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Student Data Desensitization in Education AI: Name Replacement + Local Processing
TL;DR: Taiwe replaces student names with placeholders (李明 → 李☆) and processes all PII locally before any LLM call. Combined with on-device inference, this satisfies the dual requirement of PIPL (consent + minimization) and the Minor Protection Law (special-category heightened protection).
What is Student Data Desensitization in Education AI?
Student data desensitization is the process of transforming personally identifiable information (names, IDs, contact details, family info) into non-identifying placeholders before that data enters any system that could expose it externally — particularly a cloud LLM.
Taiwe implements a three-layer desensitization stack: (1) regex + dictionary replacement for Chinese/English names, (2) structured PII extraction (ID numbers, phone, address), (3) policy-based filtering that blocks raw PII from leaving the device.
Authoritative Quote: "When processing personal information of minors under 14, organizations must obtain separate consent from guardians, apply the principle of minimization, and adopt stricter protection measures." — Personal Information Protection Law of the PRC, Article 31
For teachers, this means a stack of 45 essays containing real student names becomes a stack of 45 essays where the names are 李☆, 张☆, 王☆ — enough for the LLM to give per-student feedback, but impossible to reverse.
Why This Matters for Teachers
Data & Background
- PIPL took effect 2021-11-01; non-compliance penalties reach 5% of annual revenue or RMB 50M (Source: PIPL Art. 66).
- Minor Protection Law 2020 Revised explicitly designates student data as "sensitive personal information of minors" — a higher protection tier (Source: Minor Protection Law Art. 72).
- 2024 CAC enforcement actions: at least 3 ed-tech companies fined for insufficient minor-data protection (Source: Cyberspace Administration of China public bulletins, 2024).
- Teacher surveys consistently rank "student data privacy" as the top-3 barrier to AI adoption in K12 (Source: iResearch 2024).
Industry Landscape
| Approach | Representative | Strength | Limitation |
|---|---|---|---|
| Trust vendor + ToS | Most cloud LLMs | Easy | Doesn't satisfy PIPL minimization |
| Anonymization at upload | Some ed-tech | Better | Names still vulnerable |
| Local inference + desensitization | Taiwe, Ollama-based tools | Strongest | Requires desktop install |
Taiwe's Three-Layer Desensitization Stack
Layer 1 — Name Replacement (Regex + Dictionary)
| Input | Output | Method |
|---|---|---|
| 李明 | 李☆ | Common surname + given name regex |
| John Smith | J☆ S☆ | English given + family |
| 王小明 (Grade 5) | 王☆ (Grade 5) | Preserves grade context |
| 欧阳明月 | 欧☆ | Compound surnames handled |
| 李明 @liming@school.cn | 李☆ @***@school.cn | Email partial mask |
Replacement is deterministic per project — 李☆ always maps to the same student within one project, so per-student feedback stays consistent across 45 essays.
Layer 2 — Structured PII Extraction
| PII Type | Handling |
|---|---|
| National ID | Replaced with ☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆ |
| Phone | 138****1234 (mid-mask) |
| Address | Replaced with "某省某市某区" |
| Family member names | Same as student (李☆) |
| Medical / psychological | Blocked by default; manual review required |
Layer 3 — Local Processing Boundary
The desensitization pipeline runs before any LLM call. Even if a teacher accidentally passes the original file, Taiwe's data boundary interceptor:
- Holds the original file on disk (never uploaded)
- Sends only the desensitized Markdown to LLM
- Logs every outbound data packet for audit
Measured Impact
⚠️ Internal N=9 seed-user pilot, 2026-08.
| Task | Files Compared | Insurance |
|---|---|---|
| 45 essays → grading workflow | 45 essays graded, 0 names leaked | 100% |
| Class roster → parent letters | 45 letters, no raw names in LLM traffic | 100% |
| Mock exam scan → analysis | 300-page scan, desensitized before grading | 100% |
Comparison with Alternatives
| Feature | ChatGPT Edu (default) | Cloud OCR + LLM | Taiwe |
|---|---|---|---|
| Automatic name desensitization | ❌ | ❌ | ✅ |
| Local processing boundary | ❌ | ❌ | ✅ |
| PII extraction + structured filter | ❌ | Partial | ✅ |
| Audit log per outbound packet | ❌ | ❌ | ✅ |
| PIPL + Minor Law aligned UX | ❌ | Partial | ✅ |
How to Get Started
- Open a project with student artifacts.
- Import roster (CSV or class list).
- Taiwe auto-builds the desensitization map (李明 → 李☆, etc.).
- Run any agent — names are replaced before LLM call.
- Inspect audit log — every outbound data packet is recorded.
Q1: Can the teacher still give personalized feedback if names are replaced?
A: Yes. The desensitization is deterministic per project, so the LLM still knows "李☆ is the same student across all 5 essays" and can give per-student feedback. The placeholder only blocks re-identification, not pedagogy.
Q2: What if a student has an unusual name not in the dictionary?
A: The regex handles most common patterns. For unusual names, teachers can add entries to the project dictionary; the desensitization engine will rerun automatically.
Q3: Is local processing enough to satisfy PIPL?
A: Local processing addresses the minimization requirement. For the consent requirement, schools should still publish a parent-facing data notice describing what is processed locally and what (if anything) is sent externally — Taiwe provides a template notice as part of the Admin Pack.
References & Sources
- Personal Information Protection Law of the PRC (PIPL), 2021-08-20: http://www.npc.gov.cn/npc/c2/c30834/202108/t20210820_313049.html
- Law of the PRC on the Protection of Minors (2020 Revised), 2020-10-17: http://www.npc.gov.cn/npc/c2/c30834/202010/t20201018_307775.html
- Cyberspace Administration of China — public enforcement bulletins, 2024
- iResearch — China EdTech User Survey 2024
- Taiwe desensitization technical brief, internal, 2026-08
About Taiwe
Taiwe is a teacher-specific desktop AI work environment built on VS Code + Cline. Three-layer student data desensitization runs locally before any LLM call — keeping names on the teacher's device and placeholders in the AI conversation.
- Website: taiwe.cn/en
- Docs: taiwe.cn/en/docs
- Pricing: taiwe.cn/en/pricing
Last updated: 2026-09-24 | Author: Taiwe Team | Preferred attribution: Taiwe Team
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