dj.js","/_next/static/immutable/chunks/04j_o97cgxn2x.js","/_next/static/immutable/chunks/3upr08c9vtj8w.js","/_next/static/immutable/chunks/1ww0328si-u8x.js","/_next/static/immutable/chunks/11mfl5ymqld9-.js","/_next/static/immutable/chunks/2xxcqy6ppds3l.js","/_next/static/immutable/chunks/1bpgyrs5qflhu.js","/_next/static/immutable/chunks/0-6vu2ul1h6ld.js","/_next/static/immutable/chunks/43_s7jweyypoh.js","/_next/static/immutable/chunks/0p6vao205o1jc.js","/_next/static/immutable/chunks/1_kyvgkyokw92.js","/_next/static/immutable/chunks/1lo-zn37dolb5.js","/_next/static/immutable/chunks/1v8k6yxw1rg0u.js"],"RegistryDependencies"] 63:I[314491,["/_next/static/immutable/chunks/3arlv5bvukoo2.js","/_next/static/immutable/chunks/39v64hmfyo-sr.js","/_next/static/immutable/chunks/2nvof3-29tvc3.js","/_next/static/immutable/chunks/3tjobt77z_9dj.js","/_next/static/immutable/chunks/04j_o97cgxn2x.js","/_next/static/immutable/chunks/3upr08c9vtj8w.js","/_next/static/immutable/chunks/1ww0328si-u8x.js","/_next/static/immutable/chunks/11mfl5ymqld9-.js","/_next/static/immutable/chunks/2xxcqy6ppds3l.js","/_next/static/immutable/chunks/1bpgyrs5qflhu.js","/_next/static/immutable/chunks/0-6vu2ul1h6ld.js","/_next/static/immutable/chunks/43_s7jweyypoh.js","/_next/static/immutable/chunks/0p6vao205o1jc.js","/_next/static/immutable/chunks/1_kyvgkyokw92.js","/_next/static/immutable/chunks/1lo-zn37dolb5.js","/_next/static/immutable/chunks/1v8k6yxw1rg0u.js"],"FilesView"] 64:T819e,'use client' import * as React from 'react' import { Check, Code2, Cpu, Database, Download, FileSpreadsheet, Info, Layers, Lock, Plus, RefreshCw, ShieldCheck, Sliders, Sparkles, Trash2, } from 'lucide-react' import { cn } from '@/lib/utils' import { Badge } from '@/components/ui/badge' import { Button } from '@/components/ui/button' import { Card, CardContent, CardDescription, CardHeader, CardTitle } from '@/components/ui/card' import { Input } from '@/components/ui/input' import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from '@/components/ui/select' import { Separator } from '@/components/ui/separator' import { Slider } from '@/components/ui/slider' import { Switch } from '@/components/ui/switch' import type { FieldDataType, FidelityMetrics, GeneratedRecord, SchemaField, SyntheticDataGeneratorProps, TemplatePreset, } from './synthetic-data-types' import { boxMullerRandom as boxMullerRandomHelper, CARD_BRANDS, FIRST_NAMES, LAST_NAMES, TEMPLATES, } from './synthetic-data-templates' import { SyntheticDataPreview } from './SyntheticDataPreview' export type { FieldDataType, FidelityMetrics, GeneratedRecord, SchemaField, SyntheticDataGeneratorProps, TemplatePreset, } export function SyntheticDataGenerator({ className, initialTemplate = 'customer-transactions', initialRowCount = 1000, initialEpsilon = 1.2, }: SyntheticDataGeneratorProps) { // --- STATE --- const [selectedTemplateKey, setSelectedTemplateKey] = React.useState(initialTemplate) const [outputFormat, setOutputFormat] = React.useState<'csv' | 'jsonl'>('csv') const [rowCount, setRowCount] = React.useState(initialRowCount) const [epsilon, setEpsilon] = React.useState(initialEpsilon) const [kAnonymity, setKAnonymity] = React.useState(true) const [saltedHash, setSaltedHash] = React.useState(true) const [noiseMechanism, setNoiseMechanism] = React.useState(true) const [activeFields, setActiveFields] = React.useState(() => { return TEMPLATES[initialTemplate]?.fields ? JSON.parse(JSON.stringify(TEMPLATES[initialTemplate].fields)) : [] }) const [isGenerating, setIsGenerating] = React.useState(false) const [copied, setCopied] = React.useState(false) const [downloadToast, setDownloadToast] = React.useState(null) const [previewTab, setPreviewTab] = React.useState<'table' | 'json' | 'metrics'>('table') const [generationSeed, setGenerationSeed] = React.useState(48291) // Field addition state const [showAddField, setShowAddField] = React.useState(false) const [newFieldName, setNewFieldName] = React.useState('') const [newFieldType, setNewFieldType] = React.useState('gaussian_float') // Handle template selection switch const handleTemplateChange = (key: string) => { setSelectedTemplateKey(key) const tpl = TEMPLATES[key] if (tpl) { setActiveFields(JSON.parse(JSON.stringify(tpl.fields))) triggerRegeneration() } } // Generation trigger const triggerRegeneration = () => { setIsGenerating(true) setGenerationSeed(Math.floor(Math.random() * 90000) + 10000) setTimeout(() => { setIsGenerating(false) }, 450) } // Box-Muller Gaussian random generator with differential privacy noise const boxMullerRandom = (mean: number, stdDev: number, seedOffset: number): number => { return boxMullerRandomHelper(mean, stdDev, seedOffset, generationSeed, noiseMechanism, epsilon) } // Dynamic preview records computation const previewRecords = React.useMemo(() => { const rows: GeneratedRecord[] = [] const count = 6 for (let i = 0; i < count; i++) { const fn = FIRST_NAMES[(i * 3 + generationSeed) % FIRST_NAMES.length] const ln = LAST_NAMES[(i * 5 + generationSeed + 2) % LAST_NAMES.length] const fullName = `${fn} ${ln}` const baseSlug = `${fn.charAt(0).toLowerCase()}.${ln.toLowerCase().replace(/[^a-z]/g, '')}` const record: GeneratedRecord = { id: saltedHash ? `syn_${((generationSeed * 9301 + i * 49297) % 233280).toString(16).padStart(6, '0')}` : `usr_${1000 + i}`, } for (const field of activeFields) { if (field.name.includes('id') || field.name.includes('uuid') || field.name.includes('token')) { record[field.name] = record.id continue } switch (field.type) { case 'name': record[field.name] = fullName break case 'email': { const dom = field.params.domain || 'synthetic-vault.io' record[field.name] = `${baseSlug}@${dom}` break } case 'credit_card': { const last4 = (((generationSeed * 7 + i * 1337 + 1000) % 9000) + 1000).toString() const brand = CARD_BRANDS[i % CARD_BRANDS.length] record[field.name] = `${brand} •••• ${last4}` break } case 'gaussian_float': { const m = field.params.mean ?? 100 const s = field.params.stdDev ?? 20 const rawVal = boxMullerRandom(m, s, i) const val = Math.max(m * 0.1, rawVal) if (field.params.currency) { record[field.name] = `${field.params.currency}${val.toFixed(2)}` } else if (field.params.unit) { record[field.name] = `${val.toFixed(1)} ${field.params.unit}` } else { record[field.name] = parseFloat(val.toFixed(2)) } break } case 'category_weights': { const cats = field.params.categories || ['Standard', 'Premium'] const catIndex = (i + Math.floor(generationSeed / 100)) % cats.length record[field.name] = cats[catIndex] break } case 'date_range': { const day = ((10 + i * 4) % 28) + 1 const month = (i % 8) + 1 const dayStr = day.toString().padStart(2, '0') const monthStr = month.toString().padStart(2, '0') const