TAR-set anonymisation is the removal of personal data from documents fed to technology-assisted ranking, an approach English courts have accepted as consistent with the proportionate-search duty in CPR 31.7 since Pyrrho Investments Ltd v MWB Property Ltd [2016] EWHC 256 (Ch). anonym.plus runs locally, so PII does not enter the training data before that proportionality question is even settled.
When this applies
The model ranks documents by relevance to speed triage, an approach Pyrrho Investments approved as a reasonable and proportionate way to conduct a search under what is now CPR 31.7. Feeding raw PII into it spreads exposure across the whole index, so clearing it first keeps that risk low regardless of how the ranking model performs.
How anonym.plus handles it
- Point anonym.plus at the document set on your device.
- Local OCR reads any scanned items in the set.
- The tool flags names, contacts, and IDs across files.
- Use steady labels so relevance signals survive.
- Replace or mask each confirmed value.
- Save the clean set for the ranking workflow.
What you need to provide
- The document set (mixed PDF, DOCX, email, or scan).
- Replace with steady labels to preserve text signals.
- Optional batch run of up to 20 files at a time.
PII entity types detected
| Category | anonym.plus entity type | Example |
|---|---|---|
| Names | PERSON | custodian name → [PERSON_n] |
| Contact | EMAIL_ADDRESS | sender email → [EMAIL] |
| Dates | DATE_TIME | doc date → [DATE] |
| Identifiers | UK_NINO | NINO → [NINO] |
| Location | LOCATION | address → [ADDRESS] |
| Account | UK_SORT_CODE | sort code → [SORT_CODE] |
Compliance achieved
- Reflects the proportionate-search approach to disclosure in CPR 31.7.
- Technology-assisted review of this kind was accepted in Pyrrho Investments Ltd v MWB Property Ltd [2016] EWHC 256 (Ch).
- Steady labels keep relevance signals for the model.
- Offline work keeps the training data inside your firm.
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Limitations & cautions
Anonymising before machine ranking can shift how a model reads context. Steady labels keep most signals, but test recall on a control set first. Free-text clues that survive redaction still need a human pass on relevant items.
Frequently asked questions
Do English courts accept technology-assisted review?
Yes. Pyrrho Investments Ltd v MWB Property Ltd [2016] EWHC 256 (Ch) was the first English case to approve predictive coding as a reasonable and proportionate search method, and later cases have followed that approach under CPR 31.7.
Does anonymising first hurt accuracy?
It can shift context, but steady labels keep most text signals. Test recall on a control set before relying on the ranking, whatever proportionality argument you plan to make under CPR 31.7.
Why clear PII before the model trains?
Raw PII in training data spreads across the whole index at once. Clearing it first keeps that exposure low, independent of whether the court later accepts the ranking approach as proportionate.