PII masking for privacy-grade machine learning

This post details the implementation of PII masking for machine learning data streaming pipelines at Grab. It describes a process involving PII tagging in protobuf schemas using a custom `pii.proto` library, a CI pipeline with a Python script to enforce tagging and a whitelist for exceptions, and a Flink application that consumes production data, dynamically masks PII based on schema tags (using HMAC for strings and seeded random generators for numbers/dates), and publishes sanitized data to the staging environment. The goal is to enable data engineers to tune ML models with production data while maintaining privacy by design.