4.3.2 Technical Collaboration

A "Privacy Computing Closed Loop" with 1+1+1>3

The privacy computing network of Anonymous Imperium is not a simple superposition of the three technologies, but realizes in-depth collaboration between technologies through a "scenario trigger + dynamic scheduling" mechanism, and outputs the optimal solution for different scenarios:

• Single-organization data computing (such as in-house data analysis of banks): "Homomorphic encryption" is preferentially used. Data can be computed without leaving the warehouse, which is simple to operate and has strong privacy.

• Multi-party data collaborative computing (such as cross-enterprise joint modeling): The combined solution of "MPC + federated learning" is automatically activated - MPC is used to realize the secure exchange of data shards between parties, and federated learning is used to complete the decentralized training of the model, taking into account data collaboration and computing efficiency.

• Large-scale AI training (such as the global anonymous credit score model): "Federated learning" is the core. "Homomorphic encryption" is introduced in the parameter aggregation stage to protect parameter privacy, and "MPC" is used in the model verification stage to realize joint verification by multiple nodes, forming a full-process privacy protection.