refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-data-ethics-privacy
+title: Data Ethics and Privacy
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [data ethics, privacy, GDPR, CCPA, differential privacy, federated learning, k-anonymity, PII]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [ethics, privacy, gdpr, ccpa, differential-privacy, federated-learning, ai-governance, pii]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: ethics / law / cryptography
+  applicable_to: [Data Governance, ML Privacy, Compliance]
+---
+
+# Data Ethics and Privacy
+
+## 매 한 줄
+> **"매 can we의 X — 매 should we"**. 매 GDPR (EU 2018) + CCPA (CA) + 매 EU AI Act (2024). 매 modern technique: differential privacy, federated learning, homomorphic encryption, ZK proof. 매 LLM 시대 의 training data + memorization 의 new challenge.
+
+## 매 핵심 principle
+
+### 매 Fair Information Practice Principles (FIPPs)
+1. **Notice / Transparency**.
+2. **Consent / Choice**.
+3. **Access / Participation**.
+4. **Integrity / Security**.
+5. **Enforcement / Redress**.
+
+### GDPR (EU, 2018)
+- **Lawful basis**: 매 6 (consent, contract, legal, vital, public, legitimate interest).
+- **Data minimization**.
+- **Purpose limitation**.
+- **Right to access / rectify / erasure / portability / object**.
+- **DPIA** (Data Protection Impact Assessment).
+- **DPO** (Data Protection Officer).
+- 매 fine: 매 4% global revenue.
+
+### CCPA / CPRA (California)
+- 매 sell 의 opt-out.
+- 매 sensitive data 의 limit.
+
+### 매 EU AI Act (2024)
+- 매 risk tier (unacceptable / high / limited / minimal).
+- 매 high-risk: 매 audit, 매 human oversight.
+
+### 매 privacy techniques
+
+#### Anonymization (irreversible)
+- 매 PII 의 remove + 매 generalize.
+- 매 k-anonymity (Sweeney 2002).
+- 매 l-diversity, t-closeness.
+
+#### Pseudonymization (reversible with key)
+- 매 PII 의 hash / token.
+- 매 GDPR 의 lighter requirement.
+
+#### Differential Privacy (Dwork 2006)
+- 매 noise injection.
+- 매 mathematical guarantee (ε).
+- 매 Apple, Google, US Census.
+
+#### Federated Learning
+- 매 raw data 의 leave 의 X.
+- 매 model update 만.
+- 매 mobile, healthcare.
+
+#### Homomorphic Encryption
+- 매 encrypted 의 compute.
+- 매 expensive (still).
+
+#### Secure Multi-party Computation (MPC)
+- 매 multiple party 의 compute 의 share X.
+
+#### Zero-Knowledge Proof
+- 매 prove without reveal.
+- 매 ZK-ML 의 emerging.
+
+### LLM-specific privacy
+- **Training data leak**: 매 verbatim memorization.
+- **Membership inference**: 매 was 매 X 의 train data?
+- **Model inversion**: 매 model 의 input 의 reconstruct.
+- **Mitigation**: dedup, DP-SGD, scrubbing, prompt safety.
+
+### 매 design pattern
+1. **Privacy by Design** (Cavoukian).
+2. **Data minimization**.
+3. **Encryption at rest + in transit**.
+4. **Access control + audit log**.
+5. **Retention policy**.
+6. **Right to erasure pipeline**.
+
+## 💻 패턴
+
+### PII detection + redaction
+```python
+import re
+from presidio_analyzer import AnalyzerEngine
+from presidio_anonymizer import AnonymizerEngine
+
+analyzer = AnalyzerEngine()
+anonymizer = AnonymizerEngine()
+
+def anonymize(text, language='en'):
+    results = analyzer.analyze(text=text, language=language)
+    return anonymizer.anonymize(text=text, analyzer_results=results).text
+
+# 매 example
+print(anonymize('John Smith, ssn 123-45-6789, lives at 123 Main St.'))
+# <PERSON>, ssn <US_SSN>, lives at <LOCATION>.
+```
+
+### k-Anonymity
+```python
+def k_anonymize(df, quasi_identifiers, k=5):
+    """매 매 group 의 size ≥ k."""
+    grouped = df.groupby(quasi_identifiers).size()
+    valid_groups = grouped[grouped >= k].index
+    return df[df.set_index(quasi_identifiers).index.isin(valid_groups)]
+
+# 매 generalize age, zip → 매 group ≥ 5
+```
+
+### Differential Privacy (Laplace mechanism)
+```python
+import numpy as np
+
+def laplace_mechanism(query_result, sensitivity, epsilon):
+    """매 ε-differential privacy."""
+    noise = np.random.laplace(0, sensitivity / epsilon)
+    return query_result + noise
+
+# 매 example: 매 count of users with X
+count_true = df['has_x'].sum()
+count_dp = laplace_mechanism(count_true, sensitivity=1, epsilon=1.0)
+```
+
+### DP-SGD (training)
+```python
+from opacus import PrivacyEngine
+
+privacy_engine = PrivacyEngine()
+model, optimizer, train_loader = privacy_engine.make_private(
+    module=model,
+    optimizer=optimizer,
+    data_loader=train_loader,
+    noise_multiplier=1.0,
+    max_grad_norm=1.0,
+)
+
+# 매 training 의 DP guarantee
+for x, y in train_loader:
+    optimizer.zero_grad()
+    loss = F.cross_entropy(model(x), y)
+    loss.backward()
+    optimizer.step()
+
+epsilon = privacy_engine.get_epsilon(delta=1e-5)
+print(f'(ε={epsilon}, δ={1e-5})-DP')
+```
+
+### Federated Learning (Flower)
+```python
+import flwr as fl
+
+class Client(fl.client.NumPyClient):
+    def get_parameters(self, config):
+        return [val.cpu().numpy() for val in model.state_dict().values()]
+    
+    def fit(self, parameters, config):
+        # 매 local training (raw data 의 stay local)
+        set_parameters(model, parameters)
+        train(model, local_dataset)
+        return get_parameters(model), len(local_dataset), {}
+    
+    def evaluate(self, parameters, config):
+        loss, accuracy = test(model, local_dataset)
+        return loss, len(local_dataset), {'accuracy': accuracy}
+
+fl.client.start_client(server_address='central:8080', client=Client())
+```
+
+### Right to Erasure (GDPR)
+```python
+def erase_user(user_id):
+    """매 GDPR Art. 17."""
