Technical Overview

vBase acts as an “easy button” for creating and validating cryptographic data commitments.

The diagram below illustrates a typical vBase data-production and validation flow:

The vBase Process
The vBase Process

Step-by-Step Overview

  1. Data generation — vBase does not interfere with existing data pipelines. Producers continue generating and storing data in their usual systems (e.g., raw files, SQL databases, S3, etc.).

  2. Commitment creation — when producers want to establish provenance, they call lightweight vBase commitment APIs. These APIs can either receive pre-computed content fingerprints (hashes) or can read the data to perform the necessary hashing operations.

  3. Blockchain anchoring — vBase’s Commitment Service records these content IDs and timestamps on the blockchain, creating a tamper-proof, privacy-preserving provenance record. The blockchain’s decentralized ledger ensures that each record is immutable, independently verifiable, and long-term auditable.

  4. Data sharing — vBase does not alter or replace existing sharing workflows. Producers continue distributing data to consumers through their normal channels, while vBase records remain independent of, and external to, the data itself.

  5. Data receipt — consumers receive the shared data as usual, without needing any special integration or credentials.

  6. Verification — when consumers wish to confirm provenance, they call vBase’s verification APIs to check data against on-chain commitments.

  7. Validation — the blockchain records prove both when the data became known and that it has not been modified since, ensuring end-to-end data integrity and trust.

Why It Matters

By integrating vBase, organizations gain cryptographic proof of data provenance without disrupting their existing workflows.

This non-intrusive architecture maintains privacy and operational efficiency while providing verifiable, long-term assurance of data integrity and reliability — strengthening trust between producers, consumers, and partners across the data lifecycle.

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