HIPAA/GDPR-compliant data engineering and de-identification techniques

Answer Strategy

The interviewer is assessing your ability to translate regulatory requirements into technical architecture. Use a structured approach: 1) Define the goal (e.g., limit data to minimum necessary under BAA). 2) Outline the pipeline stages (ingest, process, store, deliver). 3) Specify controls at each stage. Sample Answer: 'First, I'd conduct a data minimization analysis under the BAA to limit fields. In the pipeline, I'd apply a two-layer approach: first, reversible tokenization for internal troubleshooting, then irreversible de-identification (e.g., generalizing DOB to year, truncating zip) before data leaves our VPC. I'd enforce these rules using infrastructure-as-code in Spark, with all transformations logged in an immutable audit trail. For delivery, I'd use SFTP with client-certificate auth and encrypt files with a PGP key provided by the vendor.'

Answer Strategy

This tests pragmatic problem-solving and stakeholder management. Focus on the tension between data usefulness and privacy loss. Sample Answer: 'On a project to build a readmission model, we needed patient demographics but couldn't use precise geolocation. I evaluated three techniques: k-anonymity (which caused too much data loss for sparse zip codes), differential privacy (which was too complex for our timeline), and targeted generalization. I implemented a controlled generalization of zip codes to hospital service areas and added Laplace noise to age. I communicated trade-offs by creating a benchmark table showing model AUC score versus privacy risk metrics (like re-identification risk estimates) for each option. This allowed the clinical and compliance teams to make an informed decision, opting for a 2% model performance dip in exchange for a 95% reduction in estimated re-identification risk.'