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PII ya Kiarabu na Kiebrania: Zana za Magharibi Zinashindwa

GDPR haikomi kwenye Bosphorus. PII ya Kiarabu na Kiebrania katika mtiririko wa kazi wa biashara za EU haijahifadhiwa ipasavyo. Ugunduzi wa lugha nyingi wa XLM-RoBERTa na.

April 1, 20268 dakika kusoma
Arabic PII detectionHebrew NERRTL text processingMENA GDPR complianceXLM-RoBERTa multilingual

Pengo la Uzingatiaji la Maandishi ya RTL

GDPR haikomi kwenye Bosphorus. Makampuni ya EU yanayotumia zana za maandishi ya Kilatini yana pengo kubwa. Hili ni la kweli na kwa kiasi kikubwa linapuuzwa.

Tatizo si tu mwelekeo wa maandishi. Hati zinazoandikwa kulia kwenda kushoto (RTL) zinahitaji uwekaji alama tofauti. Zinahitaji usegmentaji tofauti. Mipaka ya vitengo inafanya kazi tofauti kuliko maandishi ya LTR. Mifumo ya NER iliyofunzwa kwa Kiingereza inatumia kanuni za LTR. Kanuni hizo zinashindwa kwenye maandishi ya RTL. Zinatoa mipaka mibaya ya vitengo.

Morfologia ya Kiarabu inafanya mambo kuwa magumu zaidi. Lugha inatumia mizizi. Mzizi mmoja unazalisha maumbo ya maneno mengi. Jina kama Mohammed linaweza kuonekana kama "Al-Mohammed," "bin Mohammed," au "Mohammed al-Rashid." Mifumo ya regex iliyoundwa kwa majina ya Magharibi inakosa maumbo haya. Mifano iliyofunzwa kwa Kiingereza pia inakosa.

GDPR haizingatie lugha kama mpaka wa uzingatiaji. Kampuni ya EU inayoshughulikia barua za wateja kutoka MENA lazima izingatie kanuni sawa na barua za Kifaransa. Kukosa PII katika maandishi ya RTL ni kushindwa kisheria chini ya GDPR Kifungu cha 32.

Kesi ya Matumizi ya KYC

Fintech ya Dubai inayoshughulikia nyaraka za KYC kwa wateja wa EU inaonyesha hili wazi.

Faili za KYC za wateja wa Kiarabu zina majina katika maandishi ya RTL, nambari za Emirates ID za UAE, na anwani za RTL. Hizi zinakaa pamoja na maandishi ya kibiashara ya Kiingereza.

Muundo wa Emirates ID ni 784-XXXX-XXXXXXX-X. Msimbo wa nchi 784. Mwaka wa kuzaliwa. Tarakimu saba. Tarakimu ya ukaguzi. Zana za PII za Magharibi ambazo hazina ufafanuzi wa vitengo vya UAE haziwezi kupata muundo huu. Sehemu za majina hupitia NER ya maandishi ya Kilatini. Usegmentaji ni mbaya. PII inakuwa haionekani katika mtiririko wa kazi.

Kwa makampuni yenye wajibu wa GDPR juu ya data hii, pengo hili linaunda hatari ya kisheria halisi. GDPR Kifungu cha 32 kinahitaji hatua sahihi za kiufundi. Zana inayokosa vitambulisho katika 22% ya lugha za ulimwengu si hatua sahihi.

Kiebrania na Hati za Lugha Nyingi

Kiebrania pia ina matatizo sawa. Maandishi yanakimbia kulia kwenda kushoto. Nambari za vitambulisho vya Israeli zinatumia jumla ya ukaguzi -- mtihani unaofanana na Luhn kwenye tarakimu tisa.

Nyaraka za kisheria za Israeli mara nyingi zinachanganya Kiebrania, maandishi ya Kiarabu, na Kiingereza katika faili moja. Hii ni kawaida katika mikataba ambapo Kiebrania ni lugha kuu na maneno ya Kiingereza yanaongezwa kwa rejea.

Faili za maandishi mchanganyiko zinahitaji ugunduzi wa maandishi kabla ya NER. Bila hilo, NER moja inatumia kanuni za Kilatini kwenye maandishi ya RTL. Matokeo ni mabaya.

Utafiti katika Nature Scientific Reports (2025) ulijaribu NER ya lugha nyingi kwenye PII ya RTL. Mifano ya kawaida ilipata alama ya F1 ya 0.60-0.83. XLM-RoBERTa iliyofunzwa kwa data ya NER ya RTL ilipata 0.88 na zaidi.

Mahitaji ya Usanifu wa Lugha Nyingi

Ugunduzi mzuri wa PII ya RTL unahitaji mambo matatu ambayo zana za Magharibi kwa kawaida hazijakuwa nazo.

Ushughulikiaji wa maandishi ya RTL: Uzingatiaji wa Unicode bidirectional kwa mtiririko sahihi wa maandishi. Uwekaji alama unaozingatia RTL unaopata mipaka ya maneno katika maandishi ya kulia kwenda kushoto.

NER inayozingatia morfologia: Kichunguzi cha kimorfologia kama Farasa kwa Kiarabu, au mfano wa transformer uliopewa mafunzo kwa data ya NER ya RTL. Mfano lazima ujifunze tofauti za kimorfologia.

Aina za vitengo za kanda maalum: Emirates ID, Israeli ID, Saudi National ID, na Egyptian National ID kila moja inahitaji ufafanuzi wazi na kanuni za muundo. Zana za Magharibi za jumla hazina hizi.

Angalia jinsi mfumo wetu wa NER wa lugha nyingi unavyoshughulikia ugunduzi wa maandishi katika lugha 48. Kwa orodha kamili ya aina za vitambulisho vya MENA tunazounga mkono, tembelea katalogi ya vitengo. Mwongozo wetu wa uzingatiaji wa GDPR unashughulikia jinsi mapungufu ya ugunduzi yanavyounda hatari ya Kifungu cha 32.

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.
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  • We never keep your work after you delete it.
  • We never share keys with any outside firm.
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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.