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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+---
+id: wiki-2026-0508-secure-multi-party-computation
+title: Secure Multi-party Computation
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [MPC, SMPC, Secure Computation]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.88
+verification_status: applied
+tags: [cryptography, privacy, mpc, federated, ai-privacy]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: crypten
+---
+
+# Secure Multi-party Computation
+
+## 매 한 줄
+> **"매 N parties 가 jointly compute f(x1, ..., xN) without revealing inputs"**. Yao 1982 garbled circuits → BGW 1988 secret sharing → modern SPDZ, ABY3, CrypTen for privacy-preserving ML. 매 2026 production: Apple PCC (Private Cloud Compute), Meta CrypTen, Google federated analytics.
+
+## 매 핵심
+
+### 매 Primitives
+- **Secret sharing** (Shamir): 매 split secret into N shares, t+1 reconstruct.
+- **Garbled circuits** (Yao): 매 2-party Boolean circuit evaluation.
+- **Homomorphic encryption** (FHE/PHE): 매 compute on ciphertext.
+- **Oblivious Transfer** (OT): 매 sender sends 1 of 2, receiver picks without revealing.
+
+### 매 Threat models
+- **Semi-honest** (passive): 매 follow protocol but try to learn.
+- **Malicious** (active): 매 deviate arbitrarily — 매 needs MAC/zero-knowledge.
+- **Covert**: 매 cheat detected with high probability.
+
+### 매 Modern frameworks
+- **CrypTen** (Meta): PyTorch-style MPC for ML.
+- **MP-SPDZ**: 매 wide protocol library.
+- **TF-Encrypted**: TensorFlow MPC.
+- **Concrete** (Zama): TFHE for ML inference.
+
+### 매 응용
+1. Privacy-preserving ML inference (medical, financial).
+2. Federated learning aggregation (secure aggregation).
+3. Private set intersection (ad measurement).
+4. Apple PCC: 매 trusted enclave + attestation for LLM.
+
+## 💻 패턴
+
+### Shamir secret sharing
+```python
+import random
+from sympy import mod_inverse
+
+PRIME = 2**127 - 1
+
+def share(secret, n, t):
+    coeffs = [secret] + [random.randrange(PRIME) for _ in range(t-1)]
+    shares = []
+    for i in range(1, n+1):
+        y = sum(c * pow(i, j, PRIME) for j, c in enumerate(coeffs)) % PRIME
+        shares.append((i, y))
+    return shares
+
+def reconstruct(shares):
+    secret = 0
+    for i, (xi, yi) in enumerate(shares):
+        num, den = 1, 1
+        for j, (xj, _) in enumerate(shares):
+            if i != j:
+                num = (num * -xj) % PRIME
+                den = (den * (xi - xj)) % PRIME
+        secret = (secret + yi * num * mod_inverse(den, PRIME)) % PRIME
+    return secret
+```
+
+### CrypTen ML inference
+```python
+import crypten
+import torch
+
+crypten.init()
+
+# Two parties: server has model, client has input
+@crypten.mpc.run_multiprocess(world_size=2)
+def private_inference():
+    model = crypten.nn.from_pytorch(my_model, dummy_input)
+    model.encrypt(src=0)  # server holds model
+
+    x_enc = crypten.cryptensor(client_input, src=1)  # client input
+    y_enc = model(x_enc)
+    y = y_enc.get_plain_text()  # decrypt result
+    return y
+
+private_inference()
+```
+
+### Secure aggregation (federated learning)
+```python
+def secure_aggregate(client_updates, threshold):
+    # Each client masks update with random pad shared via DH
+    n = len(client_updates)
+    masks = [generate_pairwise_masks(i, n) for i in range(n)]
+    masked = [u + sum(masks[i]) for i, u in enumerate(client_updates)]
+    # Server sums — masks 매 cancel out
+    return sum(masked)  # 매 individual updates 매 hidden
+```
+
+### Garbled circuit (Yao 2PC)
+```python
+def garble_AND():
+    # 매 circuit: z = x AND y
+    keys = {(b1, b2): random.randbytes(16) for b1 in [0,1] for b2 in [0,1]}
+    output_keys = {0: random.randbytes(16), 1: random.randbytes(16)}
+    table = []
+    for (b1, b2), k_in in keys.items():
+        z = b1 & b2
+        ct = aes_encrypt(k_in, output_keys[z])
+        table.append(ct)
+    random.shuffle(table)
+    return table, output_keys
+```
+
+### TFHE inference (Zama Concrete)
+```python
+from concrete import fhe
+
+@fhe.compiler({"x": "encrypted"})
+def relu(x):
+    return fhe.maxes(x, 0)
+
+circuit = relu.compile([(i,) for i in range(-128, 128)])
+encrypted = circuit.encrypt(-5)
+result = circuit.run(encrypted)
+print(circuit.decrypt(result))  # 0
+```
+
+### Private set intersection
+```python
+def psi_dh(a_set, b_set):
+    # Diffie-Hellman based PSI
+    a_secret, b_secret = random_scalar(), random_scalar()
+    A_blinded = [hash_to_curve(x) ** a_secret for x in a_set]
+    B_blinded = [hash_to_curve(y) ** b_secret for y in b_set]
+    A_double = [p ** b_secret for p in A_blinded]
+    B_double = [p ** a_secret for p in B_blinded]
+    return set(A_double) & set(B_double)
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| 2-party ML inference | 매 Garbled circuits 또는 CrypTen |
+| N-party aggregation | 매 Secret sharing (BGW, SPDZ) |
+| Single ciphertext compute | 매 FHE (Concrete, Microsoft SEAL) |
+| Trusted hardware available | 매 TEE (SGX, Apple PCC) — 매 fastest |
+| Production LLM privacy | 매 Apple PCC pattern (TEE + attestation) |
+
+**기본값**: 매 ML inference 면 CrypTen (semi-honest 2PC), 매 production privacy LLM 면 TEE-based (Apple PCC).
+
+## 🔗 Graph
+- 부모: [[Practical-Cryptography|Cryptography]] · [[Privacy-Preserving ML]]
+- 변형: [[Homomorphic Encryption (HE)]]
+- 응용: [[Federated Learning]]
+- Adjacent: [[Differential Privacy]]
+
+## 🤖 LLM 활용
+**언제**: 매 multi-party data joint analysis, 매 client-side model with private data, 매 medical/financial cross-org compute.
+**언제 X**: 매 single-party compute (DP 면 충분), 매 latency-critical (MPC 매 100-1000× slower).
+
+## ❌ 안티패턴
+- **Semi-honest in production**: 매 malicious adversary 가능 면 fail.
+- **MPC for everything**: 매 100× overhead — TEE 가 better when available.
+- **Naive secret sharing**: 매 multiplication 매 expensive (Beaver triples 필요).
+- **Ignoring side-channels**: 매 timing/power leak — 매 protocol-only 매 부족.
+
+## 🧪 검증 / 중복
+- Verified (Yao 1982, BGW 1988, Damgård SPDZ 2012, CrypTen 2020).
+- Apple PCC technical paper 2024.
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — primitives, modern frameworks, Apple PCC 추가 |