The battle for privacy, who can perform best in the blockchain arena?
Privacy is one of the important foundational issues in the entire industry.This article was published on November 26, 2020, by Mars Finance, authored by Carrie&lvy from Qianfeng Capital.
1. Industry Status
As a trust solution in the digital age, blockchain technology faces a certain contradiction between its decentralized transparency and the privacy required in reality. The public verifiability of on-chain data means that transaction data is traceable and cannot be tampered with. However, when we interact in the real world and the virtual world, it inevitably leaves traces that can be tracked back to real identity information from transaction data. All data stored on the chain is publicly verifiable, which poses a fatal problem for blockchain and smart contracts. The lack of privacy protection is unacceptable in the real world, as not only individuals wish to protect their property information and other private data, but any enterprise or organization also wants to keep its sensitive and valuable data confidential.
Moreover, privacy regulations such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) indicate that regulation on the protection of personal data will become increasingly strict. Therefore, if privacy issues cannot be resolved, cryptocurrencies and blockchain cannot achieve mass adoption. Currently, the industry has proposed the following different solutions to address the privacy issues of blockchain:
1.1 Privacy Cryptocurrencies
To achieve privacy in cryptocurrencies, it is necessary to keep information such as the sender, receiver, transaction amount, and transaction IP confidential, making this information visible only to the parties involved (or third parties designated by the parties). This has led to the emergence of anonymous coin branches. This branch is characterized by being purely a currency without supporting smart contract functions. Currently, this field has a rich and mature project landscape, with many leading projects launched as early as 2014. As of November 23, 2020, the total market value of the anonymous coin sector is 25.4 billion RMB.
Data Source: Non-small number, as of November 23, 2020
Bitcoin's Privacy Infrastructure
In fact, Bitcoin's original design was also aimed at achieving anonymity, but Bitcoin uses asymmetric encryption algorithms, and its privacy anonymity is relatively weak, as users' transactions can still be viewed by all nodes. Furthermore, with the improvement of algorithms, transactions can be traced back to the sender and receiver based on UTXO. Thus, some improved algorithms were proposed in the early days based on Bitcoin's privacy infrastructure.
• CoinJoin
The principle of this solution is relatively simple, which is to merge multiple transactions from different users into one transaction to obscure the ownership of UTXO. Others cannot determine that the address belongs to one person from this obfuscated transaction, nor can they determine the flow of currency. Additionally, users can perform CoinJoin operations multiple times to further hide transaction information. The CoinJoin solution does not require changing Bitcoin's protocol and is relatively easy to implement. The downside of CoinJoin is that the intermediary server can grasp all users' input and output addresses, exposing privacy to third parties. Moreover, if the intermediary server is attacked, users' information may be easily leaked. Besides, among the participants in the mixing, at least one party knows who participated in the mixing or can access the mapping between addresses. Currently, there are three main privacy wallets that use mixing servers (tumblers): Wasabi, Samourai, and Joinmarket. Users utilizing CoinJoin need to register on the server, where the service aggregator requests participation in the mixing process for multiple users. Mainstream service providers such as BTCPay Server also adopt the CoinJoin-based privacy technology P2EP. Unlike conventional Bitcoin transfers initiated solely by the sender from their wallet, P2EP transfers bundle the inputs of both the sender and receiver together, with the receiver also sending additional Bitcoin to themselves. This enhances privacy performance compared to simple CoinJoin.
• TumbleBit
TumbleBit is a decentralized mixing service. It creates offline payment channels among participants using a decentralized tumbler. Users send coins to an intermediary tumbler and receive an equivalent amount of other Bitcoins through these channels. TumbleBit offers a higher level of confidentiality than CoinJoin because the interaction between individuals and the tumbler is independent and not influenced by other malicious parties, and the anonymity size is not restricted. However, mixing on TumbleBit requires pre-funding for the mixing.
