Privacy-Preserving Computation enables data computation without revealing original values, considered a key technological breakthrough for data factor circulation.

Core Technology Comparison

Federated Learning: Multiple parties train models locally, sharing only gradient updates, protecting original data. Widely applied by Google and Apple on mobile.
Secure Multi-Party Computation (MPC): Multiple parties jointly compute functions without exposing each other’s inputs. Suitable for statistical analysis and machine learning inference.
Homomorphic Encryption: Directly compute on encrypted data, theoretically the most perfect but with high performance overhead (currently ~1000x slower).

Application Scenarios

Financial joint risk control: Multiple banks jointly model without sharing customer data. Medical data sharing: Hospital inter-record data analysis protecting patient privacy. Government data opening: Releasing government data value while protecting citizen privacy.

Conclusion

Privacy computing technology is moving from laboratory to commercial application. 2026 will be the year for large-scale implementation of privacy computing.