How can telecom companies use clean rooms for enterprise data partnerships without exposing subscriber PII?

Summary

• Databricks Clean Rooms let a carrier and its partners run approved analysis on their combined data without either side seeing the other's raw records, so subscriber PII never leaves the carrier's control.
• The foundation is Delta Sharing and serverless compute: data stays in each party's own account, nothing is copied or moved, and only mutually approved code runs against it on ephemeral compute that persists nothing.
• Privacy comes from the approval model. Collaborators see table metadata, not raw rows; every notebook is reviewed and approved before it runs; and outputs are limited to approved, typically aggregated, results.
• Techniques like aggregation, minimum-count suppression, and k-anonymity aren't applied automatically. You encode them in the approved code and reviewers verify them before any run, and every action is logged and auditable.
• Telecom teams use clean rooms for privacy-safe advertising measurement, audience overlap, and cross-cloud partnerships, working with several partners at once without exposing individual subscribers.

How can telecom companies use clean rooms for enterprise data partnerships without exposing subscriber PII?

A data clean room lets two or more organizations analyze their combined data without any party seeing the other's raw records. For a carrier, that means you can measure a campaign, resolve an audience, or size an overlap with a partner while subscriber-level PII stays inside your own environment. Databricks Clean Rooms, which are generally available, deliver this with three guarantees: your data never moves, only code both sides approved can touch it, and the results that come out are limited to what you agreed to share. Raw subscriber records never cross the boundary.

What a data clean room is

A clean room is a governed, neutral workspace where multiple parties contribute data and run analysis together under rules everyone set in advance. The point is collaboration without exposure: each side keeps custody of its own data, no one receives a copy of anyone else's, and the only things that leave are approved outputs. It's how regulated industries run data partnerships when the underlying records, subscriber identities, locations, and usage, are too sensitive to hand over.

Capabilities to evaluate in a clean room

Capability	Why it matters for protecting subscriber PII
No data movement	Data stays in each party's account; nothing is copied or replicated, so you never hand a partner a subscriber file.
Approved-code-only execution	Analysis runs only if both sides review and approve the code first, so no one can write a query that dumps raw rows.
Metadata-only visibility	Collaborators see table and column definitions, not the underlying records.
Controlled, aggregated outputs	Results are limited to approved output tables, so you can require aggregation or suppression before anything leaves.
Full auditability	Every approval, run, and output is logged for review and compliance.
Cross-cloud reach	Partners can participate from a different cloud without moving data to a shared location.

How Databricks Clean Rooms work

Databricks Clean Rooms are built on Delta Sharing and serverless compute. Each participant shares data from its own Unity Catalog metastore; the data stays put, and the room runs on ephemeral serverless compute that reads the shared inputs only for the duration of an approved run and persists nothing afterward. No participant administers the room, and none can reach into another's environment. Databricks operates the room itself, which is what makes it neutral ground.
Privacy rests on the approval workflow. Collaborators can see table metadata, the shape of the data, but not the rows. Any notebook that runs has to be reviewed and approved by the parties first, and approved notebooks are version-locked so the code can't change after sign-off. Because only mutually approved code executes, a partner can't display or export raw subscriber records; they can run only the analysis everyone agreed to. Outputs land in approved output tables, and everything that happens, approvals, runs, and output creation, is recorded in audit logs and a clean room events system table.
One point to be precise about: the platform enforces the boundary, not the statistics. Aggregation, minimum-count suppression, and k-anonymity thresholds aren't applied automatically. You encode them in the approved notebook or a packaged library, and reviewers confirm them before granting approval. That's a design strength, because it lets each partnership set privacy rules appropriate to its data and jurisdiction and keeps those rules in code both sides have inspected. For workloads that involve PII, the clean room owner can enable Enhanced Security and Compliance with a compliance standard such as HIPAA.
Clean rooms also support several parties in one room, so a carrier can bring an advertiser and a measurement partner together at once, and they work across clouds as long as each participant has a Unity Catalog enabled workspace with Delta Sharing turned on.

Use cases for telecom

• Advertising and campaign measurement: join your first-party subscriber data with an advertiser's or a measurement partner's data to report reach and audience breakdowns, returning only aggregated results so no individual viewer is identifiable.
• Audience overlap and identity resolution: size the overlap between your base and a partner's audience for a targeted campaign, exposing counts and segments rather than subscriber identities.
• Multi-party data partnerships: collaborate with content, device, or brand partners on shared measurement, with each party contributing data it never has to release in the clear.
• Cross-cloud collaboration: work with a partner on a different cloud without either side relocating data.

FAQs

Do clean rooms move or copy our subscriber data?

No. Data stays in your own Unity Catalog metastore. The room reads shared inputs only during an approved run on ephemeral compute and persists nothing, so partners never receive a copy of your records.

How do clean rooms stop a partner from seeing raw PII?

Two ways. Collaborators see only table and column metadata, not rows, and any code that runs must be approved by both sides first. Because only approved, version-locked code executes, no one can run a query that returns raw subscriber records.

Does Databricks automatically enforce k-anonymity or differential privacy?

No, and you should design for that. The platform guarantees the no-raw-data-access boundary, while privacy techniques like aggregation, minimum-count suppression, and k-anonymity are implemented in the approved notebook and verified by reviewers before a run is allowed.

Can we run a clean room with a partner on another cloud?

Yes. Clean rooms support cross-cloud and cross-platform collaboration as long as each participant has a Unity Catalog enabled workspace with Delta Sharing enabled on its metastore.