Replacing Thousand-Dollar Data Clean Rooms with 110 Lines of Ephemeral, Self-Expiring Dart 

The corporate data clean room market is exploding. Projected to hit over 18 billion dollars by 2034, clean rooms are heavily used by enterprise organizations to conduct joint data analysis, such as a marketing audience-overlap study or legal due-diligence during a merger negotiation, without exposing raw customer data to the other company.

This current approach suffers from a costly contradiction: it builds permanent infrastructure to answer temporary questions.

The average company spends hundreds of thousands of dollars setting up persistent clean room environments, even though cross-company inquiries are almost always one-time events. Once the question is answered, the clean room sits idle, transforming into an unmonitored, forgotten security liability.

At the Atsign AI Architect Hackathon, third-place winner Aquib Aquil upended this entire model. He built a collaborative analytics environment that doesn’t just get decommissioned when an analysis is complete, it actually stops existing.

You can explore the source code, run the isolation proofs, and inspect the company agent file directly in Aquib Aquil’s Self-Expiring Data Clean Room GitHub repository.

The philosophy of self-expiration

Every major data clean room product on the market today defaults to a persistent footprint. They rely on manual data deletion or cloud storage retention timers. If a system engineer forgets to tear down the environment, the access keys and underlying network paths remain intact.

The Self-Expiring Data Clean Room replaces human memory with architectural enforcement. It operates on a single, powerful mechanism: the moment both corporate stakeholders click Confirm, the cryptographic keys required to decrypt the environment are permanently deleted.

Both companies submit their encrypted dataset identifiers through a write-only channel. The clean room runs a Private Set Intersection (PSI) protocol, computing a single aggregate number, which is the overlap count. Neither company can see the other’s raw records, and they cannot even read back their own submissions.

When the authorized stakeholders from Company A and Company B review the aggregate result inside their Flutter mobile companion app and click the complete button, a Retirement Policy agent fires. This instantly deletes the shared keys and the synchronization mutex across the entire system. Because the Flutter UI connects natively to the underlying Dart SDK keystore, state management handles the teardown instantly: the local reactive UI updates, an immutable audit receipt card is rendered to prove what was destroyed, and the peer network paths are broken forever.

Verifiable security through a 110-line code proof

The most extraordinary feature of this hackathon submission isn’t what the application does, but what it cannot do.

The entire backend agent running on the company infrastructure is roughly 110 lines of simple, elegant Dart code (company_agent.dart). It demonstrates that the exact same language Flutter developers use to build interactive frontends is exceptionally capable of handling strict, headless architectural boundaries at the source layer:

• Zero read capabilities: Running a search for atClient.get( or atClient.lookup( across the codebase yields exactly 0 hits. The agent literally does not possess the code paths required to read data out of the clean room.
• Total isolation: The literal string company_b does not exist anywhere in Company A’s binary.

There is no complex application-layer permissions engine that a software bug inside the Flutter UI could accidentally disable; the code to violate privacy simply was never written.

Real engineering for the real world

In alignment with the spirit of the hackathon, Aquib openly documented the real-world boundaries of his 24-hour build:

• The privacy floor: For the hackathon build, the mathematical matching engine uses salted SHA-256 hashing to compare identifiers. While highly performant for datasets in the millions, Aquib notes that a production deployment would scale this up to a blinded, ECDH-based Private Set Intersection protocol running over Atsign’s NoPorts secure tunnels to fully eliminate dictionary attacks against low-entropy data like phone numbers.
• The limit of destruction: Aquib candidly notes that while his architecture guarantees that session keys are erased from the cryptographic environment, no platform can mechanically prevent a company from manually copying or saving their piece of the aggregate result before the retirement button is pressed.

By pairing the structural clarity of the Atsign platform with the native identity routing of the Atsign SDK, the Self-Expiring Data Clean Room proves that zero-trust compliance doesn’t require a massive cloud budget, it just requires the right blueprint.

Looking back at the series

The Self-Expiring Data Clean Room wraps up our series on the Atsign AI Architect Hackathon winners. From patient-controlled healthcare data to identity-based telecom routing and ephemeral compliance clean rooms, these three projects prove that a private, decentralized internet is entirely practical when engineering starts with identity boundaries first.

Catch up on the rest of the series:

Read the overview: [Atsign AI Architect Hackathon Winner Showcase]

1st Place: [Medshare: Restoring Patient Data Sovereignty]

2nd Place: [IoT Lighthouse: Identity-Based Telecom Security]