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Վերադառնալ բլոգինՏեխնիկական

Presidio. 3-Šabatanoc Karcavorumë vs Karavarutyam PII

Microsoft Presidio-n ë GitHub-ë šat hazaravori asteghner uneç ev hariuravorî bac xndirner: Karcavorman buxtakutyunë, PySpark integraciai arsazmutyunë ev Python-i axavarkutyan...

June 5, 20266 րոպե կարդալ
Presidio setupPySpark integrationmanaged PresidioPython dependenciesPII setup complexity

Presidio. Arqiutyan Gorcik, Yerkar Karcavorumov

Noriçvac 2026-ovê:

Microsoft Presidio-ë harcadyun gorcik ê PII haytnabеrutyan ev de-identifikaciai hamar: Bayç ayn mec inženerarakan dznagir ë: Nranë artadrutyunum backarum ë iskakan ç'apêl: Hamaynoghutyanë dranë hamamayn ë:

GitHub-i xndir #237 lav orinaka ë: Naev habaguyn programmavornerë hamaxmbutyunayin handipumner en ançnum: Nranq model-i barcnulyum-i xafutyunneri ev API-i sxalutyunneri are entanum: Araçin kargar gorcut'yanic norenova aranc debug-i meratamut'yan ank andradzrel:

Inç ë Cuyç Talis Hamaynoghutyan Tvalyalnerê

Presidio GitHub-i bazumavori asteghner uneç en: Ayd vakar inzhekerutyun ê bnakutyun: Bayç bac xndirnerum avoraky ayl patmutyun ê:

Mijavaranayin xndirner. Python-i versioni handipumnerë terbak en: Naev spaCy model-i anhamapatasxanutyunnere ev ONNX runtime-i sxalutyunnere: Ayd xndirnerë xcum en programmavornerov, vornq hecelutyun en patkar:

Model-i barcnulyum xafutyunner. spaCy modelovnerë lav en bercvum, bayç orosh karcavorumneri mej barcnulyum en ëndlaynum: Container-nerë ev cax memory-i kazmaviçumnerë terbak xndir-maxejer en: Nranç štvutyunë spaCy-i internerum çarcelutyun ë paxanjatcum:

Artadrutyunayin API-i xafutyunner. Analyzer-ë lav ë kargar dev-um: Artadrutyunayin bèrî ëndlaynum ë xaxyum: Threading-i xndirnerë ev NLP modelovnerit memory-i ëntum-nerë glanvayin pricinneri en:

Integraciai arsazmutyun. Ploomber-i blog-ë ayd karavi masin lriv dzorutyunov carkanal ë: Ayn gorçarcutyum ê mek bazmaqan service-neri — analyzer-ë, anonymizer-ë ev petcinalli image redactor-ë: Nranç kapcumë aşxatanq ê aveleçnum: Service-neri mijèv tvalyalneri cackaçumë ev aylel aşxatanq ê aveleçnum:

Microsoft Fabric-i Dêpqê

Microsoft Fabric-i gagnakan docerë lriv çuyç ê talutyum «hascaneli» ev «kargar» mijèv:

Fabric-i blog-i grakanutyunë PySpark-i masin anmijakan sranç ë nshanumy: Karcavorumë "paxanjatcum ë kakhvaç arxorin kakhvaçutyunneri ev hazukayin tarkê karcavarutyam:" Fabric-i ogtagorçnere ëntrkeç karbavarutyam cloud platform, vor ayd tipov aşxatanqic kercanel: Bayç artorin gorcikner ëntkalutyunan buxtutyunë aylnov beri:

PySpark-i karcavorman kamkamner ev ënter.

  1. presidio-analyzer ev presidio-anonymizer utrakidz ancel Fabric notebooks-um:
  2. spaCy modelovnerë bercutyam Fabric-i mijavarancum:
  3. PySpark UDF wrapper-ner grel analyzer-i ev anonymizer-i hamar:
  4. spaCy model-i packing-ë karcanel Spark-i vorêutyan vra ogtagerèlou hamar:
  5. Lezvaynayin haytnabеrutyunë karcavel baravan-lezvov datasets-i hamar:

Amèn kame heçnum ë coc axtalovutyunan ejanak: Ayd urçov mec ardyunatakutyunë hètadzrel en mek ev erkù šabaт minçev nranq mšakutyunyam araçin pastatxtë mšakumy en:

Erkù Urç. Inqnahostë vs Karavarutyamê

Karavrutyam morçutyunë karcavorman patmutyanë šrjaberyum:

Inqnahost urç.

