[G1-Sync] Manual knowledge update
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Antigravity Agent
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id: wiki-2026-0508-ensuring-data-privacy
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title: Ensuring Data Privacy
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category: 10_Wiki/Topics
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status: merged
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redirect_to: 보안_및_시스템_신뢰성_표준
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canonical_id: wiki-2026-0507-039
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aliases: []
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status: verified
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canonical_id: self
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aliases: [Data Privacy, Privacy Engineering, GDPR Compliance]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.92
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tags: [uncategorized]
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confidence_score: 0.9
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verification_status: applied
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tags: [privacy, gdpr, security, compliance]
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raw_sources: []
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last_reinforced: 2026-05-08
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github_commit: pending
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inferred_by: Claude Opus 4.7 (auto-normalize 2026-05-08)
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last_reinforced: 2026-05-10
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github_commit: applied
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tech_stack:
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language: Python/TypeScript
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framework: OneTrust/Fides/OPA
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---
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# Redirect
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# Ensuring Data Privacy
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이 문서는 Canonical 문서인 [[보안_및_시스템_신뢰성_표준]]으로 통합되었습니다.
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모든 최신 지식과 세부 내용은 위 링크를 참조하십시오.
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## 매 한 줄
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> **"매 personal data 가 lawful basis + minimum + purpose-limited 로 다뤄진다."**. Data privacy engineering 은 매 GDPR/CCPA/LGPD/K-PIPA 의 legal requirement 를 매 storage, processing, transfer, retention 의 매 단계 에 deterministic control 로 구현. 2026 stack: classification + DLP + tokenization/PETs (DP, FHE, TEE) + consent management + DSAR automation + privacy-by-design.
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## 매 핵심
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### 매 Privacy Principle (GDPR Art.5)
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1. **Lawfulness, fairness, transparency** — consent / legitimate interest.
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2. **Purpose limitation** — 매 collected purpose 외 사용 금지.
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3. **Data minimization** — 매 필요한 최소.
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4. **Accuracy** — correctable.
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5. **Storage limitation** — retention schedule.
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6. **Integrity & confidentiality** — encryption.
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7. **Accountability** — DPO, audit, DPIA.
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### 매 PET (Privacy-Enhancing Tech) 2026
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- **Pseudonymization**: tokenization, format-preserving encryption (FPE).
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- **Anonymization**: k-anonymity, l-diversity, t-closeness.
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- **Differential Privacy**: ε,δ noise — Apple, US Census, Chrome.
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- **Federated learning**: 매 model travels, data stays.
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- **Homomorphic encryption (FHE)**: 매 compute on encrypted — Microsoft SEAL, OpenFHE.
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- **Confidential computing (TEE)**: Intel TDX, AMD SEV-SNP, Apple Private Cloud Compute.
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- **Zero-Knowledge Proofs**: identity 증명 without disclose.
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### 매 응용
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1. EU GDPR + 한국 PIPA + 중국 PIPL compliance.
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2. Healthcare HIPAA, PCI-DSS payment.
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3. ML training without raw data (FL, DP).
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4. Cross-border transfer (SCC, BCR, DPF).
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5. Right to be forgotten (RTBF) automation.
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## 💻 패턴
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### Data classification + DLP
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```python
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# 매 PII detection — Microsoft Presidio
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from presidio_analyzer import AnalyzerEngine
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analyzer = AnalyzerEngine()
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results = analyzer.analyze(text=user_input, language='en',
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entities=['EMAIL_ADDRESS','PHONE_NUMBER','CREDIT_CARD','PERSON','KR_RRN'])
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for r in results: redact_or_mask(text, r.start, r.end)
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```
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### Format-preserving tokenization
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```python
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# 매 ff3-1 — preserves format (e.g., card number)
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from ff3 import FF3Cipher
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c = FF3Cipher(key, tweak)
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token = c.encrypt("4242424242424242") # → 16-digit string
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plain = c.decrypt(token)
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```
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### Differential Privacy noise
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```python
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import numpy as np
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def laplace_mechanism(true_val, sensitivity, epsilon):
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return true_val + np.random.laplace(0, sensitivity / epsilon)
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# 매 query: count of users in segment
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noisy_count = laplace_mechanism(true_count=1234, sensitivity=1, epsilon=1.0)
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```
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### k-anonymity check
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```python
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import pandas as pd
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def k_anonymity(df: pd.DataFrame, quasi_ids: list[str]) -> int:
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return df.groupby(quasi_ids).size().min()
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# 매 ensure k>=5 before release
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assert k_anonymity(df, ['zip','age','gender']) >= 5
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```
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### DSAR (Data Subject Access Request) automation
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```python
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async def dsar_export(user_id: str) -> bytes:
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bundle = {
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'profile': await db.users.find_one({'_id':user_id}),
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'orders': [o async for o in db.orders.find({'userId':user_id})],
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'logs': await elasticsearch_export(user_id),
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}
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return json.dumps(bundle, default=str).encode()
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async def dsar_erasure(user_id: str):
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await db.users.update_one({'_id':user_id},
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{'$set': {'email':None,'name':None,'erasedAt':datetime.utcnow()}})
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await s3.delete_objects(Bucket='pii', Prefix=f'users/{user_id}/')
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```
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### Consent record (Fides/IAB TCF)
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```typescript
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const consent = {
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userId: 'u_123',
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purposes: { analytics: true, marketing: false, personalization: true },
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vendors: { google: true },
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timestamp: new Date().toISOString(),
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version: 'tcf-2.2',
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signature: hmac(record),
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};
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await db.consents.insertOne(consent);
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| EU users | GDPR + Schrems II SCC |
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| 한국 users | PIPA — 개인정보처리방침, 위탁 동의 |
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| Aggregate analytics | Differential Privacy |
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| Payment data | PCI-DSS tokenization |
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| ML training | Federated learning + DP |
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| Cross-org compute | TEE (Confidential Computing) |
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**기본값**: 매 minimize + classify + tokenize + consent ledger + DSAR API.
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## 🔗 Graph
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- 부모: [[Practical-Cryptography]] · [[Symmetric-Encryption]]
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- 변형: [[Zero-Trust Architecture]]
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- 응용: [[Anomaly-Detection]] · [[Information-Society]]
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- Adjacent: [[Digital Intellectual Property Rights]]
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## 🤖 LLM 활용
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**언제**: privacy policy 검토, DSAR response draft, PIA 질문 generation.
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**언제 X**: 매 PII 를 third-party LLM 에 raw 로 전송 — anonymize 먼저.
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## ❌ 안티패턴
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- **Hash = anonymized 오해**: 매 hash 는 pseudonymization, GDPR 적용.
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- **Consent on entry-only**: 매 ongoing — withdrawable, granular.
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- **Log PII**: 매 logger 가 leak source — redact filter.
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- **Forever retention**: 매 GDPR 위반 — TTL + erasure.
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- **Plaintext backup**: 매 encryption at rest 필수.
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## 🧪 검증 / 중복
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- Verified: GDPR Art.5/17/25; ISO/IEC 27701; NIST SP 800-188; Microsoft Presidio docs.
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — principles + PETs + DSAR/DP patterns |
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