---
id: wiki-2026-0508-data-privacy-local-processing
title: "Data Privacy & Local Processing"
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [On-device AI, Local-First, Privacy-Preserving ML, Edge Privacy]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [privacy, local-first, on-device, gdpr, federated-learning]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python/Swift/Rust
  framework: MLX / CoreML / ONNX Runtime / Flower
---

# Data Privacy & Local Processing

## 매 한 줄
> **"매 data privacy + local processing 의 핵심: data minimization + on-device inference + cryptographic guarantees"**. 매 GDPR (2018), CCPA, AI Act (2024 EU) 의 regulatory pressure + 매 Apple Intelligence (2024), Google Gemini Nano (2024), 매 on-device LLM (Llama 3.2 1B/3B, Phi-4 mini, Gemma 3 nano) 의 등장 으로 매 2026 현재 cloud → device shift 가 현실화. 매 Local-First Software 운동 의 main-stream 진입.

## 매 핵심

### 매 privacy primitives
- **Data minimization**: 매 collect only 필요 — 매 GDPR Art. 5(1)(c).
- **On-device inference**: 매 raw data 의 device 외 미전송.
- **Differential Privacy (DP)**: 매 ε-noise — 매 Apple, Google 의 telemetry 사용.
- **Federated Learning (FL)**: 매 model 의 device 학습 → gradient aggregate.
- **Homomorphic Encryption (HE)**: 매 encrypted compute — 매 latency penalty 큼.
- **Secure Enclave (TEE)**: 매 Apple Secure Enclave, Intel SGX, AWS Nitro.
- **Zero-Knowledge Proof (ZKP)**: 매 prove without reveal.

### 매 regulatory landscape (2026)
- **EU AI Act**: 매 high-risk system 의 data governance + transparency.
- **GDPR**: 매 right to erasure, data portability, DPIA.
- **CCPA / CPRA**: 매 California 의 sale opt-out.
- **HIPAA** (US health), **PIPEDA** (Canada), **APPI** (Japan), **PIPL** (China — 매 cross-border data transfer 매우 strict).

### 매 응용
1. On-device LLM assistant (Apple Intelligence, Pixel Gemini Nano).
2. Health apps (HealthKit, on-device biometric ML).
3. Federated keyboard prediction (Gboard, SwiftKey).
4. Local-first note apps (Obsidian, Anytype, automerge-based).

## 💻 패턴

### On-device LLM with MLX (Apple Silicon)
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Llama-3.2-3B-Instruct-4bit")
out = generate(model, tokenizer, prompt="Summarize: ...",
               max_tokens=200, verbose=False)
# Data never leaves device
```

### Differential privacy (Opacus)
```python
from opacus import PrivacyEngine

privacy_engine = PrivacyEngine()
model, optimizer, loader = privacy_engine.make_private_with_epsilon(
    module=model, optimizer=optimizer, data_loader=loader,
    epochs=10, target_epsilon=3.0, target_delta=1e-5, max_grad_norm=1.0,
)
```

### Federated learning (Flower)
```python
import flwr as fl

class Client(fl.client.NumPyClient):
    def get_parameters(self, config): return get_weights(model)
    def fit(self, parameters, config):
        set_weights(model, parameters)
        train_local(model, local_data)
        return get_weights(model), len(local_data), {}

fl.client.start_numpy_client(server_address="server:8080", client=Client())
```

### CoreML on-device inference (iOS / macOS)
```swift
import CoreML
let model = try MyModel(configuration: MLModelConfiguration())
let input = try MyModelInput(text: userText)
let output = try model.prediction(input: input)
// Inference never sends data to network
```

### Secure Enclave key wrapping (iOS)
```swift
let attrs: [String: Any] = [
  kSecAttrKeyType as String: kSecAttrKeyTypeECSECPrimeRandom,
  kSecAttrKeySizeInBits as String: 256,
  kSecAttrTokenID as String: kSecAttrTokenIDSecureEnclave,
]
var error: Unmanaged<CFError>?
let key = SecKeyCreateRandomKey(attrs as CFDictionary, &error)!
```

### Local-first sync (Yjs / Automerge)
```ts
import * as Y from "yjs";
import { IndexeddbPersistence } from "y-indexeddb";

const doc = new Y.Doc();
new IndexeddbPersistence("notes", doc);  // Local persistence
// Optional E2E-encrypted relay for sync
```

### Data redaction before LLM API call (defense in depth)
```python
import re
PII = [r"\b\d{3}-\d{2}-\d{4}\b", r"\b[\w.-]+@[\w.-]+\b"]
def redact(text):
    for p in PII:
        text = re.sub(p, "[REDACTED]", text)
    return text
# Use redact() before sending to remote LLM
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Health / financial data | On-device only + TEE |
| Personalized model | Federated learning |
| Aggregate analytics | Differential privacy |
| Multi-party compute | HE / MPC (still slow) |
| Compliance (GDPR / HIPAA) | DPIA + minimization + audit log |
| Personal AI assistant | Local LLM (Llama 3.2 3B 4-bit on phone) |

**기본값**: 매 user-content processing 의 default 의 on-device, 매 cloud 의 explicit consent + minimization.

## 🔗 Graph
- 부모: [[Privacy]]
- 변형: [[Federated Learning]] · [[Differential Privacy]] · [[Homomorphic Encryption (HE)]]
- 응용: [[On-device AI]]
- Adjacent: [[Edge Computing]] · [[Practical-Cryptography|Cryptography]] · [[GDPR]]

## 🤖 LLM 활용
**언제**: 매 privacy-impact-assessment drafting, 매 redaction-pipeline scaffolding, 매 GDPR/CCPA compliance checklist generation.
**언제 X**: 매 actual user PII 의 cloud LLM 의 직접 send X — 매 on-device 또는 redact-first.

## ❌ 안티패턴
- **Plaintext PII to cloud LLM**: 매 GDPR violation potential.
- **DP without ε accounting**: 매 cumulative leakage 의 무인지.
- **Federated 의 raw gradient leak**: 매 gradient inversion attack — 매 secure aggregation 필요.
- **Local-first 의 backup absent**: 매 device loss = data loss.
- **"Anonymized" via removing names only**: 매 quasi-identifier 의 re-identification.
- **Storing decryption key alongside ciphertext**: 매 obvious 하지만 흔한 fail.

## 🧪 검증 / 중복
- Verified (GDPR text, NIST Privacy Framework, Apple Differential Privacy white papers, Flower & Opacus docs, EU AI Act 2024).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — privacy primitives + on-device LLM 2026 |
