Tool usage guide

Learn when to use this tool, what it supports, and how real users apply it.

Key facts

Category: Text Processing
Input types: textarea, select, checkbox, text
Output type: json
Sample coverage: 4
API ready: Yes

Overview

Consistent Pseudonym Text Anonymizer detects selected common PII in pasted text and replaces repeated entities with stable labels, synthetic placeholders, or deterministic hashes. It returns anonymized text and JSON details such as the unique entity count.

When to use

Anonymize logs, notes, or text containing repeated email addresses, phone numbers, IP addresses, or other supported identifiers.
Share sanitized text while preserving consistency for the same entity throughout the content.
Create repeatable pseudonyms with a mapping seed when deterministic replacements are needed.

How it works

1Paste text containing the entities you want to anonymize into Text Content.
2Select one or more entity types, such as email addresses, phone numbers, IPv4 addresses, API keys, or names after name labels.
3Choose Stable Labels, Synthetic Placeholders, or Deterministic Hashes as the replacement mode.
4Optionally include the entity mapping and provide a mapping seed for deterministic results, then receive the anonymized text as JSON.

Use cases

Sanitizing application logs before sharing them with a support or engineering team.
Preparing privacy-conscious datasets or text samples while preserving repeated-entity relationships.
Replacing contact details and identifiers in test documents with stable labels or hashes.

Examples

1. Keep repeated contacts consistent

Support engineer

Background

A support engineer needs to share a text extract that contains the same customer email address twice and a phone number.

Problem

The contact details must be removed without changing repeated occurrences into unrelated replacements.

How to use

Paste the text, select Email Addresses and Phone Numbers, choose Stable Labels, and enable Include Mapping.

{"entityTypes":["email","phone"],"replacementMode":"label","includeMapping":true,"seed":"demo"}

Outcome

The repeated email becomes EMAIL_1 in both locations, the phone becomes PHONE_1, and the JSON result reports 2 unique entities.

2. Use deterministic hashes for text sharing

Data analyst

Background

A data analyst is preparing sample text for review and wants repeated email addresses to remain linkable without returning a reverse mapping.

Problem

The same email appears multiple times, but the original address should not be exposed in the shared output.

How to use

Paste the text, select Email Addresses, choose Deterministic Hashes, leave Include Mapping disabled, and set a seed.

FAQ

What types of information can the tool detect?

It can detect email addresses, phone numbers, IPv4 addresses, credit card numbers, API keys and tokens, national ID cards, passport numbers, and names after name labels.

Are repeated entities replaced consistently?

Yes. The same detected entity receives the same pseudonym throughout the text.

Which replacement modes are available?

You can use stable labels, synthetic placeholders, or deterministic hashes.

Can I receive the replacement mapping?

Yes. Enable Include Mapping to request the mapping in the JSON result.

What does the Mapping Seed do?

The optional seed supports deterministic pseudonym generation, so the same input settings can produce repeatable replacements.

{"entityTypes":["email"],"replacementMode":"hash","includeMapping":false,"seed":"demo"}

Consistent Pseudonym Text Anonymizer

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Learn when to use this tool, what it supports, and how real users apply it.

Key facts

Overview

When to use

How it works

Use cases

Examples

1. Keep repeated contacts consistent

2. Use deterministic hashes for text sharing

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