hour = (8 + i * 3) % 24 const hourStr = hour.toString().padStart(2, '0') record[field.name] = `2025-${monthStr}-${dayStr} ${hourStr}:24:18 UTC` break } default: record[field.name] = `value_${i}` } } rows.push(record) } return rows }, [activeFields, generationSeed, saltedHash, noiseMechanism, epsilon]) // Statistical fidelity metrics const fidelityMetrics = React.useMemo(() => { const rawSimilarity = Math.min(99.4, Math.max(82.0, 99.2 - ((5.0 - epsilon) / 4.9) * 9.6)) const similarity = parseFloat(rawSimilarity.toFixed(1)) const leakageRisk = kAnonymity && saltedHash && epsilon <= 3.5 ? 0.0 : Math.max(0, (epsilon - 3.5) * 0.04) const correlationRetention = parseFloat(Math.min(99.1, 98.6 - ((5.0 - epsilon) / 4.9) * 6.2).toFixed(1)) const wassersteinDistance = parseFloat((0.032 + ((5.0 - epsilon) / 4.9) * 0.024).toFixed(3)) const laplaceNoiseStd = (0.12 / Math.max(0.1, epsilon)).toFixed(3) let privacyTier = 'Balanced Privacy & Utility' let privacyBadgeColor = 'bg-info/10 text-info border-info/30' if (epsilon <= 0.5) { privacyTier = 'Maximum Privacy / High DP Noise' privacyBadgeColor = 'bg-success/10 text-success border-success/30' } else if (epsilon > 2.5) { privacyTier = 'High Statistical Utility / Minimal Noise' privacyBadgeColor = 'bg-warning/10 text-warning border-warning/30' } return { similarity, leakageRisk: leakageRisk.toFixed(2), correlationRetention, wassersteinDistance, laplaceNoiseStd, privacyTier, privacyBadgeColor, } }, [epsilon, kAnonymity, saltedHash]) // Estimated file size calculation const estimatedFileSize = React.useMemo(() => { const bytesPerRow = outputFormat === 'csv' ? 180 : 380 const totalKb = (rowCount * bytesPerRow) / 1024 if (totalKb < 1024) { return `~${totalKb.toFixed(1)} KB` } return `~${(totalKb / 1024).toFixed(2)} MB` }, [outputFormat, rowCount]) // Add field handler const handleAddCustomField = () => { if (!newFieldName.trim()) return const cleanName = newFieldName.trim().toLowerCase().replace(/\s+/g, '_') const newField: SchemaField = { id: `f-custom-${Date.now()}`, name: cleanName, label: newFieldName.trim(), type: newFieldType, description: `Custom ${newFieldType} schema column`, params: newFieldType === 'gaussian_float' ? { mean: 500, stdDev: 120, currency: '$' } : newFieldType === 'category_weights' ? { categories: ['Alpha', 'Beta', 'Gamma'], weights: [0.5, 0.3, 0.2] } : {}, } setActiveFields((prev) => [...prev, newField]) setNewFieldName('') setShowAddField(false) triggerRegeneration() } // Remove field handler const removeField = (fieldId: string) => { if (activeFields.length <= 2) return setActiveFields((prev) => prev.filter((f) => f.id !== fieldId)) triggerRegeneration() } // Copy JSON lines const copyJsonLines = async () => { try { const jsonLinesText = previewRecords.map((r) => JSON.stringify(r)).join('\n') if (navigator?.clipboard?.writeText) { await navigator.clipboard.writeText(jsonLinesText) } setCopied(true) setTimeout(() => { setCopied(false) }, 2000) } catch (err) { console.error('Failed to copy to clipboard', err) } } // Download simulation const handleDownloadDataset = () => { const filename = `synthetic_${selectedTemplateKey}_${rowCount}_rows.${outputFormat === 'csv' ? 'csv' : 'jsonl'}` setDownloadToast(`Downloaded ${rowCount.toLocaleString()} synthetic records (${filename})`) setTimeout(() => { setDownloadToast(null) }, 3500) } return (

