By · Last updated 2026-06-05

Rudi kwa BlogGDPR & Ufuatiliaji

LGPD Brazil: CPF, CNPJ, na Ulinzi wa Data

LGPD inashughulikia Wabrazili milioni 215 na ANPD ilianza utekelezaji mkubwa mwaka 2024. CPF imegunduliwa kwa usahihi wa asilimia 45 tu na zana zilizofunzwa kwa Kiingereza.

June 5, 20268 dakika kusoma
Brazil LGPDCPF detectionBrazilian Portuguese PIIANPD complianceSouth America data protection

LGPD Brazil: CPF, CNPJ, na Ulinzi wa Data

Sheria ya Ulinzi wa Data ya Jumla (LGPD) ya Brazil inashughulikia watu milioni 215. Ni sheria ya tatu kwa ukubwa zaidi ya ulinzi wa data duniani kwa idadi ya watu. Inashughulikia watu zaidi kuliko Ujerumani, Ufaransa, na UK kwa pamoja. Mamlaka ya Taifa ya Ulinzi wa Data (ANPD) ilitoa faini zake za kwanza kubwa mwaka 2024. Kipindi cha neema baada ya kutumiwa kwa LGPD mwaka 2020 kumekwisha.

Kuna pia changamoto ya kiufundi. Nyaraka za LGPD ziko kwa Kireno cha Brazil. Vitambulisho vya taifa Brazil vinatofautiana na vya Ureno. Pia vinatofautiana na vitambulisho vya nchi nyingine yoyote.

Kwa Nini PII ya Brazil Ni Tofauti

Mifumo ya vitambulisho ya shirikisho na majimbo ya Brazil ilikua mbali na mifumo ya utambulisho wa kidijitali ya Ulaya. Hii iliunda seti ya kipekee ya vitambulisho. Zana nyingi za NLP zimefunzwa kwenye data ya Kiingereza au Ulaya. Zinashindwa kugundu vitambulisho vya ndani.

CPF (Cadastro de Pessoas Fisicas): Nambari ya mlipa kodi ya tarakimu 11. Muundo: XXX.XXX.XXX-XX. Ina tarakimu mbili za ukaguzi. Fomula inatumia hatua mbili tofauti za hesabu. Zote mbili lazima zioanishe ili CPF iwe halali.

Pengo la utambuzi ni kubwa. Zana za NLP zilizofunzwa kwa Kiingereza zinagundu CPF kwa usahihi wa asilimia 45 tu (ANPD, 2024). Sababu mbili zinaeleza hili. Kwanza, zana zinazooanisha nambari za tarakimu 11 bila mantiki ya tarakimu mbili za ukaguzi zinachanganya nambari sahihi za CPF na mifuatano ya nasibu. Pili, CPF wakati mwingine haina muundo wa XXX.XXX.XXX-XX. Hii hutokea katika matokeo ya OCR na fomu za maandishi wazi.

CNPJ (Cadastro Nacional da Pessoa Juridica): Nambari ya kitambulisho cha kampuni ya tarakimu 14. Muundo: XX.XXX.XXX/XXXX-XX. Pia ina tarakimu mbili za ukaguzi. Fomula inafanana na CPF lakini si ile ile.

RG (Registro Geral): Kadi ya utambulisho wa kiraia ya jimbo. Muundo unatofautiana kwa jimbo. Sao Paulo inatumia herufi 2 na tarakimu 5-9. Rio de Janeiro inatumia tarakimu 7-8 zenye kistari. Minas Gerais inatumia tarakimu 7-9. Majimbo mengine yana muundo wake. Zana inayojua muundo wa jimbo moja tu itakosa nambari nyingi za RG.

CNH (Carteira Nacional de Habilitacao): Nambari ya leseni ya udereva ya tarakimu 11. Ina tarakimu moja ya ukaguzi. Muundo unajumuisha nambari ya wilaya.

Titulo de Eleitor: Nambari ya kitambulisho cha mpiga kura ya tarakimu 12. Ina sehemu tatu: nambari ya kitambulisho ya tarakimu 8, nambari ya jimbo ya tarakimu 2, na tarakimu 2 za ukaguzi.

Nambari ya SUS (Cartao SUS): Kitambulisho cha afya ya umma cha tarakimu 15. Kila mtu nchini anapata kimoja. Inaonekana katika rekodi zote za hospitali na kliniki.

PIS/PASEP: Nambari ya programu ya kijamii ya tarakimu 11. Inaonekana katika kila rekodi ya ajira.

Kiwango cha Usimbaji Fiche cha LGPD

LGPD Kifungu cha 12 kinafafanua data isiyojulikana. Kiwango: data ambayo "haiwezi kutambuliwa, ukizingatia njia za kiufundi zinazofaa wakati wa usindikaji." Hii ni kiwango kinachohusiana na teknolojia. Data ya leo isiyojulikana inaweza kubaki hivyo na mbinu za utambuzi upya zikiwa bora zaidi.

ANPD inaongeza mwongozo zaidi. Kuondoa vitambulisho vya moja kwa moja kama CPF na jina haitoshi. Makundi ya vitambulisho vya karibu yanaweza bado kuruhusu utambuzi upya. Safu ya umri, mji, jinsia, na kazi pamoja zinaweza kutambua mtu. Hizi lazima zishughulikiwe kwa kuweka vikundi au kuongeza kelele.

Kwa data ya mafunzo ya AI, ANPD inahitaji moja ya masharti matatu. Kwanza: data inakidhi kiwango cha Kifungu cha 12. Pili: kila mhusika wa data alitoa idhini wazi kwa matumizi maalum ya mafunzo. Tatu: kuna kusudi halali lililorekodiwa.

Mahitaji ya Lugha ya Kireno

Kireno cha Brazil kinatofautiana na Kireno cha Ulaya. Maneno, tahajia, na muundo wa hati si sawa. Mifano ya NLP iliyofunzwa kwenye maandishi ya Ureno inafikia karibu asilimia 71 ya usahihi wa mifano iliyofunzwa kwenye maandishi ya ndani. Hii inatoka kwa tathmini ya kiufundi ya ANPD.

Tofauti kuu kwa utambuzi wa PII:

  • Majina: Matumizi ya majina mawili ya ukoo na mpangilio wa majina vinatofautiana na Ureno.
  • Anwani: Nambari za CEP zinatumia muundo XXXXX-XXX. Muundo huu ni wa kipekee kwa nchi hii. Unahitaji mantiki yake ya utambuzi mwenyewe.
  • Maneno ya hati: "Carteira de Identidade" hapa dhidi ya "Bilhete de Identidade" Ureno. Majina ya wakala pia yanatofautiana.

Uzingatiaji wa ANPD Unahitaji Nini

Mahitaji manne ya kiufundi yanashughulikia uzingatiaji wa ANPD. Utambuzi wa CPF na CNPJ lazima ujumuishe uthibitishaji wa tarakimu mbili za ukaguzi. Utambuzi wa RG lazima ushughulikie majimbo yote. Utambuzi wa nambari ya SUS na Titulo de Eleitor pia unahitajika. Mifano ya NLP lazima ifunzwe kwenye Kireno cha ndani.

Tazama mwongozo wetu wa utambuzi wa kitambulisho cha PII cha kimataifa na vitendo vya utekelezaji wa LGPD mwaka 2024.

Vyanzo

Tayari kulinda data yako?

Anza kuanonymisha PII na aina 285+ za vitu katika lugha 48.

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.