+    # 매 1. primary
+    db.users.delete(user_id)
+    
+    # 매 2. derived (cache, analytics, ML training)
+    cache.invalidate(f'user:{user_id}')
+    analytics_db.delete_user_events(user_id)
+    
+    # 매 3. backup (delayed)
+    schedule_backup_purge(user_id, after_days=30)
+    
+    # 매 4. ML model — 매 retrain or 매 forget request
+    if user_in_training_data(user_id):
+        machine_unlearning(user_id) or schedule_retrain()
+    
+    # 매 5. log + audit
+    audit_log.write({'action': 'erase', 'user_id': user_id, 'date': now()})
+```
+
+### Access control + audit
+```python
+def access_pii(user_id, requester, reason):
+    if not requester.has_role('data_steward'):
+        raise PermissionError()
+    
+    audit_log.write({
+        'requester': requester.id,
+        'subject': user_id,
+        'reason': reason,
+        'timestamp': datetime.now(),
+        'data_accessed': ['email', 'phone'],
+    })
+    
+    return db.users.get(user_id, fields=['email', 'phone'])
+```
+
+### LLM verbatim leak detection
+```python
+def check_training_data_leak(model, training_corpus, n_samples=100):
+    """매 model 의 verbatim 의 reproduce?"""
+    leaks = []
+    for chunk in random.sample(training_corpus, n_samples):
+        prefix = chunk[:100]
+        completion = model.generate(prefix, max_tokens=200)
+        if chunk[100:300] in completion:
+            leaks.append({'chunk': chunk[:50] + '...', 'leaked': True})
+    return leaks
+```
+
+### Membership inference attack (defense check)
+```python
+def membership_inference_test(model, member_data, non_member_data):
+    """매 model 의 매 specific 의 in train data 의 distinguish?"""
+    member_loss = [loss(model, x, y) for x, y in member_data]
+    non_member_loss = [loss(model, x, y) for x, y in non_member_data]
+    
+    # 매 simple threshold attack
+    threshold = np.median(member_loss + non_member_loss)
+    
+    correct = sum(1 for l in member_loss if l < threshold) + \
+              sum(1 for l in non_member_loss if l > threshold)
+    accuracy = correct / (len(member_loss) + len(non_member_loss))
+    
+    if accuracy > 0.55:
+        return 'WARN: membership inference vulnerability'
+    return 'OK'
+```
+
+### Data retention policy
+```python
+def retention_purge():
+    """매 매 day 의 cron."""
+    # 매 GDPR + business 의 retention.
+    db.events.delete(where=f"created_at < NOW() - INTERVAL '7 years'")
+    db.user_logs.delete(where=f"created_at < NOW() - INTERVAL '90 days'")
+    db.analytics_raw.delete(where=f"created_at < NOW() - INTERVAL '13 months'")
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| EU users | GDPR compliance (DPO + DPIA + erasure) |
+| US users | CCPA + sector law (HIPAA, FERPA) |
+| Sensitive aggregate stats | Differential Privacy |
+| Cross-org training | Federated Learning |
+| Encrypted compute | Homomorphic / MPC |
+| LLM training | DP-SGD + dedup + scrub |
+| Identity-needed | Pseudonymize + access control |
+| Public data | Anonymize (k-anonymity) |
+
+**기본값**: Privacy by Design + 매 access control + 매 retention + 매 erasure pipeline.
+
+## 🔗 Graph
+- 부모: [[AI-Ethics]] · [[Privacy]]
+- 변형: [[GDPR]] · [[CCPA]] · [[Differential-Privacy]] · [[Federated-Learning]] · [[Homomorphic-Encryption]]
+- 응용: [[k-Anonymity]]
+- Adjacent: [[Algorithmic Fairness]] · [[Authenticity]] · [[AI-Sovereignty]] · [[Data-Flywheel-Effect]] · [[Anthropomorphism]]
+
+## 🤖 LLM 활용
+**언제**: 매 product privacy review. 매 GDPR audit. 매 ML privacy. 매 cross-border data transfer.
+**언제 X**: 매 specific legal advice (lawyer). 매 medical clinical (HIPAA expert).
+
+## ❌ 안티패턴
+- **Anonymization 의 false sense** (linkage attack 가능): 매 k-anonymity + 매 l-diversity.
+- **Long retention without business need**: 매 GDPR violation.
+- **No erasure pipeline**: 매 right 의 fulfill X.
+- **PII in logs**: 매 invisible leak.
+- **Federated 의 raw data 의 leak** (model invert): 매 DP-SGD 도 필요.
+- **Consent 의 invalid** (forced, vague).
+
+## 🧪 검증 / 중복
+- Verified (GDPR text, Apple DP, Dwork DP paper, Sweeney k-anonymity).
+- 신뢰도 A.
+- Related: [[Algorithmic Fairness]] · [[Authenticity]] · [[Bias-Correction-Algorithm]] · [[Anthropomorphism]] · [[Atmospheric-Intelligence]] (privacy challenges).
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — GDPR + DP + FL + 매 Presidio / k-anon / Opacus / Flower / erasure code |