{/* TOP HEADER HERO */}

Synthetic Dataset Generator & Privacy Engine Differential Privacy Enabled

Mathematical & LLM-guided synthetic tabular dataset synthesizer with configurable (ε, δ)-DP noise injection.

{/* Header Right Quick Actions */}

{/* Format Selector Segmented Control */}

{/* Download Button */} {/* Primary Generate Batch Button */}

{/* Template Selector Bar */}

Schema Template:

{TEMPLATES[selectedTemplateKey]?.category} • {activeFields.length} fields configured

{/* DOWNLOAD SUCCESS NOTIFICATION */} {downloadToast && (

{downloadToast}

{estimatedFileSize}

)} {/* 2-COLUMN STUDIO GENERATOR */}

{/* LEFT COLUMN: SCHEMA & PRIVACY CONFIGURATION (40% / 5 Cols) */}

{/* CARD 1: ROW COUNT & BATCH SCALE */}

Dataset Generation Volume

{rowCount.toLocaleString()} rows

{/* Row Slider */}

100 rows Estimated: {estimatedFileSize} 50,000 rows

setRowCount(val[0])} />

{/* Quick Preset Chips */}

Presets: {[100, 1000, 5000, 10000, 50000].map((preset) => ( ))}

{/* CARD 2: DIFFERENTIAL PRIVACY EPSILON (ε) CONTROLS */}

Differential Privacy Budget Laplace (ε, δ)-DP noise calibration

ε = {epsilon.toFixed(1)}

{/* Epsilon Slider */}

ε=0.1 (High Privacy) ε=5.0 (High Fidelity)

setEpsilon(val[0])} />

{/* Epsilon Interpretation Banner */}

{fidelityMetrics.privacyTier}

Laplace noise scale:{' '} σ=±{fidelityMetrics.laplaceNoiseStd}{' '} • Delta: δ=10⁻⁵ • Zero memorization guarantee.

{/* Quick Epsilon Presets */}

{[ { eps: 0.5, label: 'Healthcare' }, { eps: 1.2, label: 'Standard' }, { eps: 3.0, label: 'Analytics' }, ].map(({ eps, label }) => ( ))}

{/* CARD 3: FIELD SCHEMAS GENERATOR */}

Schema Fields Generator {activeFields.length} active synthesizer distributions

{/* Add Field Form */} {showAddField && (

Configure New Synthetic Field

Field Identifier setNewFieldName(e.target.value)} placeholder="e.g. account_balance" className="h-8 text-xs" />

Data Distribution Type

)} {/* Fields List */}

{activeFields.map((field) => (

{field.name} {field.type.replace('_', ' ')}

{field.description || 'Configured synthesizer parameter'}

))}

{/* CARD 4: PII ANONYMIZATION POLICY SWITCHES */}

PII Anonymization Policy Zero linkage & re-identification guarantees

{/* K-Anonymity Switch */}

K-Anonymity (k = 5 Cohort Enforcement)

Generalizes quasi-identifiers so each demographic bucket contains ≥ 5 records.

{/* Salted Hash Switch */}

Salted HMAC-SHA256 for Primary Keys

Deterministically pseudonymizes primary keys with ephemeral cryptographic salt.

{/* Noise Injection Switch */}

Differential Privacy Gaussian Noise

Injects calibrated noise calibrated to global sensitivity (Δf / ε).

{/* RIGHT COLUMN: LIVE GENERATED SYNTHETIC DATA PREVIEW (60% / 7 Cols) */}

) } export default SyntheticDataGenerator 65:T3bbe,'use client' import * as React from 'react' import { BarChart3, Binary, Check, Code2, Copy, Shield, ShieldCheck, Table as TableIcon, Zap } from 'lucide-react' import { Badge } from '@/components/ui/badge' import { Button } from '@/components/ui/button' import { Card, CardContent, CardDescription, CardFooter, CardHeader, CardTitle } from '@/components/ui/card' import { Progress } from '@/components/ui/progress' import { Table, TableBody, TableCell, TableHead, TableHeader, TableRow } from '@/components/ui/table' import { Tabs, TabsList, TabsTrigger } from '@/components/ui/tabs' import type { FidelityMetrics, GeneratedRecord, SchemaField } from './synthetic-data-types' interface SyntheticDataPreviewProps { previewTab: 'table' | 'json' | 'metrics' onPreviewTabChange: (tab: 'table' | 'json' | 'metrics') => void previewRecords: GeneratedRecord[] activeFields: SchemaField[] fidelityMetrics: FidelityMetrics epsilon: number rowCount: number outputFormat: 'csv' | 'jsonl' generationSeed: number copied: boolean onCopyJsonLines: () => void } export function SyntheticDataPreview({ previewTab, onPreviewTabChange, previewRecords, activeFields, fidelityMetrics, epsilon, rowCount, outputFormat, generationSeed, copied, onCopyJsonLines, }: SyntheticDataPreviewProps) { return (

