区块链安全多方计算方法,区块链安全多方计算

『一』 Modern cryptography in blockchain

1983 - Blind signature described by David Chaum
1997 - HashCash (proof of work) invented by Adam Back An example of a system)
2001 - Ron Rivest, Adi Shamir and Yael Tauman proposed ring signatures to the crypto community
2004 - Patrick P. Tsang and Victor K. proposed using a ring signature system for voting and Electronic Cash;
2008 - Bitcoin White Paper published by Satoshi Nakamoto
2011 - Analysis of Anonymity in the Bitcoin System, Fergal Reid and Martin Harrigan
2012 - Destination Address Bitcoin Anonymity (CryptoNote one-time address in ).

Secure multi-party computation originated from Yao Qizhi’s millionaire problem in 1982. Later Oded Goldreich had a more detailed and systematic discussion.

The Yao Millionaire problem was first raised by Professor Yao Qizhi, a Chinese computer scientist and Turing Award winner. The problem is formulated as follows: Two millionaires, Alice and Bob, want to know which of them is richer, but neither of them wants the other to know any information about their wealth. This problem has some practical applications: Suppose Alice wants to buy some goods from Bob, but the maximum amount she is willing to pay is x yuan; the minimum price Bob wants to sell for is y yuan. Both Alice and Bob really want to know which is bigger, x or y. If x>y, they can all start bargaining; if z
This scheme is used to compare two numbers to determine which one is larger. Alice knows an integer i; Bob knows an integer j. Alice and B0b want to know whether i>=j or j>i, but neither wants the other party to know their own numbers. For simplicity, assume that the range of j and i is [1, 100]. Bob has a public key Eb and a private key Db.

The research on Secure Multi-Party Computation is mainly aimed at the problem of how to safely calculate an agreed function without a trusted third party. Secure multi-party computation is used in electronic elections, electronic It plays an important role in scenarios such as voting, electronic auctions, secret sharing, and threshold signatures.

Homomorphic Encryption is an Open Problem proposed by the cryptography community a long time ago. As early as 1978, Ron Rivest, Leonard Adleman, and Michael L. Dertouzos proposed this concept in the context of banking [RAD78]. Yes, you read that right, Ron Rivest and Leonard Adleman are R and A respectively in the famous RSA algorithm.

What is homomorphic encryption? Craig Gentry, who proposed the first construction of Fully Homomorphic Encryption [Gen09], gave the best intuitive definition: A way to delegate processing of your data, without giving away access to it.
< br /> What does this mean? General encryption schemes focus on data storage security. That is, I want to send an encrypted thing to other people, or I want to store something on a computer or other server. I want to encrypt the data before sending or storing it. Without the key, it is impossible for the user to obtain any information about the original data from the encrypted result. Only users with the key can correctly decrypt and obtain the original content. We noticed that during this process, users cannot do any operations on the encryption results, they can only store and transmit them. Any operation on the encryption result will result in incorrect decryption or even decryption failure.

The most interesting thing about the homomorphic encryption scheme is that it focuses on data processing security. Homomorphic encryption provides a function for processing encrypted data. That is, others can process the encrypted data, but the processing will not reveal any of the original content. At the same time, the user who has the key decrypts the processed data and gets exactly the processed result.

A bit abstract? Let’s take a real-life example. A user named Alice bought a large piece of gold, and she wanted workers to make it into a necklace. But workers may steal gold during the building process. After all, even one gram of gold is worth a lot of money... So is there a way for workers to process the gold nuggets (delegate processing of your data), but Not getting any gold (without giving away access to it)? Of course there is a way, Alice can do this: Alice locks the gold in a sealed box, and this box is equipped with a glove. Workers can wear these gloves and inspect the boxesThe gold inside is treated. But the box was locked, so not only could the workers not get to the gold nuggets, but they also couldn't get to any gold that fell out during processing. After processing is completed. Alice took the box back, opened the lock, and got the gold.

