Privacy Regulations & Compliance
Phony Cloud helps organizations comply with privacy regulations through data de-identification and synthetic data generation.
Why Synthetic Data for Compliance?
De-identified data is often exempt from privacy regulations:
| Regulation | De-identified Data Status |
|---|---|
| CCPA | Explicitly excluded from scope |
| GDPR | Anonymized data falls outside scope |
| HIPAA | Safe Harbor de-identification = compliant |
| LGPD | Anonymized data not considered personal data |
Phony's Approach:
Production Data (PII) → Phony Engine → Synthetic Data (No PII)
↓
• Looks like real data
• Statistical properties preserved
• Zero re-identification risk
• Exempt from many regulationsUS Privacy Regulations
California Consumer Privacy Act (CCPA)
| Aspect | Detail |
|---|---|
| Scope | CA residents' data |
| Thresholds | $25M+ revenue OR 50K+ CA consumers OR 50%+ revenue from data sales |
| Key Rights | Know, delete, opt-out, non-discrimination |
| Penalties | $2,500 unintentional / $7,500 intentional per violation |
| Enforcement | CA Attorney General, Private right of action (breaches) |
Phony Solution: Synthetic data in dev/test environments eliminates CA consumer data exposure.
Virginia Consumer Data Protection Act (CDPA)
| Aspect | Detail |
|---|---|
| Scope | VA residents' data |
| Thresholds | 100K+ VA consumers OR 25K+ consumers & 50%+ revenue from data |
| Key Rights | Access, correct, delete, data portability, opt-out |
| Penalties | Up to $7,500 per violation |
| Enforcement | VA Attorney General only (no private right of action) |
Colorado Privacy Act (CPA)
| Aspect | Detail |
|---|---|
| Scope | CO residents' data |
| Thresholds | 100K+ CO consumers OR 25K+ consumers & revenue from data sales |
| Key Rights | Access, correct, delete, portability, opt-out |
| Penalties | Up to $20,000 per violation |
| Enforcement | CO Attorney General |
Illinois Biometric Information Privacy Act (BIPA)
| Aspect | Detail |
|---|---|
| Scope | Biometric data (fingerprints, face scans, etc.) |
| Thresholds | Any biometric data collection |
| Key Requirements | Written consent, retention policy, no sale |
| Penalties | $1,000 negligent / $5,000 intentional per violation |
| Enforcement | Private right of action (class action exposure) |
Note: BIPA has resulted in significant class action settlements. Never use real biometric data in testing.
Health Insurance Portability and Accountability Act (HIPAA)
| Aspect | Detail |
|---|---|
| Scope | Protected Health Information (PHI) |
| Covered Entities | Healthcare providers, plans, clearinghouses, business associates |
| Safe Harbor | 18 identifier types must be removed for de-identification |
| Penalties | $50,000 - $250,000 per violation + potential imprisonment |
| Enforcement | HHS Office for Civil Rights |
HIPAA Safe Harbor Identifiers (must be removed):
- Names
- Geographic data smaller than state
- Dates (except year) related to individual
- Phone numbers
- Fax numbers
- Email addresses
- SSN
- Medical record numbers
- Health plan beneficiary numbers
- Account numbers
- Certificate/license numbers
- Vehicle identifiers
- Device identifiers
- Web URLs
- IP addresses
- Biometric identifiers
- Full face photos
- Any other unique identifying number
Phony Solution: Generate synthetic healthcare data that preserves statistical properties without any PHI.
International Privacy Regulations
General Data Protection Regulation (GDPR) - EU
| Aspect | Detail |
|---|---|
| Scope | EU residents' data, regardless of company location |
| Thresholds | Any processing of EU personal data |
| Key Principles | Lawfulness, purpose limitation, data minimization, accuracy, storage limitation, integrity, accountability |
| Penalties | Up to 4% global annual revenue OR €20M (whichever higher) |
| Enforcement | Data Protection Authorities (DPAs) in each member state |
GDPR Key Rights:
- Right to access
- Right to rectification
- Right to erasure ("right to be forgotten")
- Right to restrict processing
- Right to data portability
- Right to object
- Rights related to automated decision-making
Phony Solution: Synthetic data = no personal data = outside GDPR scope.
