Who are the top players in the 2021 privacy computing track billion-level financing “get together”?
On November 1, the “Personal Information Protection Law” was formally implemented. “Currently on the eve of the detonation of the private computing business”, Qiming Venture Capital partner Zhou Zhifeng, based on his many years of practice and observation in the private computing industry, came to this conclusion: “2022 may become the first year of large-scale commercial implementation. .”
After ten years of sharpening a sword, Frost Blade has never tried. The privacy computing track that has been devoted to many years has finally won the favor of many capitals. In 2021, the privacy computing industry is like “opening up”, with leading companies emerging, and technological innovation and commercial implementation going hand in hand.
How big is the scale of this blue ocean market? In the short term, according to Gartner data, by 2024, spending on privacy-driven data protection and compliance technologies will exceed 15 billion U.S. dollars globally, or more than 100 billion yuan; according to KPMG forecasts, the domestic privacy computing market will grow rapidly. After three years of development, technical service revenue is expected to reach 10-20 billion yuan, and even leverage the 100 billion-level data platform operating income space.
From a long-term perspective, Zhou Zhifeng believes: “Based on the background of huge data processing needs, the commercial market for private computing technology itself is very exciting. Moreover, after the private computing market gradually matures, it will be related to artificial intelligence and distribution. The integration and development of new technologies such as mobile ledgers and edge computing has an inestimable future.”
Undoubtedly, the venture capitalists who look for potential stocks all year round have a precise vision and a keen sense of smell. Qiming Ventures has been in the privacy computing track for many years and has invested in top privacy computing companies such as Tongdun Technology and Fengwei Technology. Zhou Zhifeng revealed: “We have defined privacy computing as a track that has the potential to grow into a megatrend (Megatrend) since 2018, and systematically discuss the development of this technology with top domestic and foreign universities, Internet technology companies and research institutes. From the perspective of time, privacy computing is in the accumulation period of technology to application transformation in 2018-2019, and 2020-2021 is the exploratory period of commercial landing.”
Through research on the development of top privacy computing companies, the Hashoku Think Tank has learned that the core barriers of the privacy computing industry are quite high. Head privacy computing companies face self-developed privacy computing technologies and platforms, academic capabilities, and commercial landing capabilities. The contest.
However, the competition among privacy computing industries is still in its infancy, and it is far from developing to the stage of vertical product competition among industries. With high trees and no trees, the development of the industry is inseparable from the joint efforts of privacy computing companies to promote. “
The primary market is booming, and the privacy computing industry is emerging. What preparations need to be made by major leading companies to become the main force sharing this big cake when the market breaks out? It is worth discussing.
Privacy computing, a blue ocean of hundreds of billions
“In the blue ocean market of privacy computing, there is not a competitive landscape, and companies have enough space to acquire brand-new markets or scenarios through differentiated methods.” Zhou Zhifeng said.
2018 and 2019 are two years of intensive establishment of privacy computing companies. Judging from the background of the year, the EU introduced the General Data Protection Regulation in May 2018, and the fines for illegal companies can reach up to 20 million euros (approximately 150 million yuan) or 4% of their global turnover, which is higher. Whichever prevails.
This also makes the regulation known as “the most stringent data protection law in history.” That year, American companies such as Facebook and Google became the first defendants under the GDPR Act. Google was finally fined 50 million euros by the French data protection regulator. In addition, in July 2019, British Airways violated the General Data Protection Regulation. Was fined 18339 million pounds (approximately 1.58 billion yuan).
At that time, China also had a preliminary awareness of privacy protection. Some experts and professors who have long studied data security protection realized that there will be a huge market for privacy computing.
Fengwei Technology was quite representative of the intensive establishment of privacy computing companies at that time. Recalling the transfer of research from abroad to China, Professor Wang Shuang, CTO of Fengwei Technology, said frankly: “In 2011-2018, medical care with privacy protection was carried out in the United States. For health big data analysis and research, for work reasons, there have always been opportunities for academic exchanges with China. In 2018, China has begun to draft the “Data Security Law” and the “Personal Information Protection Law”. I am aware of China’s era of privacy protection It is here. As the second largest economy in the global digital economy, the market based on data needs and applications will be very huge. At that time, it was decided to return to China to develop privacy computing technology. After a period of planning, it will officially land in Hangzhou in 2019.”
