Popular explanation of privacy computing (multi-party secure computing)

Privacy computing is a technology in which multiple parties participate in the calculation. The essence is that without revealing the relevant specific information, it can collaboratively calculate the desired result.

For individuals, institutions, and governments, privacy computing is very important. There are more privacy calculations here, and another sentence can be used instead of “multi-party secure computing”.

Getting started with multi-party secure computing

The most famous (and world-renowned) researcher of multi-party secure computing in China is Yao Qizhi. Yao Qizhi is the founder of Tsinghua Yaoban, Turing Award winner, academician and so on.

“A hot issue that I attach great importance to is the issue of privacy protection. I proposed multi-party secure computing more than 30 years ago. Both of us have one data. We want to combine the two data, but do not want to transfer the data. To the other party. We hope to make this calculation complete, but we do not disclose what our data is. Sharing data to protect privacy has now become an important area of ​​password security. Multi-party secure computing is now a very popular key technology and will be used in financial technology. , Artificial intelligence, medical protection, data sharing, etc. are all key technologies. Now the key has become how countries can implement the technology. I hope that China also has its own original technology.”

Yao Qizhi

“The Multi-Party Secure Computing (MPC: Secure Muti-Party Computation) theory is a theoretical framework proposed by Mr. Yao Qizhi to solve the problem of collaborative computing between a group of untrusted parties under the premise of protecting private information and without a trusted third party. Multi-party secure computing can simultaneously ensure the privacy of inputs and the correctness of calculations, and use mathematical theories to ensure that the input information of all parties involved in the calculation is not exposed under the premise of no trusted third party, and at the same time, accurate calculation results can be obtained. “

Yao Qizhi

The above-mentioned multi-party secure computing-related content has mature content on the Internet, which can be obtained by searching for the keyword “Yao Qizhi Multi-party Secure Computing”.

Popular example 1

In 1982, Mr. Yao Qizhi raised the famous Yao millionaire problem in the “Protocols for Secure Computation”.

The so-called “Yao’s millionaire problem”, the popular explanation is that Zhang San and Li Si are rich, but the property is not disclosed, and do not trust a third party, the two people want to not disclose the specific amount of property In the case of, who is more wealthy than it comes out, privacy computing (multi-party secure computing) is used at this time.

On the basis of this basic problem, Academician Yao has developed a general technical framework that can solve the “millionaire problem.”

Popular example 2

Artificial intelligence is developing rapidly. There are three types of companies. The first type has data but no algorithms. The second type has algorithms but no data. The third type has algorithms and data. Many of these companies are giant companies such as Google and Byte. Beating, Alibaba, etc.

For organizations, algorithms need data to evolve and mature. For data companies, algorithms are needed to add value to data intelligently. The combination of the two is of course very good. However, if the data is given to the algorithm company, the data can be copied and stored. It was originally intended to make more money. As a result, the data company was collected as soon as the cooperation, or at a very low price. What is needed at this time is to optimize the algorithm without exposing the data, so that the data company gains benefits while protecting its own data assets, and the algorithm of the algorithm company can also be optimized-“multi-party secure computing” private computing That’s it.

However, this kind of cooperation is more in the academic research concept. In the current real world, data companies basically dominate. If there is data, there are means of production. You can imagine a country with abundant oil resources can’t do it. Refineries and refining technology?

Basically, the algorithm company directly “gives” the algorithm to the data company, and the data company conducts algorithm training.

Popular example 3

Zhang San opened an account with ICBC. He has never used the Bank of China in his life. Now he is going to the Bank of China to apply for a loan. How can he prove that his credit is good and that he can borrow? ICBC does not give you the data of the Bank of China, nor does it give you a flow sheet. It does not want to disclose its own data, but also has to prove that its credit is good, so there is a scenario of private computing.

The current solution is to check the “credit investigation”-there is no ridicule here, just because the credit investigation system has been done very well from top to bottom, what if it is not complete? What if there is no good credit reporting system? What if in the blockchain world or in another scenario? At that time, privacy computing will become very important.

The current value of private computing is more embodied in academic value and scientific research, but it does not mean that private computing will not have much use in the future. It just means that there is currently no efficient general standard to use privacy computing.

In the field of blockchain, privacy computing has its special significance. In the next article, we will focus on explaining which projects in the blockchain industry are on the track of privacy computing.

This article is just a popular explanation of privacy computing, and does not involve blockchain projects. At least recently, many project parties have been doing private computing, and everyone seems to be confused about the concepts of private computing, distributed computing, and anonymity.

Of course, readers are also recommended to search for keywords for learning and research, organized as follows: “Yao Qizhi”, “Multi-party secure computing”, “Homomorphic multi-party secure computing”, “Huakong Clearing.”

 

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