DOJ Second Request redaction is the removal of personal data from documents produced in a merger review. The HSR Act (15 U.S.C. §18a) authorises that second request for more data. anonym.plus strips names and contacts across the set on your device.
When this applies
A Second Request demands a huge volume of documents on a deadline. Employee and customer names recur across thousands of files and can be cleared where allowed.
How anonym.plus handles it
- Point anonym.plus at the production folder on your device.
- It scans each file for names, emails, and IDs.
- A shared label map keeps repeat people steady.
- Review the summary and fix low-confidence flags.
- Save the clean production set on your device.
What you need to provide
- A folder of documents (PDF, DOCX, or mixed).
- The shared label map turned on for steady results.
- An operator (Replace works well at volume).
PII entity types detected
| Category | anonym.plus entity type | Example |
|---|---|---|
| Names | PERSON | employee across files → [EMPLOYEE_1] |
| Names | PERSON | customer contact → [CUSTOMER_1] |
| Contact | EMAIL_ADDRESS | work email → [EMAIL] |
| Dates | DATE_TIME | sent 09 Feb → [DATE] |
| Identifiers | NATIONAL_ID | employee no. → [ID] |
| Financial | US_BANK_NUMBER | account no. → [ACCOUNT] |
Compliance achieved
- Supports production under the HSR Act (15 U.S.C. §18a) second request.
- Batch mode applies the same rules to every file.
- On-device AES-256-GCM guards the working files.
- A shared map keeps repeat people steady across the set.
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Limitations & cautions
The DOJ may require some names in the clear. Redact only what the protocol allows. Mixed folders lean on OCR for scans, so review low-confidence flags before you produce.
Frequently asked questions
Can the batch handle a large volume?
Yes. Point the tool at a folder, up to 20 files per batch, and run sequential batches for a large production locally.
How does the shared map help at volume?
It logs each person once, so the same employee or customer maps to one alias in every file across the set.
Are scanned documents supported?
Yes. Local OCR reads scanned pages, so IDs in image files are caught before the check.