ORO: A Secure, Distributed Compute-over-Data Protocol
The traditional model for training AI on private data is flawed and risky. It relies on extracting, centralizing, and storing sensitive user data, creating security and ethical issues. This approach limits true AI personalization. ORO offers a better solution: "bring the model to the data." ORO is not just a data marketplace but a decentralized compute-over-data protocol. It enables AI developers to perform computations, like model training, on private data without moving or exposing it.
Here's how ORO works:
1.Job Request: AI developers submit a compute job to the ORO network, e.g., "fine-tune my model on conversational data from users meeting criteria X, Y, Z."
2. Secure Dispatch: ORO sends the untrained model to Trusted Execution Environments (TEEs) where permissioned data resides, keeping data secure.
3. Confidential Computation: Inside the TEE, the model trains on raw data in isolation, ensuring privacy.
4. Result Delivery: Only the trained model leaves the TEE, delivering value without exposing sensitive data.
This compute-over-data model turns risky data logistics into a secure, scalable task, enabling AI to learn from real-world data without centralization risks. By joining ORO, you contribute a secure computational node to a global, distributed supercomputer, shaping the future of ethical AI.
Source :
getoro.xyz/blog/privacy-and-…
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