2nd/10_Wiki/Topics/AI_and_ML/Data-Ethics and 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-data-ethics-privacy	Data Ethics and Privacy	10_Wiki/Topics	verified	self	data ethics privacy GDPR CCPA differential privacy federated learning k-anonymity PII	none	A	0.9	applied	ethics privacy gdpr ccpa differential-privacy federated-learning ai-governance pii		2026-05-10	pending	language applicable_to ethics / law / cryptography 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

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

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)

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)

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)

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)

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

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

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)

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

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