• Coinshuffle
Coinshuffle is an improved mixing solution based on the CoinJoin concept. It introduces a monitoring mechanism that allows identifying malicious nodes after each mixing failure, enabling users to avoid malicious nodes in the next round of operations. Coinshuffle does not require additional mixing fees and can monitor low-skilled participants, but its downside is that it cannot customize the mixing amount and has lower efficiency. Overall, all mixing solutions can be compatible with the Bitcoin system and can protect Bitcoin transaction addresses. However, all mixing solutions face overhead issues, as the system requires more computational and communication resources to achieve mixing.
1.2 Privacy Smart Contracts
For the privacy of smart contracts, it is necessary to encrypt input and output data as well as network status, making them hidden from all parties other than the user themselves (including the nodes executing the smart contract). Through privacy smart contracts, sensitive data and applications can run securely in an open public chain environment, which is required by most practical use cases. Regarding the privacy issues of smart contracts, one category is infrastructure based on public chains that addresses the privacy issues of public chains; another category focuses on privacy computing, aiming to develop vertically segmented fields as foundational public chains.
1.2.1 Public Chain Privacy Solutions
1. Public chains that support privacy smart contracts (or privacy dapps) at the underlying architecture. Projects in this category include Horizen and Particl launched in 2017, as well as Zcash that emerged in 2018. The overall market value of this segmented field is about 900 million RMB. Compared to purely anonymous coins, projects that combine the concept of privacy smart contracts receive significantly less funding attention. This may be partly due to different levels of development maturity, and on the other hand, it may indicate that such project positioning has not been recognized by the market.
In addition, there are projects like AOS and Origo that are not anonymous coins themselves. Origo is a distributed privacy application platform that can simultaneously support private transfers and privacy smart contracts, with investment institutions including Polychain Capital, Consensus Lab, NGC, and other well-known institutions, as well as the same investors in Zcash. However, this project is not favored by the market, having dropped -89.86% from its IEO price, with the highest increase during that period being less than 100%. The AOS project is somewhat suspicious; from the limited disclosed information, it appears to be a privacy public chain that supports users to issue privacy assets independently and can use zero-knowledge proof programming and facilitate the development of privacy DAPPs. Although the circulating market value displayed on Non-small number is 110 million RMB, it is listed on only two very small trading platforms, making this market value highly questionable, and the project has disclosed very little information.
Data Source: Non-small number, coingecko as of November 23, 2020, for comparison, the vertical axis here is the same as that of purely anonymous coin projects
2. Self-upgrading of existing public chains. Ethereum has always been exploring scalability and privacy issues, and a solution currently highly valued by the team is the ZK Rollup technology based on zero-knowledge proofs, with engineering implementations like ZK Sync looking promising. In terms of scalability, ZK Sync can bring thousands of transactions per second (TPS) throughput to Ethereum, high resistance to auditing, and ultra-low latency. In terms of privacy, Ethereum will establish a programming framework and virtual machine environment specifically designed for zero-knowledge proof-based smart contracts, which can greatly lower the technical threshold for developers to develop privacy smart contracts.
3. Privacy tools and protocols for existing public chains. These projects do not have independent mainnets and focus on serving other public chains. For example, Phala Network, a privacy smart contract platform in the Polkadot ecosystem, will become a parachain of Polkadot in the future, providing privacy computing, confidential smart contracts, DeFi, and data services applications through cross-chain protocols for any blockchain. The protocol currently supports functions such as transaction transfers in a privacy environment and one-click publishing of privacy assets. With Polkadot's support, it can become a confidential smart contract network with composability and interoperability. There are also many privacy tools around Ethereum, such as the privacy protocol project Aztec, which adopts the ZK-ZK rollup solution to achieve hundreds of privacy transactions per second on the Ethereum mainnet while reducing the cost of each privacy transaction.
The Aztec protocol uses a "zero-knowledge receipt" system to track hidden assets. These receipts (including the owners of the receipts) are publicly available on the Ethereum network, but unless you are the owner of the receipt, you cannot know the amount in each receipt; Ethereum's layer two privacy technology Zkopru combines Zk SNARK and Optimistic rollup technology to support low-cost private transfers and atomic swaps within the layer two network; "Nightfall," released by one of the Big Four accounting firms, Ernst & Young's blockchain team, utilizes zero-knowledge proofs to enable anonymous transactions on the Ethereum blockchain. After iterations, Nightfall can be widely applied to game items and collectibles; through the open-source mixer Hopper, mobile devices can conduct private transactions on the Ethereum blockchain, allowing users to deposit or withdraw ETH in private accounts without revealing any public account addresses, and it also uses zero-knowledge proofs to verify the recipient of private transfers; Quorum allows for the construction of private contracts and private transactions based on Ethereum, permitting specification of which nodes in the designated network can access and execute the contract, while other nodes cannot see the contract's code or data, nor can they query or execute it, making transactions visible only to authorized participants.
