Your organization uses Google Cloud to process large amounts of location data for analysis and visualization. The location data is potentially sensitive. You must design a solution that allows…

Enabling location restrictions on Compute Engine instances addresses data residency for compute resources but doesn't specifically cover the secure storage of the data itself or granular control over data exposure during processing.

Using the Cloud Data Loss Prevention (Cloud DLP) API helps scan and de-identify sensitive data to minimize exposure, but it's a data protection tool, not a primary storage solution that, on its own, addresses data residency and the secure processing workflow comprehensively.

Creating regional Cloud Storage buckets addresses data residency for object storage, and Object Lifecycle Management helps with retention, but this solution doesn't specifically cover the secure *processing* of the data or methods to minimize *exposure* during analysis beyond basic storage access control.

Storing data within BigQuery and specifying the dataset location ensures data residency according to regulatory and internal policies. Using BigQuery authorized views minimizes data exposure by allowing users to query specific data subsets or query results without granting direct access to the underlying sensitive tables, thereby securing processing and analysis while adhering to the 'need to know' principle.