Store all users data in their own mobile or digital twin

 Is it possible to store all users data in his or her mobile or digital twin and just let queries accessing user data run on only those data of users who choose to participate in the campaign or offer?

Yes — it’s not only possible, it’s actually the core idea behind privacy-preserving, user-centric data architectures like personal data pods, edge computing, and digital twin concepts.

Here’s how it would work in practice:

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1. Core Concept

Instead of a central server storing everyone’s data, each user’s device (or their cloud-hosted “digital twin”) keeps their complete personal dataset.
When a business, research project, or marketing campaign wants to run a query, the query itself is sent to the user’s device or twin. The device/twin executes it locally and only returns the relevant, consented output — not the raw personal data.

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2. Technology Enablers

• On-Device Storage & Processing

  • Secure storage on smartphones (encrypted DB like SQLite + OS-level keychain)

  • Edge processing frameworks (e.g., TensorFlow Lite, Core ML) for running ML queries locally.

• Digital Twin Hosting

  • Cloud-based replicas controlled by the user (e.g., Solid PODs, IPFS nodes)

  • User grants/revokes access tokens for specific queries.

• Privacy-Preserving Query Execution

  • Federated Learning: The query (or model) travels to the data, model updates come back without sharing raw data.

  • Secure Multiparty Computation (SMPC): Multiple users can compute a result jointly without exposing their individual data.

  • Zero-Knowledge Proofs (ZKP): User proves a statement about their data without revealing the data itself.

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3. Example Scenario

Let’s say a supermarket wants to find “users who bought milk more than twice last month” for a discount offer.

1. User Device: Stores shopping history.

2. Campaign Query: Sent to all devices (or only opted-in users).

3. Local Execution: Device runs the query locally.

4. Return: Device returns only a “yes” or “no” flag — or even better, directly redeems the coupon locally without sending identifiable info.

Result:

• The supermarket never sees the full purchase history.

• Users who didn’t opt in are never contacted.

• The system is GDPR/DPDP-compliant by design.

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4. Benefits

• Full User Control – Participation is opt-in at the data level.

• Lower Breach Risk – No single central repository to hack.

• Regulatory Compliance – Aligns with data minimization principles.

• Trust Building – Users know exactly what leaves their device.

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5. Challenges

• Performance – Some queries may require heavy computation; on-device optimization is needed.

• Standardization – Need a universal query language and API format for campaigns.

• Network Efficiency – Efficient broadcasting of queries without leaking participation status.

• Business Buy-In – Companies must be willing to give up centralized data collection.

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I could outline an architecture for such a system where:

• The data pods are on mobile or cloud twin.

• The queries are sandboxed and cryptographically signed.

• The results are anonymized or aggregated before leaving the device.

That would essentially make the whole system “privacy by design” but still commercially useful.

If you want, I can map this into a practical system design with both mobile and cloud twin options, and show how consent and query execution would flow. This would turn your idea into a clear implementation blueprint.