1.2.2 Privacy Computing Public Chains
1. Public chains that support privacy smart contracts (or privacy dapps) at the underlying architecture, but whose native tokens are not anonymous coins. Representative projects are those that use privacy encryption technology as their core starting point, generally referred to as privacy computing public chains, such as Enigma, ARPA, PlatON, and Oasis Labs. On one hand, as independent public chains, they can directly develop privacy-compatible smart contracts on the main chain; on the other hand, they can also serve as layer two (Layer-2) solutions for other public chains, providing privacy computing capabilities for any public chain. Additionally, these projects aim to combine with the big data and AI industries, presenting a broad outlook. Oasis Labs and PlatON's tokens have not yet entered the secondary market, so market value comparisons cannot be made at this time. However, as representative high-quality projects in both foreign and domestic markets, they have already garnered significant capital attention in the primary market. Oasis Labs has raised a total of $45 million through private placements, with investment institutions including well-known entities like Polychain, a16z, Binance Labs, and 36 other investment institutions.
PlatON has raised over $50 million in two rounds of financing, with the latest round led by Gaoshang Capital and Hash Global Capital, with participation from Singapore's OUE Group, leading insurance asset management institutions in Asia, and other family offices. Both projects are still in relatively early stages, with their mainnets not yet officially launched and their tokens not yet listed on exchanges. However, their teams are strong and focused on technical development, consistently receiving positive feedback from the community and high market attention. It is believed that both have the potential to become the most promising projects in this field. Currently, the overall market value of this segmented field is 330 million RMB, mainly because privacy computing-type projects are still in early development, and the main projects receiving market attention have not yet entered the secondary market.
Data Source: Non-small number, coingecko as of November 23, 2020, for comparison, the vertical axis here is the same as that of purely anonymous coin projects
2. Market Pain Points
Blockchain technology has a wide range of application scenarios. In addition to cryptocurrencies and payment transfers, it can empower business scenarios across various industries. However, the current reasons for its inability to achieve mass adoption are: insufficient scalability, high costs, poor user experience, and lack of privacy protection. Alex Gluchowski, a researcher on blockchain scalability solutions, pointed out the pain points of solving privacy issues. Due to the following factors, achieving privacy protection on public blockchains is extremely difficult:
- Privacy protection must be enabled by default as a complete protocol feature. In the words of Vitalik Buterin: "Only a globally anonymous set is truly reliable and secure."
- To enable privacy protection by default, computational costs will significantly increase, but for privacy transactions to be practical, their costs must be very low.
- Privacy models must support programmability, as real-world use cases require more than just transfers: these privacy models also need account recovery, multi-signature, payment limits, and so on.
The Dilemma of Security and Privacy The design of blockchains such as Bitcoin and Ethereum has chosen security and decentralization, sacrificing scalability to some extent, making it difficult for blockchains to support heavy and complex computations. Similarly, this design has also led to a dilemma between security and privacy. On-chain data is publicly verifiable, which ensures the security of each transaction but poses significant challenges to user privacy protection. In fact, global anonymity can be achieved through data encryption, where encrypted data can be verified by permitted parties without requiring complete public transparency. For example, privacy technologies such as zero-knowledge proofs and secure multi-party computation use cryptographic methods to encrypt data, allowing only the permitted parties holding the private keys to verify correctly.
Scalability Challenges
The sacrificed scalability gives users the most intuitive feeling of long transaction waiting times and high transaction fees. On Ethereum, due to state congestion leading to skyrocketing gas fees, transaction waiting times are extended, and for the cumbersome computational steps required for privacy computing, high fees will only deter users. Therefore, some privacy computing projects choose to build their own native public chains or seek high-performance scalable cross-chain solutions.
