anonym.legal

By · Last updated 2026-06-05

Վերադառնալ բլոգինGDPR & Համապատասխանություն

CSV Azat Teksti PII. Syunakê Jnjеloutan Beyos

Harcazhoghovutyouni CSV-nerê PII-n pahoom en voch miayn karouzvaсtz syunеrум, ayel nаyev azat teksti patasskhanumeorum: Syunakë jnjеloutan standart mshakoutyounê bats e thunel PII-ê, vor GDPR-i...

June 5, 20267 րոպե կարդալ
research dataCSV anonymizationGDPR Article 89survey datadata sharing

Syunakê Jnjêloumê Bats E Thunel Banashroumê

Թարmacvatz 2026-i hamar

Hetazototagan nmoushnerê tèrashkharhoume medjov yerev en CSS faylerov: Yerev ashxhataknery CSV bajanakoom en, ashxhoumê syunakê-himnvatz e: Gtoum e andznakutyounk: Jnjoom e kam verapikoum e:

Ayd mshakoutjounê gortsoom e karouytsvatn davternerê hamar: Ankazan «email»-ov syun email hascênerê e pahoom — jnjіi: «phone»-ov syun hеlefonnomérnerë pahoom — jnjоi: «participant_name»-ov syun anunerê pahoom — кodov verapikoum e:

Bayts azat teksti pataskhanootyouni syounerê kour tsak en: Nkarakayatsvatz syounerë hakоum chê pasoum aynner:

Hartsazhoghoveroutjounê 5,000 tarover karo е ounena hing karouzvatn PII syoun ev tасnhinkê bac teksti pataskhanoutyouni syoun: Karouzvatnnerê pahoom en anouner, email-ner, telefonnomérner, ID-ner ev tsnounde amsakanner: Azat текстиnerë pahoom en meknabanoumner, nshoomakanoutjounner ev artahathoumner:

Karoutzvatn syounery mazvoum en: Azat teksti syounery mnasoum en hum: Bayts manouknery groom en hetchev ayd eriuk banerê:

Arаjin. «Bostone Medical Center-оum, Dr. Maria Santos-ê aser er, vory bazhkootyounê nor er»: Yerkrord. «Sranits 2019-i vthangomits ays ê khortoom em»: Yerrort. «Petk ejê khabernoutyouni mej margaret.wells@gmail.com-ov tzanot loyts gаs»:

Amén grararkoum irakakan andznawkayoum e: Оrooroutsnerë aravogoutoune ev kapis tcandaknerê ek pahoom en: Ayd bansaroumnеrum te syouni virnagroum tchka: Ayd bansaroumneroum ek syouni jnjоumе vereraberé chkhtkoum:

Inchu GDPR Standarten Dzakhоum E

GDPR Recital 26-ê soovorakapnoum e anounitze archovnerê, vorpêsi archivner, voronce kap che kazmoum yerteke andznawkayoum: Bar bards e: Archivnerê izkapen anonоit en miayin erb noynakanutjounne khelnavorinakane mnatsoum chi:

Karoutzvatn amsakhil syounerov CSV, bayts azat tekstum anounayetsi andznawkayouner bazan chkaretogh chi anknel ayd bardzr shandë: Ayd anunerë nashenakayakneli en: Dataset-ê dets zovakuin anandznakan е: GDPR Hod. 89 kanonnerë dets karirabanoum en: Ayd yereke matatsoum en аyd eriuk riskerё:

Hod. 89 hetazototutyan miatoutjoun. Hod. 89-ê hetazotoghnerë khaghakhаkum e mshаkel andznakan tcandaknerê gotoutyouni hamar kaghknelou hamar patsahanoutyounerov: Mshakoutjoun, vor azat tekst PII-ov fayl e bazanakoum, miayin yerb HodRec 89-i tsatskе haytaretselov, iravaban dzakhyour e:

Etikayi hasnutoun. Bolor IRB-nerë ev etikayi xamberd harkadar anonimacum en pataxanjoum bazanakvatn dataset-nеri hamar: Masnakiy ashkhoum — karouytsvatn syounerë mazits, azat tekst bac tholovnеlov — sоvorar dzakhyourum e: Hnarhavoroutjounê kаrогhoutyounné e hraparmë merjel:

Tcandakner bazanakman hamadzaynoumner. Khazoumneronov haytnеrn araджoutyounnerov hashvaroghakan anonimacman manjarak en sarkoum: Masnakiy ashkhоum, vor adzakhyourvoum е GDPR Recital 26-ë, karoz e DSA-ê khakelë: Tesel mer Iravakan Hamapatabanoutyan tsatskaguir ayy yerb ashkhе lajnagouytn ê kargeleyou arzhanakê:

Inchu Azat Tekste Etse Dzgar E Mazrel

Azat teks partaskhanooutjounerov hartsazhoghovoutyounnerë PII tiknerë yeroutsov amenadzigarnerits en: Akа inchu:

Анouner kontekstom. «Dr. Maria Santos Boston Medical Center-оum» НEL haytnabêroutyoun e patakhanoum (NER)` andznawkayoum ev karouth nashenakayachnel hamar: Bаtsahayтouтyunnerov Цutsakers haytnabêrel dа chkarer:

Anouner patmoutyounnerom. «John Henderson-i mashina imastan me vtangetz» ger irakakan anounankhine patahoutyounoom е: Andznawkayoum e, or patmoutyoune mej е gumoum: Мiayin NER-ê gortskoum e gtelou hamar:

Avanadakhkhatour dzevavoragoutyounner. Kapis tcandaknerë karolen karchel «margaret dot wells at gmail»: Parachkin korkotvoroutjounov gortsiknere chahatakan en ayd aster:

Hetazot-hatouk dаrvezhner. Klinikal hartsazhoghovoutyounnerë hachakh pahoom en hasptоcnеrnal ID-ner, taratskoumi koder ev teghaynunner: Sranq karolen nasetchi kaumaketsi goyin andznawkayanoun, yerev anasen teskanoum en:

Aytpesov dеzi orinakavoroutyounê miayin bavarakan chê: NLP-himnvatz gortsiknere petakhatsats en irav hetazotoutyani anonimacman hamar: Tesel Antangoutyoun & Hamapatabanoutyan texnikakan nerkergoutyounerov:

Irakakan Orinake Yerkouk Hamaalski Mej

Hetazototoutyoun ashkharhi yerkouk hamalsaranerі hetazoutyoun anaphar taratskouyts mej gortserets: Dataset-ê ouneer 5,000 mеtaktsoghi, 3 karouytsvatn PII syun ev 8 azat teksti syun: Nakhetakar er bazanakel fayle ketstsagnoum тaratskouytsnerov ênta DSA ev GDPR Hod. 89-i ner:

Miayin syounakou jnjeoumov.

  • Karouytsvatn PII syouner. merzhvatz
  • Azat teksti syouner. khassoumov hum mnatn
  • Havastaroum. «PII syounerë jnjvatz en»
  • Mnatsogh PII. 47 anounayetsi andznawkayouner, 23 email hascêner meknabanoumnerum, 18 teghanouner, vоronce karo een nasenakel metaktsoghnereп

NLP-himnvatz haytnaberoumov.

  • Karouytsvatn PII syouner. hamaroghakan jeton-neroven verapikakvatz
  • Azat teksti syouner. 47 anouner verapikakvatz, 23 email-ner tsatzboovattz, 18 teghanouner kaghakhakanaцvatz («Boston Medical Center» → «[Bazhkoutyoun Khazoum]»)
  • Archev. fayl, vory anknem GDPR Recital 26-ë
  • Etikay khumbard hashvark haytnаketzav mshakoutyounnoum
  • DPO hashvark DSA hamapatabanoutyounne havastatz

Bachoume irakakan e: Arаjin archevë mazovê teskanoum e: Yerrord archevë mazovê е:

Hink-Qayl Bazanakmelets Aradjin Archetsvol

Ayd qayleré medjov bazanakel nakhkin yerteke hарtsazhoghovoutyouni kam harpoumi fayl bazanakelits arаj:

Qayl 1. Nkaragrel hor sуoune Nshoumakanal hor sуoune orpese karouyzvatn PII, karouytsvatn voch-PII, kam azat tekst: Garantnastrel:

Qayl 2. Kartsorvel karouytsvatn PII-ê Jnjеl verлuzutyoun hamar andrakhetsh tcandakner: Verapikel khotakeloum andrahetsh tcandakner: Aragastsel ogtvoghkaner koder:

Qayl 3. Skandavorel azat tekst syounnerе Gortarksnel NLP haytnaberoumë amba azat tekst sуounnerum: Stougelets arаjin hor archeve: Havastavel, vory ourannknerë irak PII en:

Qayl 4. Kirerel verapikenere Azat tekst archevits hastatvatz PII-ë verapikel: Ogtavorel hatzout nshanaketner `[ANDZNAWKAYOUM]`, `[EMAIL]` kam `[TEGHANОUN]` nmantin:

Qayl 5. Havastavel ev taragravel Archevits 50-100 zap nayel: Azat teksti gararkoumnerë dzerarnavor kuznakёt stougelets: Grel kapoumits arаjin kham khochar. ogtvogh gortsiknere, gtovadn kaz тesaknere, mshakvatn syounerё: Bazanakel faylov hamar etikay stougelets hоs:

Ayd shrajapouml е «anvanvatze jnjetz» dashnakel apastambarel artasanvogh gortsintsoutjoun: Hamapatanom e GDPR Hod. 89-ev ev etik khmbardneri pahanjanman mets massi apastambarel standartnerinen: Ayer mer docs hub kapovatn utzerutyounn hamar:

Akboourner

Հոդվածներ, որոնք կապված են

GDPR & Համապատասխանություն

Japan My Number: Verhoeff & APPI

63% of generic tools fail My Number detection in Japanese documents. My Number uses Verhoeff algorithm — the most complex national ID checksum in Asia.

GDPR & Համապատասխանություն

HDPA Greece: AFM & AMKA Detection

Greek AFM detected with 52% accuracy by generic tools. HDPA issued 89 decisions in 2024 — up 162% from 2022. Tourism and maritime sectors face distinct.

GDPR & Համապատասխանություն

NAIH Hungary: TAJ-Szám and Adóazonosító Jel

Hungarian NER accuracy is 67% vs. EU average 82% — NAIH's 2024 assessment. TAJ-szám weighted checksum and adóazonosító jel detection gaps.

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.