The correspondence here is: Box: Encryption algorithm Lock on the box: User key Put the gold nugget in the box and lock it with a lock: Encrypt the data using a homomorphic encryption scheme : Apply the homomorphic feature to directly process the encryption result under the condition that the data cannot be obtained. Unlock: Decrypt the result and directly obtain the processed result. Where can homomorphic encryption be used? Hasn’t the concept of cloud computing been mentioned in recent years? Homomorphic encryption is almost tailor-made for cloud computing! Let's consider the following scenario: a user wants to process a piece of data, but his computer's computing power is weak. This user can use the concept of cloud computing and let the cloud help him process and get the results. But if the data is handed over directly to the cloud, security cannot be guaranteed! Therefore, he can use homomorphic encryption, and then let the cloud process the encrypted data directly and return the processing results to him. In this way: the user pays the cloud service provider and gets the processing results; the cloud service provider earns the fee and correctly processes the data without knowing the user's data;

Aggregated signature by Boneh et al. proposed to improve the efficiency of signature and verification mainly by aggregating multiple signatures into one signature. To sign data of multiple users, aggregate signatures can greatly reduce the complexity of signature calculations. CL is the aggregate signature.

There are two participants in the zero-knowledge proof process, one is called the prover and the other is called the verifier. The prover holds a secret, and he wants the verifier to believe that he holds the secret, but he does not want to reveal the secret to the verifier.

Both parties follow an agreement and through a series of interactions, the verifier will eventually come to a clear conclusion that the prover does or does not know the secret.

For the example of Bitcoin, whether a transfer transaction is legal or not actually only needs to prove three things:

The money sent belongs to the person who sent the transaction
Send The amount sent by the sender is equal to the amount received by the receiver
The sender's money was indeed destroyed
During the entire proof process, the miners do not actually care about the specific amount of money spent, who the sender is, and the recipient Who exactly is it? Miners only care about whether the system's money is conserved.

zcash uses this idea to implement private transactions.

The three properties of zero-knowledge proof correspond to:

(1) Completeness. If both the prover and the verifier are honest, follow every step of the proof process, and perform correct calculations, then the proof must be successful. VerificationThe proving party must be able to accept the proving party.
(2) Rationality. No one can impersonate the prover and make this proof successful.
(3) Zero knowledge. After the proof process is completed, the verifier only obtains the information that "the prover possesses this knowledge", but does not obtain any information about the knowledge itself.

There are only ring members, no managers, and no cooperation between ring members is required. The signer can sign independently by using his own private key and the public keys of other members in the set, without the need for other members. With the help of people, other members of the set may not be aware that they are included.
Ring signatures can be used as a way to reveal secrets, for example, a ring signature can be used to provide an anonymous signature from a "senior White House official" without revealing which official signed the message. Ring signatures are suitable for this application because the anonymity of a ring signature cannot be revoked and because the group used for the ring signature can be created on the fly.

1) Key generation. Generate a key pair (public key PKi, private key SKi)
2) signature for each member in the ring. The signer uses his own private key and the public keys of any n ring members to generate a signature a
for message m
3) Signature verification. The signer verifies whether the signature is signed by a member of the ring based on the ring signature and message m. If valid, accept it; if invalid, discard it.

General process of group signature

Blind digital signature (Blind Signature), referred to as blind signature - is a digital signature method. Before the message content is signed, the signature The content of the message is invisible to the user. In 1982, David Chaum first proposed the concept of blind signature. Because of its blindness, blind signature can effectively protect the specific content of the signed message, so it is widely used in fields such as e-commerce and electronic elections.

Analogy example: To sign a document is to put a piece of carbon paper in the envelope. When the signer signs the envelope, his signature is signed on the document through the carbon paper.

The so-called blind signature is to first put the concealed document into an envelope, and the process of removing the blind factor is to open the envelope. When the document is in an envelope, no one can read it. Signing a document is done by placing a piece of carbon paper in the envelope. When the signer signs the envelope, his or her signature is transferred to the document through the carbon paper.

