Anonymize CCPA opt-out-of-sale and sharing records for third-party disclosure review – CCPA/HIPAA-compliant de-identification per Cal. Civ. Code §1798.120

CCPA §1798.120 gives California consumers the right to opt out of the sale or sharing of their personal information with third parties. Opt-out records contain the consumer's identity and specific data-sharing preferences. anonym.legal pseudonymizes these records so businesses can audit opt-out processing and share compliance evidence without exposing consumer identities.

When this applies

Use this workflow when opt-out preference records must be shared with third-party data recipients, ad-tech vendors, or outside counsel to demonstrate that consumer opt-outs have been honored, or when auditing opt-out propagation across a data supply chain.

  1. Upload opt-out records, preference-signal logs, or Global Privacy Control (GPC) receipt logs to anonym.legal.
  2. The engine identifies the consumer identifier in each record: device ID, email, IP address, account number, or cookie ID.
  3. Each unique consumer identifier is replaced with a consistent pseudonym across all opt-out events and downstream propagation records.
  4. The opt-out preference flag, third-party recipient identifiers, and propagation timestamps are retained as structural compliance content.
  5. Batch processing can cover millions of GPC or opt-out signals while preserving per-consumer consistency through the pseudonym mapping.
  6. The reversible mapping key is encrypted and stored with US data residency.
  7. Pseudonymized records are exported for vendor notification verification, audit, or outside-counsel review.

What you provide

  • Opt-out preference records or Global Privacy Control signal logs in CSV, JSON, or structured export format
  • Third-party recipient identifiers to be retained for downstream propagation verification
  • Date range for the opt-out records to be processed

Limitations & cautions

  • anonym.legal does not verify that opt-outs were propagated to all downstream third parties; that requires a separate vendor-notification audit.
  • The tool does not assess whether opt-out signals received via GPC were processed within the required timeframe; deadline compliance must be tracked separately.
  • Ad-tech identifiers that are probabilistically linked across devices may carry residual re-identification risk even after pseudonymization.
  • This workflow covers CCPA/CPRA opt-out rights; analogous opt-out rights under other state laws are out of scope.

FAQ

Does this workflow cover both the 'Do Not Sell' right (pre-CPRA) and the 'Do Not Share' right added by CPRA?

Yes. §1798.120 as amended by CPRA covers both the right to opt out of sale and the right to opt out of sharing for cross-context behavioral advertising. The workflow processes opt-out records for both rights and retains the right-category flag as a structural field.

How does the tool handle Global Privacy Control signals at scale?

GPC signal logs are typically large structured files keyed on device or user identifiers. The batch-processing pipeline pseudonymizes each unique identifier while preserving the signal timestamp and originating domain, enabling compliance analytics at scale without individual consumer exposure.

Can pseudonymized opt-out logs be shared with advertising partners to confirm opt-out implementation?

Yes. Sharing pseudonymized logs with partners allows verification that the partner has applied the opt-out flag without disclosing the real consumer identifiers. The partner can confirm the flag status against their own pseudonymous records using the shared pseudonym.

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.