Composability and Interoperability
Privacy, in addition to anonymity, also requires various supporting anonymous modules to collaborate on-chain while maintaining privacy in multi-signature, account recovery, smart contracts, and other interoperability aspects. Furthermore, most existing blockchain technology systems are limited by performance and trust issues between nodes, while further development of blockchain requires not only performance improvements but also the ability to combine and interoperate across different chains. Smart contracts on different chains must be able to call each other and operate in parallel to achieve sufficient data exchange and collaborative computation, which can solve more complex problems in various applications.
3. Privacy Solutions
Based on the above pain points, different projects have chosen different technical solutions. So far, various privacy anonymity protection technologies have been proposed and are continuously evolving and improving. Initially, the CryptoNote protocol aimed at digital token privacy was proposed, which uses stealth addresses and ring signatures to protect the anonymity of the addresses of both parties in a transaction. In 2013, the "mixing" technology was proposed for Bitcoin's privacy, which only increased the difficulty of tracking but could still be traced. To improve the shortcomings of mixing that required third-party participation and insufficient anonymity, anonymous coins represented by Zcash and Monero emerged, which use zero-knowledge proofs and ring signatures to protect the privacy of native coin encryption. At the same time, side chains and channel-based layer two solutions have also been proposed one after another.
These solutions focus on anonymity at the transaction level and cannot be extended to Turing-complete smart contracts. Therefore, starting in 2018, a series of privacy computing projects began to emerge, where privacy should not only be based on user transaction privacy but should also extend to smart contracts to protect any confidential data within smart contracts from being leaked and to enable interaction of smart contracts. For example, Arpa uses cryptographic-based secure multi-party computation (MPC), while Enigma and Oasis adopt hardware-secured executable environments (TEE), and Ethereum uses zk rollup to solve scalability and privacy issues. The following table compares the application situations and advantages and disadvantages of various privacy anonymity technologies:
Comparison of Mainstream Privacy Technologies
The first three solutions in the table focus more on transaction anonymity and find it challenging to achieve good security and 100% anonymity. In terms of privacy computing, there are three cryptographic-based technologies: fully homomorphic encryption (FHE), secure multi-party computation (MPC), and zero-knowledge proofs; hardware design-based solutions mainly include trusted execution environments (TEE) and other technologies. The security and credibility of secure multi-party computation (MPC) are based on cryptography, being securely verifiable, and its practicality mainly focuses on small scenarios for processing sensitive data with specific algorithms and high-security requirements. However, its computational flexibility is limited, and as the number of participants increases, computational efficiency will further decrease, leading to communication burdens in practical applications. Currently, a single operation of MPC can achieve millisecond-level speeds, but in big data scenarios, a data application or model training may involve tens of thousands of data samples, where computational efficiency and communication burdens are bottlenecks hindering the development of MPC. Moreover, for applications requiring complex computational tasks, MPC is still challenging to meet and needs several years for optimization. Fully homomorphic encryption is still in the theoretical stage, relatively lagging in credibility, flexibility, and efficiency, with practical applications being too inefficient, and its construction and implementation technologies being complex, making large-scale commercial applications unfeasible. Existing FHE solutions mainly reduce ciphertext expansion issues through homomorphic decryption technology, which theoretically can overcome computational boundary issues, but from an implementation perspective, it is very complex.
Additionally, security and applicability issues must also be considered. Currently, most homomorphic encryption algorithms cannot effectively resist adaptive chosen ciphertext attacks, with the highest security level only able to resist chosen plaintext attacks.
Trusted execution environments (TEE) are already widely used, such as fingerprint unlocking and biometric recognition on mobile phones. TEE relies on trusted hardware facilities for security, depending on the trusted environment of the hardware and centralized hardware vendors, requiring trusted assumptions about the hardware, which may face side-channel attacks (SCA, a type of cryptographic attack that can extract secret information from cryptographic devices). Its advantages include high flexibility, friendliness to general computing, and faster rates. The technology is relatively mature, and compared to other privacy computing solutions, TEE's overall strength is the closest to practical scenarios.