  1. Docker utrakidz barcanel:
  2. docker-compose.yml karcavel:
  3. spaCy modelovnerë bercutyam:
  4. Container-i cagamijutyunë debug-el:
  5. API endpoint-ner karcavel:
  6. Kazu haytnabеrutyunë test-el:
  7. Kexc drakannery ev bechetnerë štvutyam:
  8. Hazukayin ënknišxichner šinel astandartë kayzi kazu tiperov:
  9. Audit logging aveli:
  10. Artadrutyunayin bèri hamar adjustel:

Araçin de-identificelutyam pastatxti žamanake. yereqic minçev mekamatutyan or:

Karavarutyam service urç.

  1. Hozhap stexcel:
  2. Pastatxti barcnel kam API-ë kaçel:

Araçin de-identificelutyam pastatxti žamanakê. tasnerkù rose:

Erkù urçerë ogtagutyunyum en nuyneçan haytnabеrutyunan morçutyunë: Karavarutyam urçë katarutyam ê mek aylë mi hardware-i vra:

Erb Inqnahostë Aveli Lav ë Handiputyunyam

Karavarutyam service-ë amen dêpqum ë handipes:

Hazukayin model-i usovcum. Orosh dêpqer paxanjatcum en nor NER modelovner: Petakan dèrman anunnere kam enterkayan artyanayyinerin kodyavori nšanamovnere orinakner en: Inqnahostë ndzum ë dzez usovcman gorciknerë:

Spark-native mšakutyun. Orosh pipeline-ner paxanjatcum en PII haytnabеrutyunë Spark executor-i ënkerutyum: Artorin API-i kaçumë avarutyun ê avandutyam, vor caxatum ë ayd karkë: Inqnahostë miaydn handipes ë aystex:

Amboghj varavari. Orosh anvtangayin karkagirnerë pakhum en ambogj artorin API-i kaçumnerë tvalyalneri pipeline-ë meç: anonym.legal Desktop App-ë katarum ê ambogj offline: Inqnahostë ambogj menavoracvac ëntrutyunê:

Amenayin dêpqerum — pastatxti mšakutyun, API-i kargatsutyun ev chapat-i gorcikner — karavarutyam service-ë amboghji infra strukturayin dznaguirë hèçnum ê:

Erkù Urçerë Miasnabanam Gorcarkutyamb

Anvcara mashe dzez 200 credit ë amis: Ayd bavakan ë test-el isgakan pastatxtnere: Vercnabanutyan karti petk chunn: Patkamavorum chunn:

Aystex ë pavut paralelë morçutyunê:

Šabaт 1. Dev-um inqnahost analyzer-ë karcavel: Tesnen, tu barcr ë linel artadrutyunayin kazmaviçutyunë:

Or 1, paralelë. Karavarutyam service-i hozhap stexcel: Nauyneçan test pastatxtnerë andckacel karavarutyam API-ë: Hamematec arjunqnerë:

Karevori harcerê.

  • Karavarutyam service-ë haytnakutyam ê dzez paxanjatcvac tiperim? Ayn kaverutyum ë 285+ kazu tipes: Bac kodzayin šinuytë ëndlaynum ë šurj 40:
  • Ëntardzakutyunë bazmacan ë?
  • API-ë dzer karkê ë handipes?
  • Planner-nerë handipes en dzer ardyunatakutyan ev byudjei?