Generally speaking, a good blind signature should have the following properties:

Unforgeable. No one can generate a valid blind signature in his name except the signer himself. This is the most basic property.
Non-repudiation. Once a signer signs a message, he cannot deny his signature on the message.
Blindness. Although the signer signs a message, he cannot obtain the specific content of the message.
Untraceability. Once the signature of a message is made public, the signer cannot be sure when he or she signed the message.
Blind signatures that meet the above properties are considered safe. These four properties are not only the standards we should follow when designing blind signatures, but also the basis for us to judge the performance of blind signatures.

In addition, the operability and implementation efficiency of the solution are also important factors that we must consider when designing blind signatures

. The operability and implementation speed of a blind signature depend on the following aspects:

1. The length of the key;
2. The length of the blind signature;
3. Blind Signature algorithm and verification algorithm.
Specific steps of blind signature
1. The recipient first blindly transforms the data to be signed, and sends the transformed blind data to the signer.
2. After being signed by the signer, it will be sent to the recipient.
3. The receiver performs a blinding transformation on the signature, and the result is the signer's blind signature of the original data.
4. This meets the condition ①. To satisfy condition ②, the signer must not be able to associate the blind signature with the blind data when he sees it afterwards. This is usually achieved by relying on some kind of protocol.

『二』 Insight Technology: Privacy computing protects data security and allows data elements to flow intelligently

Data view | Liu Zhenhang

Privacy computing is to ensure the data provider A series of information technologies that analyze and calculate data without leaking the original data, ensuring the "availability and invisibility" of data in the process of circulation and integration, realizing the transformation and release of data value, in order to cope with personal privacy protection and data security. An innovative solution based on technical trust is proposed to solve key problems in data circulation such as hidden dangers and data islands. Privacy computing has become one of the important ways to balance data use and security, and has also become the "security officer" in the digital economy era.

In recent years, our country has successively promulgated laws and regulations such as the "Cybersecurity Law", "Data Security Law", and "Personal Information Protection Law", and issued a number of policies and measures to promote the circulation of privacy computing technology and data elements. It promotes the activity of privacy computing track and also points out the direction for the development of privacy computing industry.

Technology integration into scenarios enables intelligent circulation of data

It is understood that at the 2022 Data Expo, Insight Technology’s “Provincial Government Data Open Platform Based on Privacy Computing” case won two awards The first is to win the "Most Excellent Award" for the series of research results on the development of the big data industry at the 2022 Digital Expo.The second is to become one of the first batch of "Data Security Industry Achievements-Top Ten Typical Application Cases" of the China Software Evaluation Center in 2022, which will be exhibited on the data security industry public service platform.

Privacy The most mainstream technologies in the computing field are secure multi-party computing, federated learning and trusted execution environments. In terms of core technologies, Insight Technology proactively builds integrated computing engines based on the accumulation of scene cognition, and conducts in-depth research on secure multi-party computing and federated learning technologies. , independently developed the financial-grade privacy computing platform InsightOne, which integrates the two technical routes in actual application scenarios, and uses blockchain technology to enhance credit privacy computing, enabling data to be encrypted to achieve cross-institutional data joint analysis and Joint modeling enables "data available but invisible" scenario applications in different industries.

In addition, Insight Technology has also developed untrusted third-party federated learning, fast federated learning, and multi-party security Graph computing, graph federated learning, interconnection algorithm containers and other technologies can overcome trust risks and performance issues in joint modeling, expand cross-platform privacy computing interconnection capabilities, and achieve "trusted and linkable computing".

Expand service scenarios to promote interconnection between privacy computing platforms

Expand service areas from government affairs and finance to more fields and provide services for more scenarios. In this process, privacy computing platforms Interconnection will be a crucial part.

