The transmission of information is the foundation of human civilization. From the ancient people's knotting and memorizing, to the use of words as the carrier, in today's digital age, data is deeply integrated with our production and life. The full mining and analysis of data has also brought technology and industry to the depths of application, changing our production, life and consumption.
The circulation value of data is obvious to all. Take the health code that most people have used as an example. It integrates big data itinerary, epidemic prevention and vaccination information, which is the guarantee for our epidemic travel. The sharing of these data has a significant effect on fighting the epidemic. Behind the "Health Code" and "Travel Code" is the support of big data across the country. The interconnection and interoperability of public health data is not limited to the effectiveness of epidemics. In future public health emergencies, data can help the government to better analyze and predict, allowing the government to make more scientific decisions to protect the health and safety of the people.
In the medical field, the sharing of data security is a "dilemma and two safety" problem. Private data has attracted much attention, and it is easy to cause multiple parties to monitor private data. But this also just gives the stage and world for privacy computing to display. In the contradiction of serious leakage of private information, but the growing momentum of data processing and analysis, the promotion of data use, value and contribution, urgently needs privacy computing to resolve the problems in circulation.
Exponential Productivity
In the context of the surge in data, there are thousands of industries standing behind the massive data, data rights confirmation and competition among industries, and data islands become the biggest obstacle in the use of data assets. It not only blocks and blocks some development possibilities of the system and scenarios, but also affects the exchange, integration, and application of information to varying degrees.
And privacy computing has become the only technical solution in the process of data value flow. Privacy computing can realize the fusion and use of data without exposing the original data. Moreover, privacy computing technology can be realized to precisely control the purpose and method of data fusion, that is, to control what and how data is counted, making it possible to supervise the use of data.
Privacy computing has become a bridge for data sharing and security, allowing data from multiple data holders to not only communicate and collaborate normally, but also to ensure the privacy of data that is invisible to complete data analysis, calculation, and application.
For example, we know that in the medical field, a lot of large-scale and high-quality data have been accumulated. In the process of implementing AI technology, we can rely on these high-quality data to train better models. However, in the actual application, due to issues such as data privacy, confirmation of rights, and isolated islands, there are many obstacles to the connection and collaboration between data. The theoretical advantages of AI in the medical field cannot be fully utilized. Privacy computing can well solve the data dilemma in the medical field, allowing data to be connected and shared to create value even when it is invisible.
The value characteristics of privacy computing data make it start to land in data-intensive industries such as finance, medical care, communication operators, government and other fields. These fields themselves are the highlands of data value, and the production and application of data are at the forefront of various industries. At the same time, these industries have high standards for data. For these fields, the effective use of data can amplify the potential behind it, which is of great value.
Taking the financial field as an example, privacy computing is mainly used in risk control and marketing in finance. With the development of blockchain technology, the combination of privacy computing and blockchain technology can also empower more financial scenarios, such as cross-border payment, supply chain finance, etc. In the anti-money laundering business, due to the incomplete grasp of the business data of small and medium-sized enterprises, banks were very cautious in lending to small and medium-sized enterprises. It is difficult for small and medium-sized enterprise loans to become a major disease that hinders the development of marketization. In privacy computing and blockchain technologies With the combination of the bank, the bank can better collect relevant information, exchange parameters with peer institutions, and jointly calculate and model, which solves the problem of back-checking data of small and medium-sized enterprises.
Privacy computing can not only realize the "multiplication effect" of data on technological innovation in finance, medical and other fields, but also fully protect data privacy, making data available and invisible in the process of circulation and fusion, the productivity of data can be fully amplified, and the value will also increase accordingly. A rising tide lifts all boats.
A fusion of technical genres
According to the State Internet Information Office, the scale of China's digital economy has reached 31 trillion yuan, accounting for about one-third of GDP. According to IDC data, the global data volume will reach 175ZB in 2025 and maintain an exponential growth trend. Against the backdrop of massive data growth, data compliance policies are also constantly being followed up.
The "Data Security Law" and "Personal Information Protection Law" began to be implemented around October last year. Privacy computing has also become a technical guarantee for data security and compliance applications, and has become the most anticipated optimal solution, sought after by capital and enterprises. According to the privacy computing report released by KPMG, it is predicted that in the next three years, the revenue service fee of privacy computing will reach about 20 billion, and the operating income that can be leveraged by the data platform can reach 100 billion.