It turns out that they are right. Regardless of market potential or policies and regulations, it is time for the wind of privacy computing to blow up.
In terms of potential scale, data released by the 2021 World Artificial Intelligence Conference show that the scale of China’s digital economy has reached 41 trillion yuan.
From a legal basis, the implementation of policies and regulations such as the “Data Security Law of the People’s Republic of China” and the “Personal Information Protection Law of the People’s Republic of China” in 2021 will legally set a moat for data privacy protection, and the legalization of data usage is imminent. And privacy computing has become the only technical solution for data privacy security and openness.
The computing power think tank has noticed that very few companies have laid out private computing tracks in advance to carve up this market pie. According to IT Orange Data, there are 29 companies related to privacy computing and 86 investment institutions. There is no shortage of well-known investment institutions such as Sequoia China, IDG Capital, Cornerstone Capital, Qiming Venture Capital, Zhiyuan Internet, Lenovo Ventures, CICC, and Huaxing Capital.
“Several leading companies have gone through multiple rounds of financing, and it can be seen that the development of the privacy computing industry is accelerating.” Zhou Zhifeng said.
Before 2018, there was not even the concept of privacy computing. The rise of the business model of private computing has only been less than 4 years from 2018 to the present, but the industry has already developed to take shape. Currently, it is roughly divided into 4 categories of privacy computing companies.
The first type of enterprise is that the traditional Internet companies have built their own private computing departments , which are mainly used to serve the internal and surrounding ecosystems of the enterprise. However, the cooperation between the major Internet companies across enterprises is also due to neutrality and other issues. waiting for solution.
The second type of enterprise is the existing big data business enterprise, which integrates privacy computing technology into the business, and assists the development of its data business through a specially customized privacy computing module.
The third category is the transformation of the blockchain to the privacy computing industry. Under the single application of the blockchain, only data traceability can be completed, and there is no way to solve the protection of data in the calculation. Some industrial projects require the combination of blockchain and privacy computing. , This kind of blockchain companies provide related services externally by using some privacy computing frameworks.
The fourth category is companies that focus on privacy computing, such as Fengwei Technology belongs to this category of companies. Its core team has more than 10 years of research experience in the field of privacy computing. The self-developed platform can support applications in different fields such as medical care, finance, and government security. Provides an adaptive privacy computing solution under the premise of accuracy and security.
Since its development, venture capital companies that smell the delicious cakes have invested more and more intensively in leading companies that focus on privacy computing. Once you target the rare seed players in this blue ocean market, there will be a phenomenon of “capital gathering”.
In 2021, good news about the financing of the privacy computing industry are frequent. In May, Nebulas Clustar received 11 million US dollars in A+ round of strategic financing. In mid-July, Fushu Technology completed hundreds of millions of B and C rounds of financing. At the end of July, Yifang Jianshu completed more than 300 million. Yuan-scale B+ round of financing, in August Fengwei Technology won 100 million yuan-level B round of financing. In October, China Securities Clearing Corporation announced the completion of 500 million yuan in Series B financing, surpassing Yifang Jianshu’s 300 million yuan financing and setting the highest financing record in the privacy computing industry.
Industry that came out of the laboratory
Before the industrialization of privacy computing technology, it experienced more than 10 years of scientific research precipitation. Although 2020 is called the first year of private computing by industry insiders, the research on private computing technology has been precipitated for decades. Since Professor Yao Qizhi put forward the “Millionaire Problem” in 1982, privacy computing technology has become a field that many aspirants have focused on.
Multi-party secure computing, federated learning, trusted computing, differential privacy, and homomorphic encryption and other theories to solve privacy computing problems are vying for the first, and theories continue to mature in practice. Through communication with multiple privacy computing companies, the Hashoku Think Tank has learned that although multiple top privacy computing companies in the industry were established in 2018 and have only been more than 3 years so far, it is worth noting that most of the top researchers in the industry are in privacy He has devoted himself to more than 10 years of research in the field of computing.
“The field of private computing can be said to be an industry that has emerged from the laboratory of university professors. ” Zhou Zhifeng believes.
The computing power think tank has noticed that, for example, Professor Wang Shuang of the federal learning pioneering team and the leading private computing company Kaiwei Technology has been developing privacy computing technology and its applications at the University of California, San Diego since 2011, and has hosted and participated in many times. The national-level healthcare big data privacy calculation project funded by the National Institutes of Health has been continuously researched for more than 10 years until this year.
