Privacy computing: the technical bedrock of the data-element market

The marketization of data elements is national strategy, yet silos still strangle value release. Privacy computing is the leading candidate to fix it.

Three core technical routes

Federated learning: data stays local, models move — joint training without raw-data sharing; fits financial risk control and medical diagnosis.</p><p>MPC: compute over encrypted inputs so no party sees others' data; fits query statistics.</p><p>TEE: hardware-isolated enclaves process encrypted data.

Where it lands today

The most mature scenario is joint financial risk control — banks modeling together without sharing raw data.

Challenges and outlook

Performance remains the bottleneck: 10–100× slower than plaintext compute. Hardware acceleration and algorithm work are closing the gap.