In June 2021, Insight Technology, Ant Group, and Nuwei Technology achieved fully peer-to-peer algorithm protocol interoperability between multi-party heterogeneous privacy computing platforms , is the industry's first three-party interconnection practice, and has currently reached interconnection cooperation with dozens of privacy computing companies in the industry. Peng Yuxiang believes that as important as technical practice in achieving cross-platform interconnection, there is also technical standardization work. In the standard work, Insight Technology takes the lead in the IEEE's international standards for privacy computing interconnection, and actively participates in the formulation of interconnection standards organized by the China Academy of Information and Communications Technology, the Information Security Standards Committee, the Beijing Financial Technology Industry Alliance of the Central Bank and other institutions.

“Insight Technology’s strategic focus in 2021 is distribution, and the focus in 2022 is connection. "Peng Yuxiang introduced that the upstream nodes in the privacy computing industry are data suppliers, and the downstream nodes are data applications. At present, Insight Technology has a complete ecological layout in the upstream and downstream, and the connection is to cross-platform and cross-platform the data of upstream and downstream enterprise organizations. Domain link, intelligent circulation network of operational data. In addition to the compliance data resources of China Chengxin Ecosystem and credit rating, Insight Technology works with relevant government departments, payment and clearing institutions, communication operators and Well-known Internet companies have reached cooperation and have settled in Beijing, Shanghai, Shenzhen, West China (Chongqing), East China (Jiangsu), Shandong, Hainan, Zhejiang, Yangtze River Delta, Guiyang, Hefei and other digital exchanges as contracted digital merchants.

Based on the extensive deployment of self-developed privacy computing platform nodes in the fields of government affairs, finance, operators and other fields in the early stage, Insight Technology's data intelligent network on the data side and scenario side has gradually taken shape. Regarding future development, Peng Yuxiang said that Insight Technology will be committed to becoming the data intelligent network infrastructure operation service provider that "knows the best about data and finance". As an independent third party, it will connect the data of upstream and downstream enterprises and institutions through "Left + The "data, right + scenario" model provides data intelligent products and operates a data intelligent network, allowing data elements to flow safely, data value to be fully released, and business results to be effectively improved.

Introduction to the interviewed guests:

Peng Yuxiang, general manager of East China Region of Insight Technology, is a master’s student at Zhejiang University. He was responsible for the product design of the privacy computing platform, led the implementation of multiple privacy computing projects, and participated in the formulation of many privacy computing technology standards such as federated learning and secure multi-party computing. He served as the AI ​​algorithm tech leader of Zhongan Technology and was responsible for AI algorithm research in insurance technology. Leading the design of eKYC, intelligent claims, RPA and other application systems, and has profound accumulation of AI algorithms, machine learning, privacy computing and other technologies.

『三』How is Qulian Technology doing in privacy computing?

The latest news is that Qulian Technology, as one of the representatives of blockchain companies, was selected by Privacy Computing The "Privacy Computing Industry Map 1.0" jointly compiled by the alliance and the Big Data Technology Standards Promotion Committee of the China Standardization Association.

QuLian Technology independently developed the blockchain privacy computing platform BitXMesh, which is the first data sharing platform that combines blockchain with secure multi-party computing technology and supports on-chain and off-chain collaboration to meet privacy requirements. Data value transfer requirements under protection requirements break data silos and make data "available, invisible, controllable and measurable". From 2020 to 2022, BitXMesh has passed the "Special Evaluation of Basic Capabilities of Multi-party Secure Computing", "Special Evaluation of Basic Capabilities of Blockchain-Assisted Privacy Computing Tools", "Special Evaluation of Multi-party Secure Computing Performance" and "Basic Capabilities of Federated Learning" by the Ministry of Industry and Information Technology. Special Evaluation", and the performance test evaluation results exceed the industry average by more than 10 times, making it a trusted infrastructure for building a distributed data element market.
Speed ​​up to find out more about it. Thanks for the invitation.

『四』Blockchain core technology research goals

Key breakthroughs include security privacy protection, open cross-chain protocols, efficient on-chain and off-chain collaboration, and secure smart contract mechanisms. Chain application support technology.

1. Security and privacy protection technology. The focus is on achieving technological breakthroughs in secure multi-party computation, zero-knowledge proof, secure transmission, and homomorphic encryption.