Professor Wang Shuang was introduced by the state as the only “Thousand Talents Program” in the field of privacy computing in 2018, and was hired as a distinguished professor of West China Hospital of Sichuan University in 2019.
In the field of privacy computing, Wang Shuang, Yang Qiang, and Li Xiaolin were also called the “Three Musketeers of Federal Learning” by the industry media. Judging from the combing of computing power think tanks, searching on Google Scholar, the federal learning research results of three Chinese professors ranked quite high.
The technical barriers of the private computing industry are quite high, and the research on private computing needs to be in-depth. Judging from the academic achievements of the technical leaders or chief scientists of various private computing companies, the technical scope of private computing coverage, the start time of research, the number of research results, the influence of research results, and the continuity of research reflect the technical threshold of the industry.
Table 2: Academic statistics of experts in the privacy computing industry
(This table is mainly based on Google Scholar statistics, or there may be inaccuracies, please correct me)
1. This table is based on the technical scope of works published on Google Scholar, including:
Tongdun Technology-Professor Li Xiaolin: https://scholar.google.com/citations?user=e-px-18AAAAJ
Huakong Qingjiao-Professor Xu Wei: https://scholar.google.com/citations?user=6jN5vScAAAAJ&hl
WeBank-Professor Yang Qiang: https://scholar.google.com.sg/citations?user=1LxWZLQAAAAJ
Fengwei Technology-Professor Wang Shuang: https://scholar.google.com/citations?user=GnDGqKQAAAAJ
Matrix Element-Dr. Xiang Xie: https://scholar.google.com/citations?user=WWb0js4AAAAJ&hl
Wing Fang Jianshu-Dr. Zhang Lintao https://scholar.google.com/citations?hl=en&user=BSa0rkwAAAAJ
Nebula Clustar- Professor Kai Chen https://scholar.google.com/citations?hl=en&user=tnRV5QYAAAAJ
2. Calculated based on the publication time of the first privacy calculation-related paper by Google Scholar
3. According to the privacy calculation related works that have been published on Google Scholar
4. The data source for the influence of research results is the citations of related works calculated according to the privacy of Google Scholar as of June 17, 2021; h-index is also called h index or h factor (h-factor), which is a kind of evaluation A new approach to academic achievement. h stands for “high citations”. The h index of a researcher means that at most h papers have been cited at least h times. The h index can more accurately reflect a person’s academic achievements. The higher the h index of a person, the greater the influence of his paper. I10-index is proposed by Google and refers to the number of articles published by authors that have been cited more than 10 times.
In terms of types, the classification of privacy computing technologies mainly includes Federated Learning (Federated Learning), Multi-Party Secure Computing (sMPC), Homomorphic Encryption (HE), Trusted Computing Environment (TEE), Differential Privacy (DP), and Zero Knowledge Proof And other subdivision technology.
The computing power think tank has noticed that at present , most private computing companies use one technology as the main research method and the other technologies as auxiliary research methods. This is also the case from the research directions of the heads or chief scientists of the above-mentioned companies. In commercial applications, private computing companies are constantly updating and iterating along with the practice of combining technology with commercial landing.
With a large amount of technology investment in the early stage, at present, can companies in the privacy computing industry achieve profitability? It also makes many investors who wait and see in this field drum up.
According to the expectation of the technology team of Kaiwei, by the end of this year, it is expected to achieve 10 times the revenue growth.
Judging from the current revenue model of Winway Technology, it provides software and hardware integrated machines, cloud computing, software packages, and virtualized container deployment in accordance with the needs of users.
From several aspects such as hardware strength compliance, leading value concept, and commercial landing results in 2021, it can be seen that Fengwei Technology can achieve ten times the revenue.
At the beginning of its establishment, the founding team of Kaiwei Technology adhered to the “Rice Flower Theory” of sustainable development. In the era of strong data supervision, the field of privacy computing emphasizes “neutrality.” In the industry, some companies dig ponds to raise fish, buy fry, raise, slaughter, and sell fish. They are both data sources and data service providers, which are easy to guard and steal. And form a monopoly.
