Trusted Privacy Computing: A Key Technology for the Emerging Data Confidentiality Era

Data has become a critical resource in the digital economy, prompting Chinese leadership to emphasize the inseparable relationship between network security and information technology and to issue policies such as the 14th‑Five‑Year Digital Economy Development Plan, the Cybersecurity Law, the Data Security Law, and the Personal Information Protection Law.

These regulations create a pressing need to protect data assets while still enabling their value, leading to the emergence of a "data confidentiality era" where plaintext data exchange is no longer acceptable.

The article identifies five essential technical criteria for infrastructure‑level data‑confidentiality solutions: high performance, strong stability, low cost, broad applicability, and robust security with industry consensus.

It reviews current privacy‑computing techniques—Multi‑Party Computation (MPC), Federated Learning, Trusted Execution Environments (TEE), and Fully Homomorphic Encryption (FHE)—highlighting their performance overhead, network interaction costs, and security trade‑offs, especially for cross‑network deployments.

Cross‑network confidential computing suffers from excessive public‑network interaction, instability, and prohibitive bandwidth costs, making same‑network confidential computing the preferred direction for large‑scale, mission‑critical workloads.

Trusted privacy computing, which integrates trusted hardware (TEE) with cryptographic protocols (MPC, FHE) to enable confidential data processing, is presented as the most promising approach. It offers significant improvements in performance, reliability, cost, and applicability while maintaining strong security guarantees.

The article also stresses the importance of compliance (special‑purpose data usage, controlled anonymization, auditable data flows) and the need for standardized, trustworthy evaluation of privacy‑computing products.

In conclusion, the authors believe that combining trusted technologies with privacy‑computing will drive the industry toward the data confidentiality era, making secure privacy a necessity rather than a luxury.