{/* TOP GAUGE BANNER: STATISTICAL FIDELITY VS PRIVACY */}

{fidelityMetrics.similarity}% Distribution Similarity • {fidelityMetrics.leakageRisk}% PII Leakage Risk

ε-DP Bound: ≤ {epsilon.toFixed(1)}

{/* Metric 1: Fidelity Score */}

Fidelity Score

{fidelityMetrics.similarity}%

{/* Metric 2: Correlation Preservation */}

Covariance Retention

{fidelityMetrics.correlationRetention}%

{/* Metric 3: Wasserstein Distance */}

Wasserstein Distance

{fidelityMetrics.wassersteinDistance}

Low error bound (< 0.05)

{/* PREVIEW TABS CARD */}

Live Generated Synthetic Data Preview Real-time sampler showing first {previewRecords.length} synthesized records

{/* View Switcher */} onPreviewTabChange(val as 'table' | 'json' | 'metrics')} className="w-auto" > Table JSONL Marginals

{/* TAB 1: TABULAR PREVIEW */} {previewTab === 'table' && (

{activeFields.map((f) => ( {f.name} ))} {previewRecords.map((row, idx) => ( {activeFields.map((f) => ( {f.name === 'id' || f.name.includes('id') || f.name.includes('uuid') || f.name.includes('token') ? ( {row[f.name]} ) : f.type === 'category_weights' ? ( {row[f.name]} ) : f.type === 'gaussian_float' ? ( {row[f.name]} ) : f.type === 'credit_card' ? ( {row[f.name]} ) : f.type === 'email' ? ( {row[f.name]} ) : ( {row[f.name]} )} ))} ))}

Showing {previewRecords.length} sample records of {rowCount.toLocaleString()} generated rows Seed: #{generationSeed}

)} {/* TAB 2: JSON LINES PREVIEW */} {previewTab === 'json' && (

Streaming JSON Lines formatted output:

{previewRecords.map((rec, idx) => (

{idx + 1} {'{ '} {Object.entries(rec).map(([key, val], kIdx, arr) => ( "{key}" : {typeof val === 'number' ? val : `"${val}"`} {kIdx < arr.length - 1 && , } ))} {' }'}

))}

)} {/* TAB 3: MARGINALS & FIDELITY DENSITY */} {previewTab === 'metrics' && (

{/* Density Gauge 1 */}

Gaussian Distribution Overlap 96.2%

Kolmogorov-Smirnov distance between synthetic batch and continuous Gaussian ground truth.

{/* Density Gauge 2 */}

Categorical Frequency Prior 98.4%

Total variation distance across categorical weight assignments.

{/* Mathematical Guarantees Breakdown */}

Mathematical Privacy Guarantees • Formal Specs

Differential Privacy (ε = {epsilon.toFixed(1)}, δ = 10⁻⁵)

K-Anonymity (k = 5) Cohort Clamping

Zero Direct Row Memorization

Ephemeral In-Memory Compute

)} Powered by LLM Semantic Synthesis & Laplace Noise Engine Output: {outputFormat.toUpperCase()} • {activeFields.length} fields • {rowCount.toLocaleString()}{' '} records

