Kenya DPA Identifier Checklist¶
This checklist maps the sensitive-personal-data classes in Kenya's Data Protection Act No. 24 of 2019 to the canonical labels and actions in OpenMed's ke_dpa policy profile. The profile is deliberately conservative: it masks direct identifiers, county-level and finer location data, sensitive attributes, and health-related concepts; retains no reversible mapping; uses high-recall arbitration; and always runs the deterministic safety sweep.
This is decision-support guidance, not legal advice, compliance certification, or a substitute for a controller's lawful-basis, consent, transfer, security, and professional-confidentiality review. De-identification lowers disclosure risk but cannot by itself establish compliance or guarantee that a person is no longer identifiable.
Statutory posture¶
The source of record is the Data Protection Act, 2019 published by Kenya Law.
- Section 2 defines personal identifiers, health data, biometric data, sensitive personal data, anonymisation, and pseudonymisation. Its sensitive classes drive the table below.
- Section 44 restricts processing of sensitive personal data, while section 45 lists permitted grounds. Selecting
ke_dpadoes not determine whether a ground applies. - Section 46 limits health-data processing to healthcare-provider responsibility, professional secrecy, public-health necessity, or another legal duty of confidentiality. The profile therefore masks clinical concepts as well as identifiers.
- Section 48 sets conditions for transfers outside Kenya, and section 49 requires consent and appropriate safeguards before sensitive personal data is processed outside Kenya. Section 49 also permits the Data Commissioner to prohibit, suspend, or condition a transfer.
- Section 50 permits specified processing to be required on a server or in a data centre located in Kenya.
Section 2 sensitive-data action map¶
OTHER is the existing conservative catch-all for sensitive attributes that do not yet have a dedicated canonical detector label. Under ke_dpa, it is masked, so those attributes cannot fall through to keep. Biometric template or reference identifiers, Huduma Namba, national identity numbers, and health-linked member identifiers use ID_NUM; raw biometric media requires a modality-specific workflow outside this text profile.
| Section 2 sensitive class | Canonical labels | ke_dpa action |
|---|---|---|
| Race | OTHER | mask |
| Health status | CONDITION, MEDICATION, LAB_TEST, PROCEDURE, BODY_SITE | mask |
| Ethnic social origin | OTHER | mask |
| Conscience | OTHER | mask |
| Belief | OTHER | mask |
| Genetic data | GENE_SYMBOL, VARIANT_DESCRIPTOR, PROTEIN_CHANGE, ZYGOSITY, CLINICAL_SIGNIFICANCE | mask |
| Biometric data | ID_NUM, EYE_COLOR, HEIGHT | mask |
| Property details | STREET_ADDRESS, LOCATION, ACCOUNT_NUMBER | mask |
| Marital status | OTHER | mask |
| Family details | PERSON, FIRST_NAME, LAST_NAME | mask |
| Sex | GENDER | mask |
| Sexual orientation | OTHER | mask |
Clinical identifier checklist¶
Before disclosing or transferring a clinical text, confirm that the local audit evidence shows each applicable item was handled:
- Patient, relative, clinician, and caregiver names are mapped to
PERSONor a name component and masked. - Huduma Namba, national ID, passport, health-plan, facility, and medical-record identifiers are mapped to
ID_NUMand masked. - Kenyan phone numbers, including
+254forms, are mapped toPHONEand masked. - Street, building, postal, GPS, and county-level location details are mapped to their location labels and masked. County values remain quasi-identifying in sparse clinical populations and are not retained by this profile.
- Health status, diagnoses, procedures, medicines, tests, body sites, and other clinical concepts are masked to keep the text outside the section 46 confidentiality perimeter only after the caller validates the output.
- Ethnicity, race, beliefs, marital or family status, sex, sexual orientation, genetic attributes, and biometric references are mapped through the table above and never use a
keepaction. - The audit report identifies
ke_dpa, records that the safety sweep was enabled, and reports zero direct-identifier leakage for the validated fixture set. Production data needs its own leakage and re-identification assessment.
Local-first transfer and residency rationale¶
Run model inference, deterministic sweeping, redaction, and audit generation on the device or within the controller's Kenyan environment before any proposed external transfer. This reduces the chance that raw clinical text leaves the section 46 confidentiality perimeter and supports assessment of the safeguards and consent required by sections 48 and 49. It also preserves the option to keep covered processing in Kenya if a section 50 requirement applies.
Local-first processing is a risk-control posture, not a legal conclusion. The controller still decides whether processing is permitted under sections 44 and 45, whether the section 46 actor and confidentiality conditions are met, and whether any transfer or residency requirement applies.