Embracing Trusted Privacy Computing in the Era of Industrial Data Confidentiality

Industrial internet development generates massive, fast‑growing data, creating strong demands for data security and ushering in a new era of data confidentiality where plaintext data will be replaced by protected, dense‑state data.

Data is likened to the new oil, yet islands of isolation and various risks hinder its collection, sharing, and circulation.

Since the Data Security Law took effect on September 1, 2021, a series of data‑security regulations have highlighted the need for compliance, bringing privacy computing to the forefront.

Privacy computing enables analysis and computation on data without revealing the original data, ensuring "usable but invisible" and "computable but unidentifiable" during data flow and integration.

It is an interdisciplinary technology stack encompassing AI, cryptography, and data science, with key branches such as secure multi‑party computation, federated learning, and trusted execution environments, all of which have been researched for years.

Industries like finance, healthcare, and government urgently need compliant data usage; in the industrial internet, privacy computing is presented as an effective solution for protecting data value, defining data assets, and unlocking industrial value.

At the Industrial Internet Security Forum on March 25, Ant Group Vice President Wei Tao delivered a speech titled "Welcoming the Era of Industrial Data Confidentiality, Embracing Trusted Privacy Computing," outlining technical characteristics of the dense‑state era and proposing trusted privacy computing as a solution for large‑scale scenarios such as "East‑Data‑West‑Compute".

The talk is organized into four parts:

1. Technical Challenges of the Data‑Confidentiality Era – Industrial internet, originally proposed by GE, integrates next‑generation ICT with manufacturing, requiring a network‑based, platform‑centric, data‑driven ecosystem that transforms traditional production models.

China’s systematic and forward‑looking industrial internet plans emphasize data aggregation, flow, analysis, and application as key resources.

Data volume in industrial internet reaches petabyte scales, creating bottlenecks for storage, bandwidth, and latency, especially for cross‑network privacy‑preserving computations.

2. Technical Challenges and Trusted Privacy Computing – The dense‑state era demands five qualities: high performance, strong stability, flexibility, low cost, and strong security.

Performance must support billions of training samples per hour; stability requires "four nines" (99.99%) reliability, aiming for "five nines" (99.999%).

Cost must be comparable to plaintext computation, and flexibility requires algorithms to remain unchanged as participants or scenarios grow.

Security must meet industry‑level standards without sacrificing other attributes.

Existing approaches such as Trusted Execution Environments (TEE), secure multi‑party computation (MPC), and federated learning each have strengths and limitations.

3. Why Trusted Privacy Computing? – It provides a trustworthy layer for the entire data lifecycle, enabling compliance, business support, and security guarantees.

Anonymous processing ("computable but unidentifiable") is essential under laws like the Personal Information Protection Law, which mandates explicit consent for each data use.

Trusted privacy computing combines TPM/TEE hardware roots of trust with MPC and FL to resist supply‑chain and side‑channel attacks.

The Trusted‑Environment‑based Cryptographic Computing (TECC) framework splits data into encrypted shards processed across multiple TEE clusters, ensuring that no single cluster can reconstruct plaintext, thereby mitigating supply‑chain and magnetic attacks.

TECC’s centralized aggregation point allows remote verification and reduces bandwidth constraints, making it suitable for massive scenarios like "East‑Data‑West‑Compute".

4. Industry Co‑construction and Outlook – Privacy computing is still in its early stage; over 50 companies have launched 67 products as of July 2021.

The market is expected to reach 10–20 billion RMB in software sales and services within three years.

Challenges remain in product maturity, engineering, performance‑security trade‑offs, and building trust, requiring coordinated effort from regulators, standards bodies, and industry players.

Ultimately, trusted privacy computing aims to protect data while enabling its valuable use across the industrial internet and broader digital transformation.