# @mobiletic/anonymizer

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Framework-agnostic **PII anonymization & pseudonymization** for TypeScript/JavaScript.

It replaces personal data in text with stable placeholders before the text leaves your trust boundary
(e.g. before sending it to a third-party LLM, log sink, or analytics pipeline), and restores the real
values afterwards — including across **streamed** tokens.

- 🔌 **Pluggable LLM detection** — catch free-form PII (names, addresses) via any OpenAI-compatible
  endpoint, or your own provider.
- 🧩 **Optional regex fallback** — structured identifiers (email, phone, IBAN, …) via configurable
  presets (`swiss`, `generic`) or your own. Opt in for graceful degradation, or omit it to **fail closed**.
- 🏷️ **Self-describing tokens** — every result ships a `legend` (`PER`→`Personne`, `M`→`Masculin`) you can
  hand to the downstream LLM so it understands the placeholders; the model may coin new abbreviations too.
- 🔁 **Deterministic coreference** — the same person keeps the same id (`[PER_1]`) across a question and
  every retrieved chunk, with automatic de-collision.
- 🌊 **Streaming-safe** — a placeholder split across two stream chunks (`[PER_` + `1.NOM:M]`) is never
  leaked partially.
- 🪶 **Zero runtime dependencies**, ESM + CJS, fully typed.

> Built by [Mobiletic](https://mobiletic.com).

## Install

```bash
npm install @mobiletic/anonymizer
```

Requires Node ≥ 18 (uses native `fetch`).

## Quick start

### Regex-only (no LLM, fully deterministic)

```ts
import { Anonymizer, presets } from '@mobiletic/anonymizer';

const anonymizer = new Anonymizer({ patterns: presets.swiss });

const { anon, mapping, legend } = await anonymizer.anonymize('Écris à jean@exemple.ch');
// anon    -> "Écris à [EMAIL_1]"
// mapping -> { "[EMAIL_1]": "jean@exemple.ch" }   (secret — keep on your side)
// legend  -> { "EMAIL": "Adresse e-mail" }        (safe to share downstream)

anonymizer.deanonymize(anon, mapping); // -> "Écris à jean@exemple.ch"
```

### With an LLM (also catches names, addresses…)

```ts
import { Anonymizer, openAICompatibleProvider, presets } from '@mobiletic/anonymizer';

const anonymizer = new Anonymizer({
  llm: openAICompatibleProvider({
    baseUrl: process.env.LLM_BASE_URL!, // OpenAI, Infomaniak, vLLM, Ollama, …
    apiKey: process.env.LLM_API_KEY!,
    model: process.env.LLM_MODEL!,
    timeoutMs: 3000,
  }),
  patterns: presets.swiss, // OPTIONAL regex fallback if the LLM is down/misbehaves
});

const { anon, mapping, legend } = await anonymizer.anonymize('Le dossier de Alain Jaccard est complet.');
// anon   -> "Le dossier de [PER_1.NOM:M] est complet."
// legend -> { "PER": "Personne", "NOM": "Nom de famille", "M": "Masculin" }
```

**Fallback is opt-in (fail-closed).** If a `patterns` fallback is configured, a failed/timed-out/invalid
LLM call degrades to the regex engine. If you omit `patterns`, there's nothing to degrade to, so the call
**throws an `AnonymizationError`** (with the underlying error as `.cause`) — it never silently returns
un-anonymized text. With no fallback the pre-filter is also bypassed, so every non-trivial call consults
the LLM (more calls, no leaks). At least one of `llm` or `patterns` is required.

`openAICompatibleProvider` retries **transient** failures (network error, timeout, HTTP 429/5xx) before
giving up; 4xx and malformed responses are not retried. Tune with `timeoutMs` (per attempt, default 3000),
`retries` (default 1 → 2 attempts), and `retryDelayMs` (linear backoff, default 250). Worst-case latency
is `(retries + 1) × timeoutMs`, so keep `retries` low on latency-sensitive paths.

### Streaming de-anonymization

When you stream an LLM answer back to a user, restore real values without ever emitting a half-written
placeholder:

```ts
const stream = anonymizer.makeStreamDeanonymizer(mapping);
for await (const token of llmTokens) process.stdout.write(stream.push(token));
process.stdout.write(stream.flush());
```

### Anonymizing retrieved chunks consistently (RAG)

`anonymizeChunks(chunks, seed)` reuses the question's `{ mapping, legend }` so the same person gets the
same id across the question and every chunk, batches the LLM call, and de-collides genuinely new entities:

```ts
const q = await anonymizer.anonymize(question);
const { anon, mapping, legend } = await anonymizer.anonymizeChunks(retrievedChunks, q);
// `mapping`/`legend` are the full question ∪ chunks tables;
// pass `mapping` to deanonymize()/makeStreamDeanonymizer(), and `legend` to the downstream LLM.
```

## Configuration

```ts
new Anonymizer({
  llm?,       // LlmProvider — omit for regex-only mode
  patterns?,  // PatternDef[] — opt-in regex fallback; omit to fail closed
  nameHint?,  // RegExp flagging likely names so the LLM is consulted (has a default)
  logger?,    // { warn(msg) } — receives fallback warnings; defaults to no-op
});
// At least one of `llm` or `patterns` must be provided, or the constructor throws.
```

### Presets & custom patterns

```ts
import { presets } from '@mobiletic/anonymizer';

presets.swiss; // AVS, IBAN CH, EMAIL, Swiss phone, DATE
presets.generic; // EMAIL, IBAN, credit card, IPv4, phone, DATE

// Compose / extend:
const patterns = [
  ...presets.generic,
  { tag: 'TICKET', re: /\bJIRA-\d+\b/g }, // patterns must use the global flag
];
```

Each pattern may carry an optional `validate(match) => boolean` second stage — a match is only redacted
if it passes. `presets.generic` uses it for a [Luhn](https://en.wikipedia.org/wiki/Luhn_algorithm) check
so arbitrary long digit runs aren't mistaken for credit cards:

```ts
{ tag: 'CREDIT_CARD', re: /\b\d(?:[ -]?\d){12,18}\b/g, validate: luhnValid }
```

> The `generic` preset is a **best-effort** starting point — broad patterns (phone, date, card) can
> overlap. For production use, prefer a locale-specific preset (`presets.swiss`) or your own patterns.

