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data-privacy-stack
presidio
Python2019–2026
data-privacy-stack / presidio

presidio

An open-source framework for detecting, redacting, masking, and anonymizing sensitive data (PII) across text, images, and structured data. Supports NLP, pattern matching, and customizable pipelines.

12Distinct people
14Mentions
0Explicit recommenders
11.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

An open-source framework for detecting, redacting, masking, and anonymizing sensitive data (PII) across text, images, and structured data. Supports NLP, pattern matching, and customizable pipelines.

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 1:03:20 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year14 dated mentions

Observed mentions: 2019-08-27 16:20:59 UTC to 2026-01-14 20:53:17 UTC.

02462019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 7 mentions; 1 submissions, 6 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 3 mentions; 2 submissions, 1 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC716
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC101
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC220
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC321
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

7 sources
Hey, I'm one of the maintainers of Presidio OSS. We built this tool mainly for Microsoft's strategic customers and decided to open-source it for others to use. No hidden agenda here. The engine has two main advantages: It's easily expandable and customizable, and it works well at scale. We do know of organizations who are very close to production with it. Every organization has their own requirements for PII entities, many of them specific to the org itself, so the engine allows a developer to easily add support for new PII entities using code, regex or black-lists. See hree: https://github.com/microsoft/presidio/blob/master/docs/custo... As for the productization aspect, we did some performance tests and are confident with taking it to production. Single instance cluster with a medium size machine has a ~65ms response time for a 100 word
Read the rest
sentence. Using better machines lowers the response time to ~24ms for 100 words and 150ms for a 1,000 words input. The current service uses regex for known patterns and Spacy for named entity recognition (person names, places etc.). Users often built custom ML models to detect new types of entities. Presidio is free, completely transparent, and fully customizable. Feel free to use it and let us know what you think.
In Presidio: Customizable data protection and PII data anonymization service

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