UK Data Protection Act (DPA 2018)
| Aspect | Detail |
|---|---|
| Scope | UK residents' data (post-Brexit GDPR equivalent) |
| Thresholds | Any processing of UK personal data |
| Penalties | Up to 4% global revenue OR £17.5M |
| Enforcement | Information Commissioner's Office (ICO) |
Lei Geral de Proteção de Dados (LGPD) - Brazil
| Aspect | Detail |
|---|---|
| Scope | Brazilian residents' data |
| Thresholds | Any processing of Brazilian personal data |
| Penalties | Up to 2% Brazil revenue, capped at R$50M (~$10M) |
| Enforcement | Autoridade Nacional de Proteção de Dados (ANPD) |
Consumer Privacy Protection Act (CPPA) - Canada
| Aspect | Detail |
|---|---|
| Scope | Canadian residents' data |
| Status | Proposed (Bill C-27), expected to replace PIPEDA |
| Penalties | Up to 5% global revenue OR C$25M |
| Enforcement | Privacy Commissioner of Canada |
Penalty Summary
| Regulation | Max Penalty | Calculation |
|---|---|---|
| GDPR | €20M or 4% global revenue | Higher of two |
| CCPA | $7,500 per violation | Per incident |
| HIPAA | $250,000 + imprisonment | Per violation category |
| BIPA | $5,000 per violation | Class action multiplier |
| LGPD | R$50M (~$10M) | 2% Brazil revenue cap |
| UK DPA | £17.5M or 4% global revenue | Higher of two |
ROI Calculation Example
Scenario: 10,000 customer records exposed in staging environment
CCPA: 10,000 × $2,500 = $25,000,000 potential exposure
GDPR: 4% of $50M revenue = $2,000,000 potential exposure
HIPAA: Per-record + per-category penalties = $500,000+ exposure
vs.
Phony Cloud Business: $199/month = $2,388/year
Break-even: 1 prevented violationPrivacy by Design Principles
Phony is built on Privacy by Design (PbD) principles:
1. Proactive Not Reactive
Prevent privacy breaches before they occur. Generate synthetic data from day one—don't wait for a breach to fix your dev/test environments.
2. Privacy as the Default
No user action required for privacy protection. Phony generates privacy-safe data by default—you have to explicitly opt-in to include real data.
3. Privacy Embedded in Design
Privacy is not a feature bolted on after the fact. The N-gram engine cannot reproduce original training data when configured with excludeOriginals: true.
4. Full Functionality
Privacy AND utility, not either/or. Statistical learning preserves data distributions, relationships, and edge cases while eliminating PII.
5. End-to-End Security
Lifecycle data protection. Cloud-trained models can be deleted; local training never uploads your data.
6. Visibility and Transparency
Users can verify privacy protection. Model introspection shows exactly what patterns are learned (without the original data).
7. User-Centric Design
Respect for user privacy is paramount. Your customers' data never needs to leave production for you to build and test great software.
Compliance Checklist for Development Teams
Before Using Phony
- [ ] Identify which regulations apply to your data
- [ ] Document your data flows (what data, where, who accesses)
- [ ] Get stakeholder alignment on synthetic data approach
Setting Up Phony Cloud
- [ ] Connect to production database (read-only recommended)
- [ ] Configure anonymization rules for sensitive columns
- [ ] Enable
excludeOriginals: truefor all generators - [ ] Set up scheduled sync to keep environments current
Ongoing Compliance
- [ ] Review anonymization rules when schema changes
- [ ] Audit access logs quarterly
- [ ] Update custom models when data patterns change
- [ ] Document synthetic data usage in compliance reports
How Phony Helps
| Compliance Need | Phony Feature |
|---|---|
| Data minimization | Subsetting with referential integrity |
| Purpose limitation | Schema-first generation (only what you need) |
| Storage limitation | Ephemeral mock APIs (data not persisted) |
| Data accuracy | Statistical learning (realistic distributions) |
| Integrity & security | Deterministic generation (reproducible tests) |
| Accountability | Audit logs, version history, model provenance |
De-identification Methods
| Method | Description | Phony Support |
|---|---|---|
| Redaction | Remove sensitive values entirely | ✓ Null/empty replacement |
| Masking | Replace with fixed patterns (XXX-XX-1234) | ✓ Format-preserving masks |
| Pseudonymization | Replace with consistent aliases | ✓ Deterministic seeding |
| Generalization | Reduce precision (age → age range) | ✓ Custom generators |
| Synthesis | Generate statistically similar data | ✓ Core feature |
| Differential Privacy | Add calibrated noise | ✓ Roadmap (Phase 3) |