### Custom LLM provider

Implement `LlmProvider` to use any backend (Anthropic, a local model, a rules engine…):

```ts
import type { LlmProvider } from '@mobiletic/anonymizer';

const myProvider: LlmProvider = {
  isConfigured: () => true,
  async anonymize(text) {
    /* return { anon, mapping, legend } */
  },
  async anonymizeBatch(texts, usedIds) {
    /* return { segments, mapping, legend } */
  },
};
```

## Placeholder format

```
[PER_1.NOM:M]   entity PER #1, attribute NOM, context M (rich, from the LLM)
[EMAIL_1]       structured id from the regex fallback
```

`PLACEHOLDER_RE` is exported if you need to scan text for placeholders. The LLM may also **coin new
abbreviations** (always uppercase `[A-Z_]`) for entities/attributes/context it discovers — every one it
uses is described in the result `legend`.

## How it works

The library does **query-time pseudonymization**: it rewrites text so that personal data never leaves your
trust boundary in identifiable form, while keeping the answer fully reversible on your side.

```
                        ┌───────────────────────────── your trust boundary ──────────────────────────────┐
  raw text  ─▶  ① pre-filter ─▶ ② LLM detect ─▶ ③ regex fallback ─▶ ④ validate ─▶  anon + mapping + legend
                  (skip if            (names,         (optional;          (every                │       │
                   clearly            addresses,      structured ids;     placeholder    ┌───────┘       │
                   PII-free)          coins abbrevs,  fail closed if      is mapped)      ▼               ▼
                                      builds legend)  absent)                          mapping         legend
                                                                                     (SECRET,        (shareable:
   downstream LLM  ◀── anon + legend ─────────────────────────────────────────────  reversible)     PER=Personne…)
   answer (with [PER_1.NOM:M] tokens)
        │
        ▼
  ⑤ stream de-anonymize ──▶ real values restored for the end user (placeholders never leak, even if split)
```

1. **Pre-filter** — a cheap regex/name check skips the LLM round-trip for text that clearly has no PII.
   (Bypassed when no regex fallback is configured, so nothing slips through.)
2. **LLM detection** — finds free-form PII a regex can't (names, addresses), keeps **coreference** (the
   same person is always `[PER_1]`), coins uppercase abbreviations for anything new, and returns a
   **legend** describing them.
3. **Regex fallback** — optional, deterministic detection of structured identifiers; used if the LLM is
   unavailable. Omit it to **fail closed** (raise `AnonymizationError` rather than risk a leak).
4. **Validation** — bidirectional check that every placeholder has a mapping entry and vice-versa.
5. **Streaming de-anonymization** — `makeStreamDeanonymizer` restores real values token-by-token, buffering
   any placeholder split across chunks so a partial `[PER_` is never emitted.

Three distinct outputs, with different sensitivities:

| Output    | Example                                  | Sensitivity                                             |
| --------- | ---------------------------------------- | ------------------------------------------------------- |
| `anon`    | `Le dossier de [PER_1.NOM:M]`            | Safe to send onward — no identifiers                    |
| `mapping` | `{ "[PER_1.NOM:M]": "Alain Jaccard" }`   | **Secret** — re-identifies people; keep it on your side |
| `legend`  | `{ "PER": "Personne", "M": "Masculin" }` | Safe to share — explains the tokens to a downstream LLM |

## How this helps with the nLPD

Switzerland's [nLPD](https://www.fedlex.admin.ch/eli/cc/2022/491/fr) (and the GDPR) push for **data
minimisation** and favour **pseudonymisation** when personal data is processed by third parties. This
library is built around those principles:

- **The third party never sees raw PII.** When you send text to an external LLM (or any external service),
  it receives only pseudonymised tokens like `[PER_1.NOM:M]` plus the non-identifying `legend` — never the
  real name, e-mail, AVS number, etc.
- **Pseudonymisation, not loss of meaning.** The re-identification key (`mapping`) stays in your
  infrastructure; only you can reverse the tokens. The `legend` lets the downstream model still reason
  correctly ("a person", "male") without knowing _who_.
- **Fail-closed option.** Omitting the regex fallback means that if detection can't run, the call errors
  instead of forwarding data that wasn't pseudonymised — no silent leak.
- **Coreference & minimisation.** Re-using one id per entity avoids spreading extra distinguishing detail
  across a prompt, and the corpus itself can stay in clear text — pseudonymisation happens only at the
  boundary, at query time.

> This is an engineering aid, not legal advice or a certification. You remain the data controller; assess
> it against your own obligations (see the disclaimer below).

## Compliance note

This library is a **best-effort** pseudonymization aid, not a guarantee of legal compliance. LLM and regex
detection can miss or mis-classify data. Validate against your own requirements (nLPD, GDPR, HIPAA, …)
before relying on it for regulated data.

## License

[MIT](./LICENSE) © Mobiletic