According to Professor Wang Shuang, the “Rice Flower Fish Theory” was proposed by Wanwei Technology in combination with its own practice. Simply put, it is to plant a paddy field first, and dig a fish pond in the middle. Food, thereby automatically attracting a steady stream of fish. The fish can also make fertilizer for the rice field, dredging mud and running water, and achieve a double harvest of “fish and rice”.
From the perspective of hardware strength, the original underlying technology modules of Fenwei Technology have independent property rights. After 10 years of R&D and practical experience, an independent, safe, and controllable new infrastructure platform for privacy computing can adapt to complex scenarios and complex data. Type, provide complex and precise solutions. The combination of the bottom-level construction and the upper-level demand side can provide a complete set of solutions that adapt to different business needs, and realize the new computing paradigm of data “availability invisible” and “data immovable value”.
The scenes and tracks that Fenwei Technology cuts into are precise, adhering to the long-term focus in the medical field, and the orderly layout based on the upstream and downstream of the medical field (medical insurance, insurance customer acquisition marketing, smart medical, etc.). Compared with the financial field, the medical field has higher complexity and threshold. Choosing the medical field will inevitably mean more hard-core technical confidence, investment and concentration.
“50% of our research energy and commercial business are in the combination of privacy computing and medical care. Finance and government affairs are areas that are more focused outside the medical field.” Professor Wang Shuang said frankly.
In addition, Fenwei Technology has ecological extension capabilities. For example, Fenwei Technology and the Chinese Medical College of the Chinese Medical Association have established a special disease network, which is used as a starting point to cover many hospitals and create data sources for the acquisition of ” running water”.
Fengwei Technology has completed the overall layout of key industries such as medical care and government affairs, occupying the first place in many industries. The company has served more than dozens of hospital customers, including a large number of national head hospitals and tertiary hospitals. It has rich and high-quality customer resources and has formed a good reputation in the industry. This is also the reason for the company to continue to expand its market share. Important support. Based on the 锘崴信® privacy computing platform, multiple medical institutions completed the world’s first transnational multi-center rare disease data sharing; realized the country’s first trans-provincial multi-center rheumatism whole genome analysis (iPRIVATES); carried out major and new breakthroughs Research on the key information technology of infectious diseases, and developed a real-time monitoring and early warning system for new emergent infectious diseases based on multi-dimensional big data to empower the public health emergency management system.
Professor Wang Shuang shared his team’s recent commercial landing story to Hash Power Think Tank.
Recently, a prospective multi-center cohort study of perioperative VTE prevention for colorectal malignant tumors in Chinese digestive surgery population with technical support provided by Fengwei Technology adopted the underlying technology of Fengwei Technology’s safe federal learning based on a similar iPRIVATES framework. A number of top three hospitals and multiple departments will register internal case data locally, and the data sources will simultaneously form a variety of relationships between the homogeneous horizontal division and the vertical division of the institution. Distributed calculation of the terminal nodes, the intermediate parameters are encrypted and then exchanged and transmitted through the central node, completing the joint training of the research model.
Venous thromboembolism (VTE) is one of the common complications after surgery and an important factor in the unexpected death of patients. It seriously affects the quality of life and survival status of patients and becomes the second death of patients with malignant tumors. The big reason. Studies have shown that malignant tumors themselves are also one of the high-risk factors for the occurrence of VTE. It will cause the patient to have abnormal blood coagulation mechanisms, secrete too much procoagulant substances, and destroy the balance of fibrin precipitation and degradation in the vascular system after surgery, so that the body has a high risk. Tendency to thrombosis. Especially in the perioperative period, the incidence of VTE is relatively high.
Therefore, how to reduce the incidence of VTE during the perioperative period of malignant tumors is a serious challenge facing thoracic surgeons in China. The use of statistics and AI methods can effectively study the related factors of VTE, including the two most high-risk factors during the perioperative period and the surgical period. Then summarize the reasons, standardize the procedures and methods of VTE prevention in patients with malignant tumors, in order to reduce the incidence of VTE in the perioperative period.
The study carried out a comprehensive correlation analysis of the final occurrence of VTE from various dimensions such as basic patient information, hospital admission symptom assessment, past medical history, and previous surgical history. Finally, a statistical regression model for predicting the occurrence of VTE is obtained. The observable variables in the above dimensions can be used to estimate the probability of VTE occurrence, so as to balance the risk of thromboembolism and the risk of hemorrhage after drug prevention, and make accurate judgments based on the actual situation. , To provide guidance for actual decision-making. At present, the research is expected to further expand into international cooperation and build a transnational multi-center medical joint research network.
