Take PlatON as an example, to talk about the current state and future development of blockchain and privacy protection technology

This article is reproduced from ChainNews | Wu Speaking blockchain real

Author: Zhuoyue Wu

Take PlatON as an example, to talk about the current state and future development of blockchain and privacy protection technology

Since 2014, with the rise of the concept of blockchain privacy and the emergence of many anonymous currencies, the popular track of cryptocurrency can be described as a farce. Till today, there is no project that can really implement privacy protection technology, but in fact, privacy protection should never be confused with many popular hype racetracks. If blockchain wants to develop into a virtual parallel world, it is necessary to create a reasonable economic system in the new world.

In the traditional economic system, the only factors of production are land and labor; the industrial era has increased capital and entrepreneurial talents (Marshall theory); and in the digital era, data has become an important factor of production. The market-based distribution of production factors can improve production efficiency, but the particularity of data “what you see is what you get” makes it not like other factors have prices. Each of us is the owner and supplier of data, just as everyone provides labor, but we are not paid for providing data. The root lies in the fact that data has not yet been privatized, which is the meaning of privacy protection technology.

Several techniques of privacy protection

Based on the “Privacy Protection Computing Technology Research Report (2020)” released by the China Communications Standards Association Big Data Technology Standard Promotion Committee in 2020, the China Academy of Information and Communications Technology divides privacy protection technologies into five types: federated learning, differential privacy, and secure multi-party computing , Homomorphic encryption, trusted execution environment. Among them, federated learning and differential privacy are mainly used in the field of machine learning, and the encryption complexity of the original data is general, which is beyond the scope of discussion. In addition, there is a privacy protection technology based on zero-knowledge proof in the blockchain.

Secure Multi-Party Computing (MPC) was first proposed in 1982 by the winner of the Turing Award, Yao Qizhi, an academician of the Chinese Academy of Sciences. The technical logic is: in a distributed network, there are N nodes that do not trust each other, and each node holds data x, and executes the function f(x) cooperatively, and finally obtains the respective result y, if the y of each node If the values are equal, it can be output as the result of the calculation. The biggest advantage of MPC is that it can achieve 100% privacy protection of data, and the calculation results can be relatively accurate; the challenge is that it requires extremely high bandwidth, and the communication level will be a big test when there are many collaborative participants. . At present, a single operation of secure computing can reach the millisecond level, but in the big data scenario, a data application or model training involves tens of thousands of data samples, and computing efficiency and communication burden are the bottlenecks hindering the development of MPC.

Homomorphic encryption (HE) is an asymmetric encryption algorithm. All participants can encrypt and calculate data, but only the holder of the private key can decrypt the data. The special feature of HE is that it allows direct calculation on the encrypted data. The theoretical calculation result is consistent with the decrypted calculation result. It is conceivable that the calculation results under the HE algorithm are difficult to achieve extremely high accuracy. How to weigh the encryption complexity and the calculation accuracy will be a big test. Fully homomorphic encryption is still based on the theoretical stage, and it is relatively backward in terms of credibility, flexibility, and efficiency. In actual use, the efficiency is too low, and the construction method and implementation technology are complex, and large-scale commercial application is not yet possible.

Trusted Execution Environment (TEE) is currently the most widely used technology for large-scale commercial use, such as fingerprint unlocking on mobile phones and face recognition. TEE data encryption must rely on hardware devices, and the calculation process is performed in an isolated execution environment based on hardware protection capabilities. Therefore, it is necessary to rely on a trusted hardware manufacturer for security. TEE’s application projects mainly include Phala Network, Oasis Labs, Enigma, etc., which are closest to practical scenarios compared to other privacy computing solutions.

Zero-knowledge proof (ZKP) is a special interactive proof in which the prover knows the answer to the question, and he can convince the verifier that his answer is correct without providing any useful information to the verifier. Zero-knowledge proof can realize flexible data calculation interaction and cross-validation, but it is still difficult to achieve because it requires repeated examples to verify that the answer is true, which requires very high computing power. At present, the efficiency of generating a proof is about 7 seconds, and a large amount of computing power is required to increase the calculation rate. The ZK rollup of the second-layer chain of Ethereum is the application of zero-knowledge proof. Therefore, the significance of ZK rollup is not only to expand the capacity, but also to assist Ethereum in implementing off-chain privacy calculations.

The biggest challenge that privacy computing currently faces is how to improve the efficiency of privacy protection and achieve large-scale commercial landing. The above several technologies, whether it is based on the calculation of HE or MPC, or based on the verification of ZKP, all have this problem. The only TEE that can achieve commercial applications relies on hardware facilities, and the development and production of dedicated computing hardware requires a huge upfront cost. Therefore, this is why the concept of privacy computing has appeared since 2014, but there is no real project yet. This is more like an industrial blockchain, which is different from our traditional blockchain. It needs to connect the virtual world with the real world.

PlatON’s privacy protection design combines some of the above technologies and strives to achieve all aspects of network privacy from three perspectives: privacy calculation, privacy verification, and dedicated privacy circuits. Firstly, privacy calculations are realized through MPC and HE; then the calculation results are verified through ZKP and verifiable calculation (VC); finally, combined with contract calculation, the encrypted smart contract is compiled into a circuit, which is split into multiple sub-circuits in the form of a circuit. Task, through the incentive mechanism to attract idle computing power in the network to calculate the subtasks, and solve the common efficiency problems of the above technologies. This kind of thinking actually borrows from Ethereum’s ZK rollup, which moves complex calculations off-chain and only transmits the calculation results back to the main chain. Due to the need to compile smart contracts into circuits, the PlatON team must cooperate with mainstream hardware manufacturers in the industry to further improve computing performance in hardware.