2. Chain-to-chain interconnection technology. The focus is on cross-chain protocols, homogeneous/heterogeneous cross-chain architecture andBreakthroughs in security, scalability, and performance.

3. On-chain and off-chain collaboration technology. The focus is on breakthroughs in technologies such as on-chain and off-chain data collaborative access control, efficient storage and management.

4. Secure smart contract technology. The focus is on breakthroughs in smart contract formal verification, security vulnerability risk assessment, and smart contract auditing.

5. Blockchain supervision technology. Focus on achieving technological breakthroughs and applications in blockchain penetrating supervision technology, dynamic monitoring technology, blockchain risk isolation and control, etc.

                                                                                                                                                                                                          ”

Privacy protection methods can be divided into three categories:
First, privacy protection of transaction information, privacy protection of transaction senders, transaction recipients and transaction amounts, including currency mixing, ring signatures and confidential transactions, etc. .
The second is privacy protection for smart contracts, and protection solutions for contract data, including zero-knowledge proof, multi-party secure calculation, homomorphic encryption, etc.
The third is the privacy protection of data on the chain, which mainly includes solutions such as ledger isolation, private data and data encryption authorized access.
Extended information:
1. Blockchain encryption algorithm isolates identity information and transaction data
1. Transaction data on the blockchain, including transaction address, amount, transaction time, etc., are open, transparent and queryable . However, the identity of the user corresponding to the transaction address is anonymous. Through the blockchain encryption algorithm, the separation of user identity and user transaction data is achieved. Before the data is saved on the blockchain, the user's identity information can be hashed, and the resulting hash value is used as the user's unique identifier. The user's hash value is stored on the chain instead of the real identity data information. Transaction data is bundled with hash values ​​rather than user identity information.
2. Therefore, the data generated by users is real. When using these data for research and analysis, due to the irreversibility of the blockchain, no one can restore the name, phone number, and email address of registered users through hash values. and other private data, playing a role in protecting privacy.
2. Blockchain "Encrypted Storage + Distributed Storage"
Encrypted storage means that access to data must provide a private key. Compared with ordinary passwords, private keys areThe key is more secure and almost impossible to be cracked by brute force. Distributed storage and decentralized features reduce the risk of all data being leaked to a certain extent. However, with centralized database storage, once the database is attacked by hackers, all data can easily be stolen. Through "encrypted storage + distributed storage", users' data privacy can be better protected.
3. Blockchain consensus mechanism prevents individual risks
The consensus mechanism is a mechanism for blockchain nodes to reach a consensus on block information across the entire network, which can ensure that the latest blocks are accurately added to the blockchain and node storage The blockchain information is consistent and unforked, which can resist malicious attacks. One of the values ​​of the blockchain lies in the consensus governance of data, that is, all users have equal management rights to the data on the chain. Therefore, the risk of individual mistakes is first eliminated from the operation. Data decentralization is solved through the network-wide consensus of the blockchain, and zero-knowledge proofs can be used to solve verification problems and realize the scenario of using user privacy data in a public decentralized system. While meeting the needs of the Internet platform, it also So that part of the data is still only in the hands of the user.
4. Blockchain Zero-Knowledge Proof
Zero-knowledge proof means that the prover can make the verifier believe that a certain assertion is correct without providing any useful information to the verifier, that is, proof The investor can fully prove that he is the legal owner of certain rights and interests without leaking relevant information, that is, the "knowledge" given to the outside world is "zero". By applying zero-knowledge proof technology, data correlation verification can be achieved in the context of ciphertext, and data sharing can be achieved while ensuring data privacy.

