vault-engine

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A local-LLM privacy layer for anything you paste into a cloud model.

Strip the identities out of your text before it reaches ChatGPT / Claude / Gemini — a model running on your own machine finds the names, orgs, places and quasi-identifiers, replaces them with stable tokens, and keeps the only key-back-to-reality on disk. When the cloud answers in tokens, you put the real identities back locally.

Best-effort de-identification — not legal anonymization or a privacy guarantee. Review high-risk material before sending.

出云前做身份脱敏：本地模型检测 → 代号化 → 云端用代号分析 → 本地还原真身。 检测不出本机，身份映射只存本地，大模型一行换。零依赖。

 ·  Python ≥3.9  ·  stdlib-only  ·  Apache-2.0

# notes.txt  ── private, on your machine
林若曦是星澜资本的合伙人，在深圳见了字节跳动的陈大壮，邮箱 lin@xinglan.example

        ▼  vault-engine scrub  (local qwen3.6:27b)

# safe.txt  ── what the cloud sees: identities swapped for tokens
P-n1 是 ORG_1 的合伙人，在 LOC_1 见了 ORG_2 的 P-n2，邮箱 EMAIL_1

---

Why

You want a frontier cloud model to analyze sensitive notes — but you don't want the cloud to learn who they're about. Masking only the names you already know leaks everything you don't: an unregistered name, an employer, a city + a rare title, a project codename. Pattern-based redaction never sees those at all.

vault-engine puts a local model in front as the detector, so the semantic identifiers get caught too — and nothing but the sanitized text ever leaves.

How it works

 private text                                    cloud model
      │                                          (sees only tokens)
      ▼                                                ▲
┌─────────────────────────── vault-engine ────────────┼───────────┐
│  ① regex PII detectors  (offline floor)              │           │
│  ② LLM detector         (local model finds names,    │           │
│                          orgs, places, quasi-IDs)    │           │
│  ③ consistent pseudonyms (张三→P-n1, 同名同号)        │           │
│  ④ residual-risk critic  (re-scan: anything left?)   │  ① send   │
│        │                                             │           │
│   sanitized text ────────────────────────────────────┘           │
│        ▲                                                          │
│   reverse map (token → real identity) ── stays LOCAL ──┐  ② reply │
│        └───────────────────── ⑤ rehydrate ◀────────────┘          │
└──────────────────────────────────────────────────────────────────┘
      ▼
 real identities restored locally → use in your own system

Benchmark

How much identity each detector actually catches, on a labelled bilingual dataset (reproduce with python eval/run_eval.py; methodology in eval/):

77 gold identities across 15 bilingual documents — easy PII plus hard cases (ambiguous common-word names, abbreviations, transliterations, @handles, a badge number, a license plate). Reproduce: python eval/run_eval.py --provider ollama --with-presidio.

⚠️ A small synthetic set for regression testing and rough comparison — not evidence of legal anonymization or complete privacy. "Recall" means flagged-for-redaction; LLM detection is non-deterministic. See the threat model.

detector	person	org	location	project	contact	id	overall	over-redaction
regex only	0%	0%	0%	0%	69%	33%	13%	0%
Microsoft Presidio (en/zh lg)	78%	59%	80%	33%	38%	0%	61%	4%
vault-engine (qwen2.5:7b, 4.7 GB)	100%	100%	100%	100%	100%	100%	100%	2%
vault-engine (qwen3.5:9b, 6.6 GB)	100%	94%	100%	100%	100%	100%	99%	0%
vault-engine (qwen3.6:27b, 17 GB)	100%	100%	100%	100%	100%	100%	100%	0%

Same set where Presidio's NER scores 61%, local LLMs clear ~100% — gap widest on codenames, @handles, IDs, and Chinese names/orgs. Detection doesn't need a big model: a 4.7 GB qwen2.5:7b matches the 27b's recall at ~4 s/doc (vs ~25 s/doc), so an ordinary laptop is enough. Presidio stays far faster (~0.4 s/doc) where its coverage suffices.

The point isn't a leaderboard — it's the shape: pattern-only redaction can't see names, organizations, locations, or codenames at all; a local LLM can.

Install

pip install vault-engine

Or get the latest straight from source:

pip install git+https://github.com/fishonbike/vault-engine

For the default local backend, install Ollama and pull a model:

ollama pull qwen3.6:27b

No model yet? The deterministic floor (emails, phones, IDs, cards, URLs) works with zero setup via --no-llm.

