anonym.legal

By · Last updated 2026-06-05

Späť na blogGDPR a Dodržiavanie

Japonske My Number: Verhoeff a APPI

63 % generickvch nastrojov zlyha pri detekcii My Number v japonskych dokumentoch. My Number pouziva Verhoeffov algoritmus - najzlozitejsi narodny kontrolny sucet ID v Azii.

June 5, 20268 min čítania
Japan PPCMy Number VerhoeffJapanese language NERAPPI complianceJapanese PII

Japonske My Number: APPI a Verhoeffova kontrola

Japonska Komisia na ochranu osobnych informacii (PPC) vydala v roku 2024 az 45 vykonnych rozhodnuti. Publikovala tiez prvu japonsku smerniu pre ochranu sukromia pri pouzivani AI. Studia PPC zistila, ze 63 % generickvch nastrojov NLP zlyha pri detekcii My Number (majna nba) v japonskych suboroch. Ak vas tim spracuva udaje japonskych rezidentov, tato medzera znamena priame riziko suladnosti s APPI.

Co je My Number

Japonsko prideluje kazdemu rezidentovi jedinecny 12-ciferny identifikator. Toto je My Number, sucast systemu individualnych cisiel (My Number system). Zahrnuje dane, dochodok, zdravotne poistenie a reakciu na katastrofy. Tento identifikator je citlive udaj podla APPI. Na jeho zber alebo zdielanie potrebujete pravny dovod.

Problem s Verhoeffovou kontrolou

My Number pouziva Verhoeffov algoritmus pre svoju kontrolnu cifru. Verhoeff je matematicka metoda, ktora zachytava vsetky chyby s jednou ciflicou. Zachytava tiez vsetky chyby, kde sa dve susedne cifry prehodia. Na fungovanie potrebuje tri vyhladavacie tabulky. Nie je mozne ho vypocitat rucne. Vyzaduje si kod.

To je dolezite z dvoch dovodov. Po prve, japonsky 12-ciferny format sa podobna mnohym inym kodom. Referencie faktury, ID dokumentov a retazce datumov zdielaju rovnaky format. Bez Verhoeffovej kontroly bude nastroj oznacovat nespravne hodnoty. Po druhe, vacsina nastrojov nepouziva Verhoeff. Pouzivaju jednoduchsie kontroly modulo-10 alebo modulo-11. Tie tu nefunguju.

Studia PPC zistila, ze 63 % nastrojov bud' preskakuje kontrolu, alebo pouziva jednoduchsiu metodu. Oba problemy sa vyskytuju sucasne: false positives aj false negatives.

Luhnov algoritmus, pouzivany pre kreditne karty, je jednoduchsi. My Number nepouziva Luhn. Nastroje vytvorene pre Luhn tu nefunguju.

Tri pisma, jedno meno

Japonsky text pouziva tri pisomne systemy sucasne. Nastroj musi zvladat vsetky tri.

Hiragana: Pouzivana pre gramatiku a rodne slova. 46 zakladnych znakov.

Katakana: Pouzivana pre cudzie slova a mena. 46 zakladnych znakov. Cudzie mena v Japonsku sa objavuju v tomto pisma.

Kanji: Symboly pre podstatne mena a mena. Priblizne 2 000 je bezne pouzivanych.

Meno jednej osoby moze mat styri formy: Kanji, Hiragana, Katakana a Romaji (Tanaka Taro). Nastroj musi zhodovat vsetky styri. Ak jedna chyba, chyba vacsina zaznamov tej osoby.

Dalsie japonske ID na detekciu

Ridicsky preukaz: 12 cislic. Prve dve cifry ukazuju prefekturu. Tokio je 10. Osaka je 62. To umoznuje nastroju skontrolovat, ci je hodnota platna pre dany region.

Pas: Dve pismena plus sedem cislic. Format ICAO. Japonsko pouziva specificke pairy pismen.

Preukaz zdravotneho poistenia: Symbol plus cislo. Format zavisi od poistovatela. Narodne zdravotne poistenie a Poistenie spravovane spolecnostami pouzivaju rozne formaty.

Karta pobytu: Pre cudzich rezidentov. Dve pismena, osem cislic, dve pismena. Kartu vydava Ministerstvo spravodlivosti.

Pravidlo anonymizacie podla APPI

APPI ma prisny standard anonymizovanych udajov nazyvany anonymizovane informacie. V jednom klucovom bode ide dalej ako GDPR. Anonymizacia musi byt overitelna tretimi stranami a technicky nevratna.

Na splnenie podmienok musi organizacia:

  1. Odstranit vsetky priame identifikatory vratatn My Number.
  2. Zvladnut vsetky kombinacie kvaziidentifikatorov.
  3. Pouzit k-anonymitu alebo podobnu metodu.
  4. Zverejnit vseobecny popis prijatych krokov.
  5. Nikdy sa nepokusit o re-identifikaciu udajov.

Smernica PPC pre AI z roku 2024 pridava specificke pravidlo. Ak trénujete AI na anonymizovanych udajoch, nesmete tento model pouzivat na re-identifikaciu ludi. Toto je priamy zakaz utokov na invertovanie modelu voci trenovacim sadám APPI.

Na splnenie standardov PPC potrebujete styri veci. Po prve, Verhoeffova validacia pre detekciu My Number. Po druhe, japonsky NER pomocou ja_core_news so spravnou tokenizaciou. Po tretie, zhodovanie mien napriec Kanji, Kana a Romaji. Po stvarte, kontroly kodov prefektur pre ridicske preukavy.

India pouziva Aadhaar, ktory tiez vyzaduje Verhoeffovu validaciu. Technicka smernica pre suladnost s DPDPA v Indii to podrobne pokryva. Pre detekciu identifikatorov vo viacerych krajinach pozrite Detekciu narodnych danovych ID EÚ v ramci GDPR.

Zdroje

Súvisiace články

GDPR a Dodržiavanie

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 a Dodržiavanie

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.

GDPR a Dodržiavanie

Czech Rodné Číslo: Gender Encoding and GDPR

Czech rodné číslo encodes gender via 50-offset month encoding — making it GDPR Article 9 special category data. 67% of Czech firms use German tools.

Pripravení chrániť vaše údaje?

Začnite anonymizovať PII s 285+ typmi entít v 48 jazykoch.

Začať bezplatnú skúšobnú verziuZobraziť funkcie

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.