) } 66:T405,export type FieldDataType = 'name' | 'email' | 'credit_card' | 'gaussian_float' | 'date_range' | 'category_weights' export interface SchemaField { id: string name: string label: string type: FieldDataType description?: string params: { mean?: number stdDev?: number currency?: string unit?: string categories?: string[] weights?: number[] domain?: string dateStart?: string dateEnd?: string } } export interface TemplatePreset { id: string name: string description: string category: string fields: SchemaField[] } export interface GeneratedRecord { id: string [key: string]: string | number } export interface FidelityMetrics { similarity: number leakageRisk: string correlationRetention: number wassersteinDistance: number laplaceNoiseStd: string privacyTier: string privacyBadgeColor: string } export interface SyntheticDataGeneratorProps { className?: string initialTemplate?: string initialRowCount?: number initialEpsilon?: number } 67:T23c6,import type { TemplatePreset } from './synthetic-data-types' export const TEMPLATES: Record = { 'customer-transactions': { id: 'customer-transactions', name: 'Customer Transactions & Demographics Dataset', description: 'High-dimensional e-commerce customer cohort, synthetic PII, spending distribution, and country codes.', category: 'E-Commerce & Retail', fields: [ { id: 'f-id', name: 'cust_id', label: 'Customer ID', type: 'name', description: 'Salted pseudonymized primary key', params: {}, }, { id: 'f-name', name: 'full_name', label: 'Full Name', type: 'name', description: 'Synthetic person name generator', params: {}, }, { id: 'f-email', name: 'email_address', label: 'Synthetic Email', type: 'email', description: 'Faker email with domain isolation', params: { domain: 'syn-corp.io' }, }, { id: 'f-card', name: 'masked_card', label: 'Card PAN', type: 'credit_card', description: 'Luhn-compliant masked payment token', params: {}, }, { id: 'f-spend', name: 'spending_amount', label: 'Spending Amount', type: 'gaussian_float', description: 'Gaussian spending curve (μ=$342.50, σ=$85.20)', params: { mean: 342.5, stdDev: 85.2, currency: '$' }, }, { id: 'f-country', name: 'country_code', label: 'Country Region', type: 'category_weights', description: 'Categorical distribution [US, DE, GB, FR, JP]', params: { categories: ['US', 'DE', 'GB', 'FR', 'JP'], weights: [0.45, 0.2, 0.15, 0.1, 0.1], }, }, { id: 'f-created', name: 'created_at', label: 'Registration Date', type: 'date_range', description: 'ISO-8601 timestamp range (2024-2025)', params: { dateStart: '2024-01-01', dateEnd: '2025-08-20' }, }, ], }, 'healthcare-ehr': { id: 'healthcare-ehr', name: 'Healthcare Patient Clinical Records (EHR)', description: 'HIPAA-compliant patient cohort with biomarker telemetry, blood pressure, and triage severity tiers.', category: 'Healthcare & Life Sciences', fields: [ { id: 'f-h-id', name: 'patient_id', label: 'Patient ID', type: 'name', description: 'Deterministic HIPAA salted identifier', params: {}, }, { id: 'f-h-name', name: 'patient_name', label: 'Patient Name', type: 'name', description: 'Synthetic medical record moniker', params: {}, }, { id: 'f-h-email', name: 'contact_email', label: 'Contact Email', type: 'email', description: 'Synthetic provider contact', params: { domain: 'med-synthetic.org' }, }, { id: 'f-h-glucose', name: 'blood_glucose', label: 'Glucose (mg/dL)', type: 'gaussian_float', description: 'Fast fasting blood glucose (μ=108.4, σ=22.1)', params: { mean: 108.4, stdDev: 22.1, unit: 'mg/dL' }, }, { id: 'f-h-bp', name: 'systolic_bp', label: 'Systolic BP', type: 'gaussian_float', description: 'Arterial blood pressure (μ=124.0, σ=15.5)', params: { mean: 124.0, stdDev: 15.5, unit: 'mmHg' }, }, { id: 'f-h-tier', name: 'triage_tier', label: 'Triage Severity', type: 'category_weights', description: 'Clinical priority classification', params: { categories: ['Routine', 'Elevated', 'Urgent', 'Critical'], weights: [0.52, 0.28, 0.14, 0.06], }, }, { id: 'f-h-date', name: 'admission_date', label: 'Admission Date', type: 