GE>[Z,^<4TR/].JR<,^F]EX'/M>(?O
SYSTEM PROCESSING...
GE>[Z,^<4TR/].JR<,^F]EX'/M>(?O
SYSTEM PROCESSING...
Posted: 2025-06-18 20:39:24 UTC
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
Status
Last Updated
2025-06-18 20:39:39 UTC
Verified By
Rollup News
ORO's privacy is a foundational principle, ensuring that personally identifiable information (PII) is never exposed when contributing, routing, or using data to train models. This is achieved through zkTLS and Trusted Execution Environments (TEEs), enabling AI to learn without directly accessing raw data.
Privacy is a foundational principle, not an optional feature.
zkTLS and TEEs protect data throughout the process.
AI learns without accessing raw data, preserving privacy.
Ensuring data privacy while still allowing for effective machine learning.
Protecting data from exposure during transmission and computation.