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Achieving privacy-preserving data trading with linear overhead and auditable fairness could revolutionize how sensitive data is exchanged on public blockchains.
LLMs can now write better quantitative trading algorithms than humans, thanks to a new framework that turns unstructured financial reports into executable code.
Securely sharing patient data across different blockchain systems is now more feasible, thanks to a new scheme that minimizes overhead and defends against both internal and external threats.