Quickstart

vault-engine scrub notes.txt -o notes.safe.txt

That writes notes.safe.txt (send this to the cloud) and notes.safe.txt.map.json (local only — the identities). Paste the sanitized text into your model, save its reply, then restore the real identities:

vault-engine rehydrate reply.json --map notes.safe.txt.map.json -o reply.real.json

The clipboard one-liner

The fastest path — scrub whatever you're about to paste into a chatbot, in place:

vault-engine clip               # de-identifies the clipboard
#   …paste into ChatGPT/Claude, copy its reply, then:
vault-engine clip --rehydrate   # restores the real identities in the clipboard

Works on macOS, Windows, and Linux (with xclip/xsel/wl-clipboard).

Library:

from vaultengine import deidentify, rehydrate, Config

result = deidentify(open("notes.txt").read(), Config(model="qwen3.6:27b"))
send_to_cloud(result.text)                  # tokens only
restored = rehydrate(get_cloud_reply(), result.vault)   # real identities, locally
result.vault.save("notes.map.json")         # the reverse map — keep it local

Use cases

• Pseudonymize before pasting into ChatGPT/Claude — analyze private notes, contracts, or chats with direct identifiers stripped.
• Redact logs & support tickets before sharing them or feeding an LLM.
• Anonymize a dataset for LLM-assisted analysis, then map results back.
• Air-gapped review loops — a model on a locked-down box only ever sees tokens.

How it compares

Presidio and LLM Guard are excellent, mature tools. vault-engine's bet is different: a local LLM as the detector catches semantic/quasi-identifiers that label-based NER misses, with zero runtime deps and first-class Chinese.

	vault-engine	Presidio	LLM Guard (Anonymize)	regex / scrubadub
Detection	local LLM + regex	NER (spaCy) + regex	NER / transformers	patterns only
Unregistered names / orgs / quasi-IDs	✅ LLM	⚠️ NER labels only	⚠️ NER-limited	❌
Reversible round-trip	✅ local map	✅ deanonymizer	✅ Vault	❌
Fully local / offline	✅ Ollama	✅	⚠️ varies	✅
Runtime dependencies	none (stdlib)	spaCy + models	several	varies
Chinese (中文)	✅ strong	⚠️ needs model	⚠️	❌
Swap the model	✅ one line	—	partial	—
Fail-loud if detector errors	✅ degrades + non-zero exit	—	—	—

Redaction policy (privacy ↔ utility)

--policy	Persons	Orgs / places / roles	Dates	Token shape
balanced (default)	✅	✅ typed (ORG_1, LOC_2)	kept	typed
max	✅	✅ opaque R_1 (type hidden)	coarsened	opaque
light	✅	left in place	kept	typed

balanced keeps coarse structure — the cloud still reads "ORG_1 hired P-n2 as ROLE_1 in LOC_1" and can reason about it, while no real identity ships. Persons are tokenized in every policy.

Swap the model

vault-engine models                                   # list local Ollama tags
vault-engine scrub notes.txt --model qwen3.6:35b-a3b  # any local model
vault-engine scrub notes.txt --provider null          # offline, regex only

Built-in providers: ollama (default), openai-compat (any OpenAI-style endpoint — opt-in; ⚠️ sends raw text to that endpoint), null (offline). Add your own by implementing one method (complete) and registering it.

⚠️ Security model — read this

• The reverse map (*.map.json) is the identity. It's the only thing that links tokens back to real people. Keep it local. Never send it to a cloud model, never commit it — .gitignore excludes *.map.json and the CLI warns every run. Use --one-way to produce no map (irreversible publish).
• Detection stays local by default. Only the sanitized text is meant to leave, and only when you send it.

Threat model & limitations (honest)

• LLM detection is best-effort, not a guarantee of non-identifiability — a model can miss a name or a rare quasi-identifier. It is not k-anonymity or differential privacy.
• The critic pass and the risk report reduce and surface residual risk; they don't certify its absence. Writing style and domain-unique facts can still identify with names removed — use max for higher-stakes material.
• If the model backend is unreachable, the run degrades to regex-only and exits non-zero (--allow-degraded to override) — it will never silently ship under-redacted text.

Protecting code & schemas (--format markdown)

With --format markdown (or auto, which switches on at a fenced block), anything inside fenced code blocks is preserved verbatim — a JSON reply-schema or code sample you include for the model survives untouched while the prose around it is scrubbed. Pre-existing placeholder tokens (e.g. P-7) pass through unchanged.

Development

python -m unittest discover -t . -s tests -v   # 59 tests, offline, no model
python eval/run_eval.py --provider ollama       # reproduce the benchmark

Fully offline and deterministic (null/fake providers); every fixture is synthetic — no real data lives in this repo.

License

Apache-2.0 © 2026 fishonbike. See LICENSE and NOTICE.