2021 Tencent privacy computing white paper

The following is the 2021 Tencent privacy computing white paper recommended by recordtrend.com. And this article belongs to the classification: research report, network security.

Privacy computing is a key technology path to ensure data security and compliance in the process of data fusion application. Its business model, application scenarios, technological changes, industrial trends and legal issues are becoming the focus of attention from all walks of life, such as politics, industry, learning, research and application. In this context, several Tencent departments jointly wrote Tencent privacy computing white paper 2021, which aims to discuss and promote the development of privacy computing technology industry with the industry, and seek the balance between development and security in digital governance.

The white paper is mainly divided into five parts:

The first part describes the development background, basic concepts and main functions of privacy computing.

The second part mainly analyzes the technical system of privacy computing, focusing on the development of Federated learning, trusted computing, secure multi-party computing and the integration of blockchain and privacy computing.

The third part mainly describes the key industries and scenarios of the current application of privacy computing.

The fourth part focuses on the role and pain points of privacy computing in data security compliance from the legal perspective.

The fifth part looks forward to the development of privacy computing from the perspectives of technology, application and law.

The development of privacy computing is still in its infancy, and is accelerating with the research of industry, University, research and application, as well as the changes of policy environment and user needs. At present, our understanding of privacy computing is also in the exploratory stage. In the future, it will be revised on the basis of continuous and in-depth research according to the practice of Tencent and its partners and feedback from all walks of life.

Some concepts:

Privacy computing is a technology and system jointly calculated by two or more participants. Participants conduct joint machine learning and joint analysis on their data through cooperation without divulging their own data. The participants of privacy computing can be different departments of the same institution or different institutions.

Federated learning is a distributed machine learning technology and system, which includes two or more participants. These participants conduct joint machine learning through secure algorithm protocol. They can jointly model multi-party data sources and provide model reasoning and prediction services when all parties’ data are not local.

Secure multi-party computing is a technology and system to safely calculate the agreed function when the participants do not share their own data and there is no trusted third party.

Trusted computing refers to the implementation of trusted execution environment (TEE) with the help of hardware CPU chip, so as to build a protected “enclave”. For applications, its enclave is a secure content container, which is used to store sensitive data and code of applications and ensure their confidentiality and integrity.

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