Everything is just beginning
Ten years have passed, and another ten years have passed. The commercial market blew up by privacy computing has just begun.
The development of privacy computing technology has just begun.
Zhou Zhifeng and his team analyzed privacy calculations from the demand side. Medical, financial, and public security areas where data is sensitive, difficult to protect, difficult to share, and difficult to confirm are in huge demand. However, from the statistical point of view, the current enterprises or The third-party public data that individuals can use legally and compliantly account for less than 1% of the total data volume, including public papers, public government data and corporate data, Internet public data, and media data. A large amount of data is urgently needed to protect data privacy through technology Release for use without being leaked.
The enthusiasm for the private computing primary market has just begun.
Investment institutions have seen the explosive point of the industrialization of private computing, which is evident from the intensive financing of the private computing track this year. In the early stages of development, even though the business model has not yet been fully formed, the entire industry’s financing has already completed more than 2 billion yuan in just a few years. After the gradual development of the potential of the privacy computing market, commercial landing applications and the explosion of business models, the enthusiasm of the primary market will be even higher on this big technology track.
Of course, even if all backgrounds are mature, what preparations do privacy computing companies need to do to gain support from investors?
First, what market will PE/VC choose?
Any successful company or a business model is based on creating value for society and customers. This is the most fundamental core point. From the perspective of the privacy computing industry, the state encourages the circulation of data elements to promote the development of the national economy, confirms the right to use, ownership and management of data, and protects data privacy. The privacy computing technology that “data is available and invisible” becomes the only technology. untie. In the short term, according to the technology maturity curve of privacy computing released by Gartner in 2021, from the perspective of personal privacy data, the personal data of approximately 75% of the world’s population will be regulated by modern privacy laws by 2023, but currently only 25% %, in the next two years, 50% of the population will have a need for privacy protection; from a company perspective, in the context of the rapid development of big data, at least 80% of companies will face the use of private data by 2023, and privacy computing The industry has a huge market and will naturally attract the attention of PE/VC.
Second, why did you choose you among the many companies in this market?
From the perspective of Winway Technology, more than 10 years of solid privacy computing technology precipitation, for example, more than 300 related papers have been published in the field of privacy computing, and hundreds of millions of yuan of grants have been obtained from the Natural Science Foundation; in 2012, it was first proposed on a global scale. The concept of “Secure Federal Learning” has been applied many times in the practice of national and provincial biomedical computing networks.
Third, can your business model be profitable?
Whether the business model of private computing technology can be recognized by PE/VC and whether it can establish its own technical barriers is critical to whether PE/VC can be convinced to win financing. Similarly, taking Fengwei Technology as an example, Fengweixin® continues to promote digital strategies as the bottom core of privacy computing. In addition to continuing to expand its leading advantages in key industries such as medical care and government affairs, it will continue to occupy the leading position in other market segments. 锘崴Technology has empowered other industries such as AI through the original core of private computing, enhancing the industry’s potential for computing, data, and computing power.
The development of private computing companies has just begun.
“Currently, the leading private computing companies, through the accumulation of early technical strength, are expected to develop into a platform company once they pass the commercial tipping point. ” Zhou Zhifeng is confident in the private computing industry.
The exploration of private computing business models has just begun.
The business model of private computing is still being explored. Hashrate Think Tank understands that there are usually three types. One is to build a private computing technology infrastructure to provide revenue from exclusive services through the establishment of a private computing platform. The other is to provide SaaS services, mainly for small and medium customer groups, such as multi-centered Privacy calculation protection needs or cross-domain analysis; third is revenue sharing. For example, in marketing, for every successful marketing order, we will get a certain percentage of profit, similar to the CPS model; of course there are more models It is still in the exploratory period, and under the condition that the market gradually matures, the business model will also keep pace with the times. “
On the whole, the transition of privacy computing from scientific research to industry is successful. 2020 is the first year of private computing. In 2021, the commercial implementation of private computing will make intensive breakthroughs. 2022 will usher in the first year of the explosion of the private computing industry.
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