2nd/10_Wiki/Topics/Domain_Programming/DevOps_and_Security/Ensuring-Data-Privacy.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-ensuring-data-privacy	Ensuring Data Privacy	10_Wiki/Topics	verified	self	Data Privacy Privacy Engineering GDPR Compliance	none	A	0.9	applied	privacy gdpr security compliance		2026-05-10	applied	language framework Python/TypeScript OneTrust/Fides/OPA

Ensuring Data Privacy

매 한 줄

"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.

매 핵심

매 Privacy Principle (GDPR Art.5)

1. Lawfulness, fairness, transparency — consent / legitimate interest.
2. Purpose limitation — 매 collected purpose 외 사용 금지.
3. Data minimization — 매 필요한 최소.
4. Accuracy — correctable.
5. Storage limitation — retention schedule.
6. Integrity & confidentiality — encryption.
7. Accountability — DPO, audit, DPIA.

매 PET (Privacy-Enhancing Tech) 2026

• Pseudonymization: tokenization, format-preserving encryption (FPE).
• Anonymization: k-anonymity, l-diversity, t-closeness.
• Differential Privacy: ε,δ noise — Apple, US Census, Chrome.
• Federated learning: 매 model travels, data stays.
• Homomorphic encryption (FHE): 매 compute on encrypted — Microsoft SEAL, OpenFHE.
• Confidential computing (TEE): Intel TDX, AMD SEV-SNP, Apple Private Cloud Compute.
• Zero-Knowledge Proofs: identity 증명 without disclose.

매 응용

1. EU GDPR + 한국 PIPA + 중국 PIPL compliance.
2. Healthcare HIPAA, PCI-DSS payment.
3. ML training without raw data (FL, DP).
4. Cross-border transfer (SCC, BCR, DPF).
5. Right to be forgotten (RTBF) automation.

💻 패턴

Data classification + DLP

# 매 PII detection — Microsoft Presidio
from presidio_analyzer import AnalyzerEngine
analyzer = AnalyzerEngine()
results = analyzer.analyze(text=user_input, language='en',
  entities=['EMAIL_ADDRESS','PHONE_NUMBER','CREDIT_CARD','PERSON','KR_RRN'])
for r in results: redact_or_mask(text, r.start, r.end)

Format-preserving tokenization

# 매 ff3-1 — preserves format (e.g., card number)
from ff3 import FF3Cipher
c = FF3Cipher(key, tweak)
token = c.encrypt("4242424242424242")  # → 16-digit string
plain = c.decrypt(token)

Differential Privacy noise

import numpy as np
def laplace_mechanism(true_val, sensitivity, epsilon):
    return true_val + np.random.laplace(0, sensitivity / epsilon)
# 매 query: count of users in segment
noisy_count = laplace_mechanism(true_count=1234, sensitivity=1, epsilon=1.0)

k-anonymity check

import pandas as pd
def k_anonymity(df: pd.DataFrame, quasi_ids: list[str]) -> int:
    return df.groupby(quasi_ids).size().min()
# 매 ensure k>=5 before release
assert k_anonymity(df, ['zip','age','gender']) >= 5

DSAR (Data Subject Access Request) automation

async def dsar_export(user_id: str) -> bytes:
    bundle = {
      'profile': await db.users.find_one({'_id':user_id}),
      'orders':  [o async for o in db.orders.find({'userId':user_id})],
      'logs':    await elasticsearch_export(user_id),
    }
    return json.dumps(bundle, default=str).encode()

async def dsar_erasure(user_id: str):
    await db.users.update_one({'_id':user_id},
        {'$set': {'email':None,'name':None,'erasedAt':datetime.utcnow()}})
    await s3.delete_objects(Bucket='pii', Prefix=f'users/{user_id}/')

Consent record (Fides/IAB TCF)

const consent = {
  userId: 'u_123',
  purposes: { analytics: true, marketing: false, personalization: true },
  vendors: { google: true },
  timestamp: new Date().toISOString(),
  version: 'tcf-2.2',
  signature: hmac(record),
};
await db.consents.insertOne(consent);

매 결정 기준

상황	Approach
EU users	GDPR + Schrems II SCC
한국 users	PIPA — 개인정보처리방침, 위탁 동의
Aggregate analytics	Differential Privacy
Payment data	PCI-DSS tokenization
ML training	Federated learning + DP
Cross-org compute	TEE (Confidential Computing)

기본값: 매 minimize + classify + tokenize + consent ledger + DSAR API.

🔗 Graph

• 부모: Practical-Cryptography · 보안 및 시스템 신뢰성 표준
• 변형: 보안 및 시스템 신뢰성 표준
• 응용: Anomaly-Detection · Information-Society
• Adjacent: Digital Intellectual Property Rights

🤖 LLM 활용

언제: privacy policy 검토, DSAR response draft, PIA 질문 generation. 언제 X: 매 PII 를 third-party LLM 에 raw 로 전송 — anonymize 먼저.

❌ 안티패턴

• Hash = anonymized 오해: 매 hash 는 pseudonymization, GDPR 적용.
• Consent on entry-only: 매 ongoing — withdrawable, granular.
• Log PII: 매 logger 가 leak source — redact filter.
• Forever retention: 매 GDPR 위반 — TTL + erasure.
• Plaintext backup: 매 encryption at rest 필수.

🧪 검증 / 중복

• Verified: GDPR Art.5/17/25; ISO/IEC 27701; NIST SP 800-188; Microsoft Presidio docs.
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — principles + PETs + DSAR/DP patterns