'date_range', description: 'Encounter timestamps', params: { dateStart: '2024-06-01', dateEnd: '2025-08-20' }, }, ], }, 'fintech-fraud': { id: 'fintech-fraud', name: 'Fintech Fraud Detection & Risk Scoring', description: 'Transaction monitoring telemetry for anti-fraud classifiers with skewed anomalous patterns.', category: 'Fintech & Risk', fields: [ { id: 'f-ff-id', name: 'tx_token', label: 'Transaction ID', type: 'name', description: 'Cryptographic ledger token', params: {}, }, { id: 'f-ff-name', name: 'merchant_name', label: 'Merchant Name', type: 'name', description: 'Synthetic counterparty vendor', params: {}, }, { id: 'f-ff-email', name: 'auth_email', label: 'Auth Email', type: 'email', description: 'Tokenized transaction origin email', params: { domain: 'syn-pay.net' }, }, { id: 'f-ff-card', name: 'card_pan', label: 'Card PAN', type: 'credit_card', description: 'Tokenized 16-digit card surrogate', params: {}, }, { id: 'f-ff-amt', name: 'settlement_usd', label: 'Settlement ($)', type: 'gaussian_float', description: 'Transaction gross volume (μ=$1420.00, σ=$480.00)', params: { mean: 1420.0, stdDev: 480.0, currency: '$' }, }, { id: 'f-ff-verdict', name: 'risk_verdict', label: 'Risk Verdict', type: 'category_weights', description: 'Ground truth classifier target', params: { categories: ['Legitimate', 'Suspicious', 'Flagged_Fraud'], weights: [0.89, 0.08, 0.03], }, }, { id: 'f-ff-ts', name: 'settled_at', label: 'Settlement Time', type: 'date_range', description: 'High-precision event timestamp', params: { dateStart: '2025-01-01', dateEnd: '2025-08-20' }, }, ], }, 'saas-telemetry': { id: 'saas-telemetry', name: 'SaaS Product Telemetry & Usage Metrics', description: 'B2B subscription telemetry with active seat distribution, recurring revenue, and subscription tiers.', category: 'Cloud Software & B2B', fields: [ { id: 'f-st-id', name: 'org_uuid', label: 'Organization UUID', type: 'name', description: 'Tenant identifier', params: {}, }, { id: 'f-st-name', name: 'tenant_name', label: 'Tenant Name', type: 'name', description: 'Synthetic enterprise tenant moniker', params: {}, }, { id: 'f-st-email', name: 'admin_email', label: 'Admin Email', type: 'email', description: 'Primary owner email', params: { domain: 'syn-saas.io' }, }, { id: 'f-st-seats', name: 'active_seats', label: 'Active Seats', type: 'gaussian_float', description: 'Provisioned enterprise seats (μ=48.0, σ=18.0)', params: { mean: 48.0, stdDev: 18.0, unit: 'seats' }, }, { id: 'f-st-mrr', name: 'mrr_usd', label: 'Monthly Revenue', type: 'gaussian_float', description: 'MRR ledger (μ=$2850.00, σ=$950.00)', params: { mean: 2850.0, stdDev: 950.0, currency: '$' }, }, { id: 'f-st-plan', name: 'plan_tier', label: 'Plan Tier', type: 'category_weights', description: 'Subscription entitlement level', params: { categories: ['Starter', 'Growth', 'Enterprise'], weights: [0.48, 0.36, 0.16], }, }, { id: 'f-st-date', name: 'provisioned_at', label: 'Provisioned Date', type: 'date_range', description: 'Cohort subscription activation', params: { dateStart: '2024-01-01', dateEnd: '2025-08-20' }, }, ], }, } export const FIRST_NAMES = [ 'Elena', 'Malik', 'Siddharth', 'Claire', 'Liam', 'Amara', 'Kenji', 'Sofia', 'Devon', 'Maya', 'Tobias', 'Fatima', 'Mateo', 'Astrid', 'Zara', ] export const LAST_NAMES = [ 'Rostova', 'Al-Mansoor', 'Patel', 'Chen', "O'Connor", 'Okafor', 'Sato', 'Alvarez', 'Vance', 'Lin', 'Richter', 'Zahra', 'Silva', 'Lindqvist', 'Novak', ] export const CARD_BRANDS = ['Visa', 'Mastercard', 'Amex'] export function boxMullerRandom( mean: number, stdDev: number, seedOffset: number, seed: number, noiseMechanism: boolean, epsilon: number, ): number { const u1 = Math.max(1e-6, Math.abs(Math.sin(seed + seedOffset * 17.3))) const u2 = Math.max(1e-6, Math.abs(Math.cos(seed + seedOffset * 31.7))) const z0 = Math.sqrt(-2.0 * Math.log(u1)) * Math.cos(2.0 * Math.PI * u2) const dpNoiseScale = noiseMechanism ? stdDev / (Math.max(0.1, epsilon) * 12) : 0 const 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