『Lu』 What is the relationship between blockchain and big data storage

The relationship between blockchain and big data storage is as follows:
1. Data security: Zone Blockchain allows data to truly flow "with confidence"
With its trustworthiness, security and non-tamperability, blockchain allows more data to be liberated. Let’s use a typical case to illustrate how blockchain promotes the generation of gene sequencing big data. Blockchain sequencing can use private keys to limit access rights, thereby circumventing legal restrictions on individuals' access to genetic data, and utilize distributed computing resources to complete sequencing services at low cost. The security of blockchain makes sequencing an industrialized solution, enabling global-scale sequencing, thus promoting the massive growth of data.
2. Data openness and sharing: Blockchain ensures data privacy
The government holds a large amount of high-density and high-value data, such as medical data, population data, etc. The openness of government data is a general trend and will have an immeasurable impetus to the development of the entire economy and society. However, the main difficulty and challenge in data openness is how to open data while protecting personal privacy. Blockchain-based data desensitization technology can ensure data privacy and provides a solution for data openness under privacy protection. Data desensitization technology mainly uses encryption algorithms such as hashing. For example, the Enigma system based on blockchain technology does not require access to the originalComputing data under the condition of data can protect the privacy of data and eliminate information security issues in data sharing. For example, company employees can safely open access to their salary information and jointly calculate the average salary within the group. Each participant is informed of his relative position within the group but not of the compensation of other members.
Data HASH desensitization processing diagram
3. Data storage: Blockchain is an immutable, full-historical, strongly endorsed database storage technology
Blockchain technology, through the network All nodes in the network participate in calculations and verify the authenticity of their information to reach a consensus across the entire network. It can be said that blockchain technology is a specific database technology. So far, our big data is still in a very basic stage. Trusted blockchain data based on the consensus of the entire network is non-tamperable and full of history, which also enables the quality of the data to gain unprecedented strong trust endorsement. It also brings the development of database into a new era.
4. Data analysis: Blockchain ensures data security
Data analysis is the core of realizing data value. When conducting data analysis, how to effectively protect personal privacy and prevent core data leakage has become the primary consideration. For example, with the popularization of fingerprint data analysis applications and genetic data detection and analysis methods, more and more people are worried that once personal health data is leaked, it may lead to serious consequences. Blockchain technology can prevent such situations through multi-signature private keys, encryption technology, and secure multi-party computing technology. When the data is hashed and placed on the blockchain, digital signature technology is used to allow only authorized people to access the data. The private key ensures data privacy and can be shared with authorized research institutions. The data is uniformly stored on the decentralized blockchain, and data analysis can be performed without accessing the original data. This can not only protect the privacy of the data, but also safely provide it to global scientific research institutions and doctors for sharing. The basic health database will bring great convenience to solve sudden and difficult diseases in the future.
5. Data circulation: Blockchain protects data-related rights and interests
For individuals or institutions’ valuable data assets, blockchain can be used to register them, and transaction records are recognized and transparent by the entire network. , traceable, clarifying the source, ownership, use rights and circulation path of big data assets, which is of great value to data asset transactions.
On the one hand, blockchain can eliminate the threat of intermediaries copying data and help establish a trustworthy data asset trading environment. Data is a very special commodity, which is essentially different from ordinary commodities. It mainly has characteristics such as unclear ownership and "it is owned after seeing it and copying it". This also determines that the transaction method using traditional commodity intermediaries cannot meet the needs of data. Share, exchange and trade. Because the intermediary center has the conditions and ability to copy and save all the data flowing through it, this is extremely unfair to the data producers. This threat cannot be eliminated by relying on promises alone, and the existence of this threatIt has also become a huge obstacle to the flow of data. Based on decentralized blockchain, it can eliminate the threat of data copying by intermediary centers and protect the legitimate rights and interests of data owners.
On the other hand, blockchain provides a traceable path and can effectively solve the problem of data verification. The blockchain jointly participates in the calculation and recording of data through multiple nodes participating in calculations in the network, and mutually verifies the validity of their information. It can not only prevent information counterfeiting, but also provide a traceable path. By stringing together the transaction information of each block, a complete list of transaction details is formed. The ins and outs of each transaction are very clear and transparent. In addition, when people have doubts about the "value" of a certain block, they can easily review historical transaction records to determine whether the value is correct, and identify whether the value has been tampered with or recorded incorrectly.
Everything is guaranteed on the blockchain, and big data will naturally become more active.
The tokens of crowdfunding projects on the Biying China platform are all developed based on blockchain technology, and relevant information will be recorded on the blockchain.