The special PoS consensus algorithm of PlatON

According to the official white paper, PlatON will launch FPGA/ASIC-based dedicated computing hardware at the right time. This is not a simple PoW mining. PoW is just one type of consensus agreement. As long as the community reaches a consensus, PoW can be changed to another consensus. , Such as PoS, Ethereum is moving in this direction. However, PlatON separates consensus from computing power, and computing power is only used to perform privacy protection calculations. The PlatON public chain plays the functions of computing task distribution, computing tasks and computing power matching, and transaction records. The core computing work occurs outside the public chain. Of course, you can understand this as a kind of PoW in disguise, but privacy computing is not a meaningless puzzle cracking game. Even if you leave the blockchain, these computing hardware can also be invested in a centralized world to provide privacy protection.

From this, the author speculates that PlatON’s ecological maintenance method may be divided into two parts: one is to use the PoS protocol to obtain fixed block rewards, and the other is to provide computing power to obtain labor fees from the data demander.

In the PoS part, first outline the four mainstream models in the industry: Chain-Based, DPoS, VRF and BFT. Chain-Based is the earliest PoS. According to the number of tokens held, validators are selected for block production. Ethereum currently uses this model. DPoS is that each token holder delegates the rights to some representatives, and the representatives participate in the production and verification of blocks. EOS currently uses this model. VRF is to randomly select verification nodes through a verifiable random function. The current representative projects include Dfinity, Algorand, etc. BFT is to confirm the final block through multiple rounds of voting through the Byzantine fault-tolerant protocol after the verification node is selected. Currently, NEO adopts this type of consensus algorithm.

According to the official blue book, PlatON uses a special PoS consensus algorithm-Giskard, which consists of PPoS (PlatON PoS) and BFT. PPoS is essentially a combination of Chain-Based and VRF. First, the node equity is mapped to the binomial distribution cumulative distribution function, and then VRF is used to randomly select verification nodes. The advantage of this kind of consensus is that the selected nodes are random and have a wireless relationship with the size of the node’s equity. After the node is confirmed, each node verifies the generated block through the BFT protocol, and finally reaches a block consensus, which can reduce the probability of a block being controlled by a malicious node. The Giskard consensus mechanism can theoretically inhibit the expansion of the mining pool endogenously to ensure the decentralization and security of the PlatON public chain.

The second part is to provide privacy calculations to obtain the labor costs of the data demander. The author believes that this is the essence of the PlatON consensus protocol. If the consensus effect can meet expectations, then market-based pricing of the data element will be realized. There are two problems in the data transaction process: one is that the ownership is not clear, and it is easy to be used without authorization; the other is that the data structure is diverse and it is difficult to quantify according to a unified standard.

The method shown in the blue book is to use cryptographic techniques such as HE and MPC to confirm data rights and determine the owner of the data. Adhering to the principle of data sovereignty in the process of data transactions makes it possible to trade data usage rights without affecting data ownership. There are two methods for data pricing: the first is absolute pricing, that is, the price that data users are willing to pay for obtaining the data; the second is relative pricing, that is, given a data set and a common task, evaluate the members of the data set Contribution to the completion of the task. Relative pricing uses the Shapley value as an important evaluation tool, which is an important concept introduced by the famous economist Lloyd Shapley (2012 Nobel Prize winner in economics) in 1953 when he studied cooperative games.

Industry development status

There are two main development paths for the privacy protection track, one is anonymous currency, and the other is privacy public chain.

Anonymous coins typically include XMR, DASH, ZEC, XZC, etc. XMR, as the leading project in this field, appeared in 2014. This technology only needs to encrypt the sender, receiver, transaction amount, transaction IP and other information, so that only the two parties involved in the transaction (or authorized third parties) can view the transaction information through the private key. Since there is not too much complicated information in currency circulation, it is not difficult for encrypted currency to realize anonymous transactions. This technology is currently very mature. In fact, BTC is also upgrading its privacy algorithm through community voting. Technologies such as CoinJoin can merge multiple transactions to cover the upstream of UTXO.

The privacy public chain technology is more complicated. Its essence is to encrypt smart contracts. It needs to encrypt input and output data and network status to make it concealed from all parties except the user (including the node that executes the smart contract). At present, the most promising development prospects are Ethereum’s two-layer network ZK rollup and Polkadot’s parallel network Phala, but these can only exist as sub-chains or parachains, mainly to provide data calculations for the main chain, and the calculation results must be returned. Main chain. If you want to develop an independent privacy public chain, the difficulty is still higher than the above-mentioned technologies. The current head projects are PlatON and Oasis, and once completed, their potential will be extremely huge. The reason is: as independent public chains, they can directly develop privacy smart contracts on the main chain, and can also be used as side chains or parachains to provide privacy calculations for other public chains.

Oasis team members include Professor Dawn Song from the University of Berkeley and a number of world-leading security experts. Currently, they have received US$45 million in investment from investment institutions such as Binance Labs and a16z. In addition, Oasis has realized the interaction with the Ethereum network, and developers have gradually tried to establish NFT projects on the Oasis network.

PlatON has currently received 50 million US dollars of investment from Alpine Capital, Hash Global Capital and other institutions. The same thing as Oasis is that both achieve high-concurrency private computing; the innovation lies in that the PlatON network is in addition to the consensus network (main network) and Oasis. The privacy computing network also has a layer of independent AI network, which aims to realize big data model training.


[1]China Academy of Communications, “Privacy Protection Computing Technology Research Report (2020)”

[2]”PlatON. Economic Blue Book (V0.1.1)”

[3]”PlatON. White Paper (V6.8)”

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