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A healthcare analytics platform ingests tabular patient data through a streaming ingestion pipeline into BigQuery for visualization in Looker dashboards. To comply with privacy regulations, you must configure Cloud Data Loss Prevention (Cloud DLP / Sensitive Data Protection) to conditionally sanitize incoming records.
Specifically, you need to configure a de-identification pipeline where:
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StudyCertsPATIENT and FACTOID fields are inspected for PERSON_NAME infoTypes.[PERSON_NAME].AGE field is greater than 89.How should you structure the DeidentifyConfig for this ingestion pipeline?
Configure TransformationDetailsStorageConfig with a BucketingConfig on AGE and apply ReplaceWithInfoTypeConfig across all table columns globally inside an InspectConfig.
Configure InfoTypeTransformations using CryptoDeterministicConfig on PERSON_NAME and filter incoming messages in Pub/Sub using SQL filter expressions on the AGE attribute.
Configure RecordTransformations with a FieldTransformation containing a CharacterMaskConfig on AGE, and define a RowCondition to drop rows matching PERSON_NAME.
Configure RecordTransformations with a FieldTransformation targeting the PATIENT and FACTOID fields, attach an infoTypeTransformations using ReplaceWithInfoTypeConfig, and attach a RecordCondition evaluating AGE greater than 89.
Configure TransformationDetailsStorageConfig with a BucketingConfig on AGE and apply ReplaceWithInfoTypeConfig across all table columns globally inside an InspectConfig.
Configure InfoTypeTransformations using CryptoDeterministicConfig on PERSON_NAME and filter incoming messages in Pub/Sub using SQL filter expressions on the AGE attribute.
Configure RecordTransformations with a FieldTransformation containing a CharacterMaskConfig on AGE, and define a RowCondition to drop rows matching PERSON_NAME.
Configure RecordTransformations with a FieldTransformation targeting the PATIENT and FACTOID fields, attach an infoTypeTransformations using ReplaceWithInfoTypeConfig, and attach a RecordCondition evaluating AGE greater than 89.
This solution uses Cloud DLP's structured tabular de-identification capabilities within DeidentifyConfig, specifically using RecordTransformations and FieldTransformations with a RecordCondition.
FieldId entries for PATIENT and FACTOID within FieldTransformation ensures that de-identification inspection is scoped precisely to the relevant columns.infoTypeTransformations with PERSON_NAME and applying a primitive transformation of ReplaceWithInfoTypeConfig replaces matching personal names with the infoType label [PERSON_NAME].RecordCondition with a relational operator of GREATER_THAN on the AGE field (value 89) ensures the transformation executes only on rows meeting the compliance threshold while leaving others unaltered.Table objects without requiring unnesting or flat string concatenation.This approach directly leverages Cloud DLP's native conditional record evaluation without requiring custom conditional routing code in the ingestion pipeline, ensuring consistent policy enforcement directly at the data protection layer.