Yêtê hayê amen-i hamar. karavarutyam service-ë hèçnum ê infra strukturayin dznaguirë: Yêtë vočë. dzez gtnutyunnerë iskakan pricinner en mnal inqnahostavatvac:

Tesnec, inçpes aylë kapakicnerë ayd ëntrutyunë katarecin mer case studies-ë: Backaparahaytnec anvtangutyan ev pashtonapelutyam masnavorutyunnere mer anvtangutyan ev çapatutyan ejë: Petec pataskhannerë hayterë erkir mer FAQ-ë:

Hamarot Asac

Yereq-šabatanoc karcavorumë doceri kam karavi xotum chi: Ayn cuyç ê talum, tu inç ê paxanjatcum artadrutyunayin NLP infrastrukturan: Marzumnerë isgakan en: Nranq žam ev habardutyun en paxanjatcum mèzkacel hamar:

Šat kapakicneri hamar, PII de-identifikaciyë hamapatasxanakan paxanjatumë: Ayn caragrakan inženerarakan gorcaren chi: Karavarutyam service-ë aveli nacutyunë nuyneçan haytnabеrutyunov ë bacum: Ayn aranc infra strukturayin dznaguirë ë katarum: Tasnerkù rose ajanqic minçev de-identificelutyam araçin pastatxtê bavarakanacnum ë gornahavel arzhekë çap ucaç:

Šarunkutyan ašxarhagneri

Հոդվածներ, որոնք կապված են

Տեխնիկական

Cross-Platform PII: Mac, Linux, and Windows

Privacy officers on Mac, legal on Windows, data engineers on Linux — all processing the same data with different tools. Here's why OS-agnostic detection.

Տեխնիկական

Cross-Application PII: Word, Chrome, and AI

Customer data flows from browser research to Word drafts to Claude prompts. Each context switch is a potential leakage point.

Տեխնիկական

GDPR in App Logs: JSON PII Compliance

Application logs contain customer email addresses, IPs, and account numbers that GDPR Article 5(1)(e) requires be managed.

Պատրաստ եք պաշտպանելու ձեր տվյալները?

Սկսեք PII անանոնիմացնել 285+ կազմակերպության տեսակներով 48 լեզուներով:

Սկսեք անվճար փորձաշրջանԴիտել առանձնահատկությունները

About this page

We update this page when our platform or the law changes.

Read our founder note for how we work.

Each change shows up in the timestamp at the top.

Related reading

We follow these rules

  • GDPR (EU 2016/679).
  • ISO/IEC 27001:2022.
  • NIS2 (EU 2022/2555).
  • HIPAA safe harbor under 45 CFR § 164.514(b)(2).

Our promise

We do not sell your data.

We do not train models on your text.

We store your files in Germany.

You can delete your account at any time.

You own your work.

Where we run

Our servers live in Falkenstein, Germany.

We use Hetzner. They hold ISO 27001 certification.

All data stays in the EU.

Backups run every day.

Need help?

Email support@anonym.legal.

We reply within one business day.

How we test

We run a full check suite on every release.

Each surface gets its own sweep script and report.

Human reviewers spot-check the output each week.

We track recall and precision on a labelled set.

Bad runs block the deploy.

What we never do

  • We never sell your information to third parties.
  • We never train models on what you upload.
  • We never keep your work after you delete it.
  • We never share keys with any outside firm.
  • We never run ads inside the product.

Plans in plain words

We sell credits, not seats.

One credit covers one short job.

Long jobs use a few credits each.

You can top up at any time.

Unused credits roll over each month.

Read the plans page for current rates.

Who built this

A small team of engineers and lawyers built this.

We ship from Europe and work in the open.

Our founder note spells out why we started.

Where to start

How the parts fit

A browser add-on cleans text inside Chrome.

A Word plug-in handles drafts in Office.

A small desktop tool works on whole folders.

An agent protocol link feeds large models safely.

All four share one core engine and one rule set.

Words from our team

We started this work after a lunch about cookies.

One friend kept getting odd ads on her phone.

We asked why a court file leaked through a draft.

We sketched the first build on a napkin that week.

By month three we had a tiny demo for a friend.

She used it on her first case the next day.

Common questions we hear

Can the tool read scanned PDFs? Yes, with OCR.

Does it work on long files? Yes, in small chunks.

Can I roll my own rule set? Yes, save it as a preset.

Does it run offline? The desktop build runs offline.

Do you keep my files? No, the cloud build wipes after each run.

Will it learn from my work? No, we never train on inputs.

A short tour of the workflow

Upload a file or paste a snippet of prose.

Pick the entities you want gone from the draft.

Choose a method: replace, mask, hash, encrypt, or redact.

Press run and watch the side panel show each hit.

Skim the result and tweak any rule that misfired.

Save the cleaned file or send it to a teammate.