docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거
Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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Antigravity Agent
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---
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id: wiki-2026-0508-privacy-preserving-ai
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title: Privacy Preserving AI
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Privacy-Preserving Machine Learning, PPML, Confidential AI]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [privacy, security, differential-privacy, federated-learning, cryptography]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: Opacus / TF-Federated / TenSEAL / PySyft
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---
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# Privacy Preserving AI
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## 매 한 줄
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> **"매 train and infer on data without exposing it — 4 pillars: DP, FL, HE, MPC"**. GDPR (2018) 와 healthcare/finance regulation 으로 driven, 2024 EU AI Act 와 US executive orders 로 mainstream. 2026 currently confidential computing (TEE: Intel TDX, NVIDIA H100 CC, Apple PCC) 가 production deployment 의 default.
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## 매 핵심
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### 매 4 pillars
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1. **Differential Privacy (DP)**: noise 추가 to bound info leakage. Calibrated by epsilon (ε).
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2. **Federated Learning (FL)**: model goes to data, not data to model.
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3. **Homomorphic Encryption (HE)**: compute on ciphertext directly.
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4. **Secure Multi-Party Computation (MPC)**: parties jointly compute without revealing inputs.
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### 매 production additions (2024-2026)
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- **TEE / Confidential computing**: Intel TDX, AMD SEV-SNP, NVIDIA H100 confidential GPU, Apple Private Cloud Compute.
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- **Synthetic data**: GAN/diffusion-generated; near-zero re-id risk if done right.
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- **Machine unlearning**: GDPR right-to-be-forgotten compliance.
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### 매 trade-offs
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| Method | Privacy | Utility | Compute | Deployed? |
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|---|---|---|---|---|
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| DP-SGD (ε≈1) | High | -2 to -5% acc | 2-5x | Yes (Apple, Google) |
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| Federated | Medium | ~same | High comm | Yes (Gboard, healthcare) |
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| HE (CKKS) | Very high | exact | 1000-10000x | Niche |
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| MPC | Very high | exact | 100-1000x | Niche |
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| TEE | High (HW trust) | ~same | ~1.1x | Rapidly growing |
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## 💻 패턴
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### DP-SGD with Opacus (PyTorch)
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```python
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from opacus import PrivacyEngine
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import torch.optim as optim
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model, optimizer = build_model(), optim.SGD(model.parameters(), lr=0.1)
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loader = build_loader()
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privacy_engine = PrivacyEngine()
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model, optimizer, loader = privacy_engine.make_private_with_epsilon(
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module=model, optimizer=optimizer, data_loader=loader,
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target_epsilon=1.0, target_delta=1e-5, epochs=10,
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max_grad_norm=1.0,
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)
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for epoch in range(10):
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for x, y in loader:
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optimizer.zero_grad()
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loss = criterion(model(x), y)
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loss.backward()
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optimizer.step()
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print(f"ε={privacy_engine.get_epsilon(delta=1e-5):.2f}")
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```
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### Federated averaging (FedAvg)
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```python
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def fed_avg(global_model, client_updates, client_weights):
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"""Weighted average of client deltas."""
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avg_state = {}
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total = sum(client_weights)
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for k in global_model.state_dict():
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avg_state[k] = sum(
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w / total * upd[k] for upd, w in zip(client_updates, client_weights)
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)
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global_model.load_state_dict(avg_state)
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return global_model
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# Each round:
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# 1. broadcast global model
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# 2. clients train locally (with DP optionally)
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# 3. clients send model deltas (encrypted)
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# 4. server aggregates via secure aggregation
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```
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### Homomorphic encryption inference (TenSEAL CKKS)
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```python
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import tenseal as ts
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ctx = ts.context(ts.SCHEME_TYPE.CKKS, poly_modulus_degree=8192,
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coeff_mod_bit_sizes=[60, 40, 40, 60])
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ctx.global_scale = 2**40
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ctx.generate_galois_keys()
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x = ts.ckks_vector(ctx, [0.1, 0.5, -0.3, 0.7])
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W = [[0.2, -0.1, 0.4, 0.05]] # plaintext weights
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b = [0.1]
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# encrypted inference: y = W*x + b
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y_enc = x.matmul(W[0]) + b[0]
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y_plain = y_enc.decrypt()
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```
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### Secure aggregation (cross-device FL)
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```python
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# Bonawitz et al protocol sketch:
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# 1. Pairwise keys via Diffie-Hellman among N clients.
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# 2. Each client sends update + sum_{j} mask_{ij} - sum_{j} mask_{ji}.
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# 3. Server sums all -> masks cancel -> only aggregate revealed.
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# Tolerates dropouts via Shamir secret sharing of seeds.
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```
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### Confidential GPU inference (NVIDIA H100 CC)
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```bash
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# Boot CC mode
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nvidia-smi conf-compute -srs 1
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# Verify attestation
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nvidia-smi conf-compute -gar
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# Application gets encrypted GPU-CPU bus + attested code
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```
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### Machine unlearning (SISA)
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```python
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# Sharded, Isolated, Sliced, Aggregated:
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# 1. Shard data into K disjoint parts; train K models.
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# 2. Aggregate (vote/avg) for inference.
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# 3. To unlearn user u: retrain only the shard containing u.
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# Cost: O(1/K) of full retrain.
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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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| Single org, sensitive labels | DP-SGD |
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| Many phones / hospitals | Federated + secure agg + DP |
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| Cloud inference, untrusted server | TEE (H100 CC) or HE |
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| Two parties, joint model | MPC (CrypTen, MP-SPDZ) |
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| GDPR right-to-be-forgotten | SISA / approximate unlearning |
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| Need to share data externally | DP synthetic data |
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**기본값**: TEE (confidential computing) for inference; DP-SGD + federated for training across orgs.
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## 🔗 Graph
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- 부모: [[Privacy]] · [[Practical-Cryptography|Cryptography]] · [[Machine-Learning]]
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- 변형: [[Differential-Privacy]] · [[Federated-Learning]] · [[Homomorphic-Encryption]] · [[Secure-Multi-Party-Computation]]
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- 응용: [[On-Device-ML]]
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- Adjacent: [[Synthetic-Data]]
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## 🤖 LLM 활용
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**언제**: regulated data (HIPAA, GDPR, PCI), cross-org training, on-device personalization, untrusted-cloud inference.
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**언제 X**: public data, no privacy requirement — overhead not worth it.
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## ❌ 안티패턴
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- **Big epsilon (ε>10)**: 매 effectively no privacy.
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- **Federated without DP or secure agg**: gradients leak training data.
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- **HE for entire training**: 1000x slowdown — only feasible for inference of small models.
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- **Anonymization theater**: removing names is not privacy (re-id attacks trivial).
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- **Trust me bro confidential**: deploy without remote attestation.
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## 🧪 검증 / 중복
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- Verified (Apple PCC 2024, Google FL papers, NIST DP guidance, NVIDIA H100 CC docs 2024).
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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 — 4 pillars + TEE / unlearning 2026 update |
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