Under the background of policy and market drive, start-up technology companies such as Internet giants, network security companies, and big data companies have entered the market one after another, seizing opportunities in the blue ocean of data value applications. The entry time of most enterprises is around 2018, and the technical genres are mainly three genres: multi-party secure computing, federated learning, and trusted execution environment. In the process of actual commercialization, the single-path technology model gradually fails to meet market demand, and the integration of the three technology paths has become the main trend now. At present, in the privacy computing track, Ant Financial, Huakong Qingjiao, Yifang Jianshu, Shudou Technology, Impulse Online, etc. are taking the lead in the track.
Most of these companies also use finance and medical care as their main application scenarios. The financial and medical fields are data-intensive industries, and the value contained in the data is high. The data in the industry is sensitive, and the characteristics of privacy computing are naturally compatible with it. The application scenarios of finance and medical care also continue to have cases where privacy computing has come to fruition.
For example, privacy computing in the financial field can be applied to joint marketing, joint risk control, unified credit, business compliance, etc. in the application of banks. For example, banks can conduct joint statistics without exposing customer assets, carry out comprehensive customer management, Query data or improve precision marketing capabilities, etc. As for the implementation of the government, some cities in Jiangsu and Zhejiang have already realized the sharing of government affairs data and bank financial data through privacy computing technology, and these data are actively used in anti-fraud work.
The products and solutions of privacy and security computing have received a strong response from the market and capital, and 2021 is called "the first year of privacy computing". Among them, there are favorable policies, such as data resources are becoming the third type of resources as important as human resources and natural resources. Data, like land, labor, technology, capital, etc., are also written into national documents as factors of production. The use of data is significant both at the industry and at the societal level.
However, in the process of continuous landing of privacy computing, there are other voices in the market who question: privacy computing can guarantee the security of data, so who will define the security of privacy computing system?
There are many types of technologies involved in data security protection, the threshold is high, and various technical terms make people feel dizzy. Moreover, because the data is imperceptible, it can only be understood unilaterally through the introduction of the company. The answer to this core doubt is that the common choice of data service providers is to leave it to the market to jointly build industry standards. The points considered in formulating security standards for privacy computing include multiple security risks such as algorithm security, password security, and product security. A series of standards for interconnection, industry scene standards, and testing systems have begun to be gradually discussed and developed. Various industry alliances for privacy computing Jointly promote the implementation of technical specifications and applications to make them develop in a healthy and orderly manner.
Limited ceiling or vast world?
The potential energy refracted by data value after analysis and calculation is immeasurable. For big data applications or artificial intelligence enterprises, reasonable boundaries and ways to use user data have become one of the core driving forces of enterprises and industries. In the start-up of the privacy computing market under this situation, there is a smooth market, policy fast lane acceleration, and some roadblocks that need to be overcome.
1. Insufficient expression of market demand. For fields such as finance, communications, and government, due to the baptism of digital technology in the Internet industry in recent years, the cost of acceptance and education is low, while in traditional large data fields such as industry, transportation, and energy, the degree of digitalization is limited, and the promotion of privacy computing market education The cost and time are relatively long, and the accurate expression of market demand feedback is limited.
2. The management standards of data in some industries are chaotic, and there are obstacles to the opening of data. For example, in the medical field, the level of digitization is uneven, data management lacks a unified standard, and the connection of data requires the cooperation of multiple parties. There are many closed-source data platforms, and the degree of coordination and execution of data parties is limited.
3. The standardization degree of privacy computing products is low, and the versatility is poor. The products and solutions in privacy computing are mostly customized, which also means that the cycle and cost are high, and the development of scale is difficult to spread.
In the infrastructure phase of privacy computing, everything is being explored and tried. There are many problems to be solved. It is still far away from the envisioned future era of sharing profits based on data transaction volume. Privacy computing and many high-tech to b platforms Similarly, it is a technology that needs to be cultivated and in-depth, and players need to be prepared for long-distance running. The consensus in the industry is that it will take ten years for full-scale application.
Regarding the future of privacy computing, there are also other voices in the market, because the privacy computing track involves the transaction and circulation of sensitive data. Compliant parties build it, and the privacy computing in the market can only exist as a technology supplier. The construction of a data infrastructure platform as a production factor will ultimately control the government, and the expected development space will not be as good as the theoretical assumption. Ceilings are limited.
Whether it is a limited ceiling theory or an infinite world, we can confirm that the unique advantages of privacy computing will make the value of data qualitatively change. And this also means that in the data age, privacy computing that promotes the healthy development of data ecology will be the best booster for data development. As long as the value of data is anchored, privacy computing that escorts its leap will have a broad development space. As for how far to go and how deep to go, the market and time will give the answer.