『撒』 What are the protection mechanisms of the blockchain ledger under the blockchain computing mode?

Consensus confirmation, multi-party storage, safe and trustworthy, and cannot be tampered with. The blockchain computing model uses the blockchain data structure to verify and store data, and uses the distributed node consensus algorithm to generate and update data. The guarantee mechanism of the blockchain account includes consensus confirmation, multi-party storage, security and reliability. Trustworthy, cannot be tampered with, and guarantees that the mechanism is a mechanism that provides material and spiritual conditions for management activities. It is divided according to functions. According to the function of the mechanism, it is divided into incentive mechanism, restriction mechanism and guarantee mechanism.

『8』What is the current development prospect of blockchain technology?

The research strength has increased and the research results have been remarkable

As of the end of 2019, my country’s blockchain technology The number of research institutions has reached 97. In addition, domestic universities have deployed blockchain technology research, strengthened the innovation of blockchain technology theoretical knowledge, and improved the blockchain technology research and development capabilities of universities. By the end of 2019, there were 24 universities in my country participating in strengthening blockchain technology research and development. The core technologies are mainly distributed in the aspects of consensus algorithm, cross-chain, underlying architecture and multi-chain.

Judging from the research results, in terms of consensus mechanism, my country’s consensus mechanism is gradually moving from a single algorithm to a hybrid consensus; in terms of cryptographic algorithms, cryptographic algorithms such as secure multi-party computation, homomorphic encryption, and zero-knowledge proof are continuously integrated and applied. ; Research on cross-chain technology and security technology has gradually become a research hotspot.

From the perspective of blockchain hardware, currently, the world’s most important digital currency mining equipment suppliers are all located in China, including Bitmain, Canaan, and Yibang Technology. Three mining equipment companies account for more than 90% of the market share.

From the perspective of industry applications, the financial industry is currently the industry with the most blockchain technology implementation projects and the richest scenarios, such as supply chain finance, asset certificate aromatization, credit reporting and risk control, etc.

2019China accounts for 60% of the world's patent applications

With the inclination of national policies towards blockchain and the implementation of applications in various fields, blockchain-related patents have gradually attracted the attention of all parties. Compared with 2018, the number of blockchain-related patent applications from Chinese enterprises has increased significantly in 2019. Among the top 100 global companies on the list, my country accounts for 63%.

The blockchain industry will be further standardized in 2020

In 2020, my country’s blockchain policies will continue to be favorable, standards and regulations will be more perfect, industry scale will continue to grow, and technology will continue to innovate and develop. , the application demonstration effect in key areas is accelerating. At the same time, based on the problems existing in the development of my country's blockchain in 2019, CCID Blockchain Research Institute proposed to speed up top-level design and formulation, establish a sound regulatory system, accelerate core technology innovation and research and development, promote third-party evaluation and certification, strengthen professional talent training, and accelerate promotion Six suggestions for implementation in various fields.

『九』 ARPA, the setter of privacy computing standards, why is it favored by Binance?

Felix Xu, co-founder & CEO of ARPA, holds a double degree in information technology and finance from New York University. With six years of experience in investment and entrepreneurship, he worked at Fosun Ruizheng Capital, an early-stage fund under Fosun Group, covering financial technology, AI big data and other industries, and was independently responsible for research and early investment in the blockchain field. He has rich industry resources and Extensive contacts in the technology and investment fields, and experience in managing operational projects. Previously worked in New York Sackler Family Office, Vertical Research Partners and other institutions. ARPA also participated in the national standards for secure multi-party computing led by the China Academy of Information and Communications Technology.

I think privacy computing will become very rigid in the future. Here’s why. First, with the tightening of regulations, domestic and foreign financial data have been unable to be collected and traded, leading to increased demand for privacy computing from financial institutions. Second, countries define data as assets and will be protected. Finally, people are becoming more aware of data privacy and realize that “free” is the most expensive.