This task removes who said what from a voluntary monitoring survey. DPA 2018 Sch. 1 Part 2 para 8 sets a specific condition for processing special category data, such as ethnicity or religion. It applies where the survey supports equality of opportunity, and the employer keeps an appropriate policy document. That data can then support the positive action steps in Equality Act 2010 ss.158-159. anonym.plus marks names and direct fields on your device. The survey still guides analytics, but names no one.
When this applies
A voluntary survey may still tie an answer to a name or an email. That goes beyond what the equality monitoring condition in Sch. 1 para 8 actually needs. You strip the direct field so replies stay grouped only. That matches what the condition is meant to permit.
How anonym.plus handles it
- Open the survey data in anonym.plus on your device.
- The app scans each response for direct identifiers.
- It marks names, emails, and staff references.
- Confirm the markings and keep the demographic fields.
- Turn the alias map OFF for irreversible removal.
- Save the cleaned dataset locally.
What you need to provide
- The survey data (CSV, XLSX, or PDF).
- An operator (Redact for true removal).
- The alias map turned OFF for true anonymity.
PII entity types detected
| Category | anonym.plus entity type | Example |
|---|---|---|
| Names | PERSON | respondent name → [RESPONDENT] |
| Origin | NRP | self-ID field → [DEMOGRAPHIC] |
| Contact | EMAIL_ADDRESS | work email → [EMAIL] |
| Identifiers | UK_NINO | staff NINO → [NINO] |
| Location | LOCATION | office site → [LOCATION] |
| Dates | DATE_TIME | submit date → [DATE] |
Compliance achieved
- Uses the equality monitoring condition in DPA 2018 Sch. 1 Part 2 para 8 for special category survey data.
- Supports the positive action route in Equality Act 2010 ss.158-159, without keeping data beyond what monitoring needs.
- Preserves demographic fields for aggregate analytics.
- Offline work keeps respondent identity off any server.
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Limitations & cautions
A small office plus a rare characteristic can re-identify one respondent, even from grouped data. Aggregate or suppress tiny cells before you publish a chart. That way, no person is singled out under the motivated-intruder test.
Frequently asked questions
What lets an employer collect ethnicity or religion data for monitoring?
DPA 2018 Sch. 1 Part 2 para 8 sets a specific condition for equality of opportunity monitoring. The organisation must also keep an appropriate policy document that sets out how the data is used and protected.
How does this connect to positive action?
Monitoring data gathered under that condition can support the positive action measures in Equality Act 2010 ss.158-159. That might mean encouraging applications from a group that is under represented, without ever needing to name a respondent.
How do I avoid re-identifying small groups?
Bucket or suppress cells with few respondents. The app removes direct references; you control the aggregation that keeps a small team anonymous.