How Privacy Computing Becomes the "Cornerstone" of Protecting Data Security

In the era of big data, the importance of "data" in various aspects of economic development, technological progress, and even social production and life has become increasingly prominent. From ordering and shopping with mobile phones to catapulting from carrier based aircraft, the calculation and application of data have become the core elements of the digital economy era.
In this process, how to safely collect, store, and utilize massive amounts of data has gradually become a focus of social attention. Against the backdrop of great emphasis on protecting data security, especially personal privacy data security, a privacy computing technology that enables data to achieve "usable but invisible" effects is increasingly becoming the "cornerstone" of protecting data security.
Why is privacy computing? According to the currently recognized definition in the industry, it refers to the theory of computation and method for the full life cycle protection of privacy information, specifically refers to the description, measurement, uation and fusion of the privacy information involved when processing video, audio, image, graphics, text, numerical, ubiquitous network behavior information flow and other information, forming a set of symbolic, formulaic and quantitative uation standards of privacy theory of computation Algorithm and application technology, supporting privacy information protection for multi-system integration.
The application of privacy computing can achieve the "availability and invisibility" of data, meeting the needs of industrial development for the circulation of data elements while fully protecting private data. The so-called 'data is available but not visible' refers to the fact that the data owner can obtain the results and higher data value generated by the data calculation without directly sharing the original data. However, throughout the entire process, the original data is always in the hands of the data owner, ensuring its security. Taking the well-known 'face brushing' as an example, in order to ensure data security, no real facial data is collected during the formal 'face brushing' process. Instead, privacy calculations are used to directly calculate whether the 'face brushing' object is the individual.
At the software level, the core technologies of privacy computing mainly include multi-party secure computing (MPC), homomorphic encryption (HE), differential privacy (DP) and other algorithms; At the hardware level, there are chip based Trusted Execution Environment (TEE) and others. The unlocking and payment functions of "face brushing" are achieved through the TEE on the phone. In addition, blockchain technology has also been used for privacy computing in recent years.
The development of the privacy computing industry has ushered in a "spring". The policy support related to data security protection has become the driving force for the development of privacy computing, such as the General Data Protection Regulations (GDPR) officially implemented by the European Union in 2018, which is currently one of the most comprehensive and widely applied privacy protection regulations. GDPR can impose fines of up to 20 million euros (approximately 150 million yuan) for violating certain important regulations, or 4% of the company's global annual revenue.
In recent years, China has also attached increasing importance to data security protection. In 2017, China implemented the Cybersecurity Law, which became the first national level law related to cybersecurity and data protection, requiring internet companies not to disclose or tamper with collected user personal information. The Data Security Law passed in June this year also clarifies mechanisms for risk assessment, reporting, information sharing, and monitoring and warning of data security.
In June of this year, the China Payment and Clearing Association released the "Multi party Secure Computing Financial Application Evaluation Specification", which provides detailed definitions and specifications for technologies such as multi party secure computing. The industry generally believes that this is an important milestone in the implementation of privacy computing policies and regulations.
In July of this year, the "Shenzhen Special Economic Zone Data Regulations" were officially announced, becoming the first fundamental and comprehensive legislation in the domestic data field.
With policy support, China's privacy computing industry has developed rapidly. According to third-party statistical data, the number of privacy computing startups has grown rapidly since 2012. From 2018 to 2020, there were about 160 related startups, including Huakong Qingjiao, Shudu Technology, Dongjian Technology, Impulse Online, Yifang Jianshu, Nowei Technology, Xingyun Cluster, Blue Elephant Zhilian, and Guangzhishu. Large enterprises such as Ant, JD.com, Baidu, and Tencent have actively deployed in various privacy computing sub fields such as secure computing, federated learning, differential privacy, and confidential computing. In addition, enterprises with highly integrated industrial data such as WeBank and Ping An Group have also entered the field of privacy computing to carry out data value-added service.
According to research firm Gartner, by 2025, more than half of the world's large enterprises or institutions will use privacy computing to process data in untrusted environments and multi-party data analysis cases.
At the application level, privacy computing applications in the financial and medical industries have developed rapidly. As a core component of personal data privacy, medical data itself has high value, so its ownership and use rights have always faced some disputes. The emergence of privacy computing has effectively resolved this contradiction. As one of the first pilot cities of medical and health big data in China, Xiamen has established a health and medical big data center, and built a "open platform for health and medical data applications" based on privacy computing technology. On the premise of ensuring data privacy and security, it improves data use efficiency through the open platform, breaks the "data island", and builds an open data ecosystem for medical data applications.
In the financial field, privacy computing technology has been applied in areas such as joint modeling of risk control models, precise establishment of marketing models, and protection of trade secrets in credit business.
The "Privacy Computing Industry Research Report 2021" jointly released by WeBank and KPMG shows that "the domestic market size will rapidly develop, and in three years, the revenue of technology services is expected to reach 10 billion to 20 billion yuan, and even leverage the operating revenue space of data platforms worth hundreds of billions." In the future, privacy computing will become the "cornerstone" of protecting data security and further unleashing the economic value of data, with broad development space.
Industry insiders say that privacy computing provides assistance and opportunities for data compliance flow, making it possible to separate data ownership and usage rights. It is necessary to develop classification, classification, and secure usage standards for industry data in the application process based on different industry acteristics. Privacy computing, as a technological means, should be combined with laws, regulations, and policy norms to promote the safe circulation and application of data in more scenarios.