Vana announced on November 20 the upcoming launch of its mainnet, introducing a blockchain ecosystem designed to enable decentralized control and monetization of user-owned data.
According to the Vana Foundation’s announcement, the new EVM-compatible Layer 1 blockchain will support DataDAOs, where users can pool their data and retain full control over its usage.
DataDAOs with User-Owned Data Control
Based on the statement, DataDAOs will allow individuals to aggregate and manage their data collaboratively. These decentralized entities will enable participants to pool personal datasets while maintaining privacy and consent over how the data is accessed or utilized.
The VANA token will serve multiple roles, including validating data contributions, governing the network, and paying transaction fees. Users contributing data to DataDAOs can receive rewards through a system that evaluates the value of the datasets provided.
We’re entering the next phase of Vana: with the creation of the Vana foundation, we’re gearing up for the upcoming launch of Vana mainnet, a bold step forward in accelerating the AI data revolution.
Our mission? To empower individuals to reclaim control over their data and… pic.twitter.com/s1pmRKqfD2
— vana (@withvana) November 20, 2024
Privacy and data security are key priorities for Vana’s architecture. According to the foundation, users can store their data securely and decide on access permissions without losing control over ownership.
The announcement indicated that Vana’s decentralized infrastructure is designed to comply with global data regulations like GDPR and CCPA, offering a compliant and scalable solution for user-owned data systems.
“To upload personal data to a DataDAO, you can either be paid in token rewards up-front by a DataDAO, which then encrypts your data with a key that is controlled by the DataDAO, and can vote to use the collective’s data to train a model,” the statement reads.
“The other option is to keep your data in your own personal storage, and only decrypt that data when a data buyer pays to access it; they then train their model in a secure environment,” said the Foundation.
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