Anonymize customer lists referenced in privacy policy disclosures for third-party review – CCPA/HIPAA-compliant de-identification per Cal. Civ. Code §1798.130

CCPA §1798.130 requires businesses to disclose the categories of personal information collected and the purposes for which it is used. When drafting or updating privacy policies, internal customer lists used to audit data practices may be shared with outside counsel. anonym.legal pseudonymizes these lists so attorneys can verify disclosure accuracy without accessing real consumer identities.

When this applies

Apply this workflow when sharing customer data inventories or sample consumer records with outside privacy counsel to verify that privacy policy disclosures accurately reflect actual data-collection and data-sharing practices.

  1. Upload the customer data inventory or sample consumer dataset to anonym.legal.
  2. The engine identifies direct consumer identifiers across all fields: name, email, postal address, device ID, IP address, and account number.
  3. Each consumer record is pseudonymized consistently across the dataset, preserving the statistical structure of the data inventory.
  4. Data-category labels, collection source fields, and third-party sharing destination records are retained as structural content for disclosure-accuracy review.
  5. A reversible mapping key is encrypted and stored with US data residency.
  6. The pseudonymized inventory is exported for outside-counsel review against the draft or live privacy policy text.
  7. Counsel can audit whether the disclosed data categories and sharing purposes match the actual data structure without accessing real consumer personal information.

What you provide

  • Customer data inventory or sample dataset in CSV, XLSX, or structured format
  • Draft or live privacy policy text for comparison
  • Third-party data-sharing agreement list identifying recipient categories

Limitations & cautions

  • anonym.legal does not assess whether the privacy policy's disclosures are legally sufficient under CCPA §1798.130; that determination requires attorney review.
  • Sample datasets may not represent the full population of data collected; counsel should receive clear documentation of sampling methodology.
  • Data-category labels in the inventory must be consistent with CCPA definitional categories under §1798.140 for disclosure-accuracy analysis to be reliable.
  • This workflow covers CCPA/CPRA disclosure obligations; other privacy-notice requirements (e.g., COPPA §312.4, FTC §5) are addressed in separate workflows.

FAQ

Does this workflow help with the annual privacy policy update required under CCPA?

Yes. Annual updates to the privacy policy should reflect actual data practices. Pseudonymizing the supporting customer data inventory before outside-counsel review enables attorneys to validate disclosure accuracy across the year's data-collection activity without personal-data exposure.

Can the workflow handle datasets where the same consumer appears in multiple product lines?

Yes. The consistent-pseudonym assignment ensures the same consumer carries the same pseudonym across all product-line records in the dataset, allowing counsel to see the full breadth of data collected per consumer-type without revealing real identities.

Is this workflow relevant for businesses that operate solely online?

Yes. Online businesses often hold device identifiers, IP addresses, and behavioral data rather than traditional address-based personal information. The engine identifies and pseudonymizes these digital identifiers, which are personal information under CCPA §1798.140.

Consumer Privacy

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.

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.