Zero-knowledge proofs have the highest credibility and can achieve complete anonymity, but some protocols also require trusted setups, relying on the generation of special random numbers. They can achieve flexible data computation interactions and cross-validation, but the implementation difficulty remains high, with the current efficiency of generating proofs being around 7 seconds, requiring substantial computational power to improve calculation speed. The following table compares the details of four privacy encryption technologies:
The different technologies in the table each have their own advantages and disadvantages. It should be noted that privacy solutions must start from demand; it is not simply a matter of judging which technology is superior or similar, but rather which technology is more suitable for solving problems in specific scenarios. Therefore, these solutions are not inherently contradictory, and in certain scenarios, combining them can achieve better results.
4. Market Forecast
4.1 The Dilemma of Anonymous Coins
Early-stage privacy projects focused on privacy currency scenarios, with Monero being the undisputed leader in this field, as it is the preferred choice for users of privacy transactions. For example, anonymous coins like Monero and Zcash can achieve features that are difficult to trace compared to Bitcoin, but their privacy use cases are limited. Most of their use cases are concentrated in illegal transactions, and the transaction basis requires liquidity and acceptance, needing many users to utilize it. This is effective for Bitcoin, as Bitcoin has greater liquidity.
Indeed, in gray area application scenarios such as hacking incidents, illegal transactions, and ransomware, Bitcoin's usage far exceeds that of Monero. Therefore, anonymous coins may never surpass or replace Bitcoin, and with the improvement of Bitcoin's own privacy, anonymous coins may lose even more market share. Thus, for anonymous coins, only leading projects still have room for survival, such as the most practical and well-known Monero, and the more technically advanced Zcash and Dash. The competitive landscape of privacy currencies has basically taken shape. It is challenging for trailing privacy coins to break through; they will either perish or have to seek alternative paths.
For instance, Beam recently launched a new feature for issuing stablecoins with privacy transaction attributes, exploring a diversified strategy in DeFi, which is a survival path Beam must take, but the opportunity may still be slim. Additionally, regulatory risks also impose significant constraints on the market space for anonymous coins. According to the well-known blockchain analysis firm Chainalysis, global law enforcement and government agencies' interest and demand for blockchain investigation technologies are continuously growing.
In June 2019, the FATF (Financial Action Task Force) released a regulatory bill for cryptocurrencies, requiring exchanges to collect and transmit customer information during transactions. This information includes the names, account numbers, and address information of transaction initiators, as well as the names and account information of recipients. This effectively strikes at the Achilles' heel of anonymous coins; once the G20 adopts the same FATF regulations in its member countries, most mainstream exchanges may delist anonymous coins. Recently, the company signed a controversial contract with the IRS to help track Monero. The CEO of Chainalysis believes that the future of anonymous coins like Monero is limited. In terms of regulation, anonymous privacy coins face the greatest obstacles, and privacy protection needs to seek alternative paths.
4.2 The Continuous Growth of Ethereum
On the public chain infrastructure, Ethereum adopts zero-knowledge proof technology for privacy, which can not only bring dozens of times performance improvements to existing Ethereum but also solve global privacy issues for Ethereum. Currently, there are multiple projects on Ethereum serving as privacy infrastructures, such as the AZTEC protocol, which uses zero-knowledge proofs and is highly efficient in both scalability and anonymity. The implementation of Aztec can bring a private decentralized exchange to Ethereum, enabling the trading of different Aztec assets under complete confidentiality; private weighted voting in community governance and other financial applications, ensuring the privacy of voters; anonymous identity-sharing solutions that prove identity ownership without revealing identity. The expansion of these applications plays a crucial role in the interaction of smart contracts on Ethereum, and true anonymity may become a reality. Ethereum's scalability and privacy issues are gradually being resolved, and with its currently established strong application ecosystem, other public chain projects will find it challenging to compete.
4.3 Development in Vertically Segmented Fields
In addition to the continuous development and improvement of public chains themselves, privacy computing also has a place in segmented fields. In 2018, a batch of projects focusing on privacy computing emerged, such as arpa, oasis labs, PlatON, and Phala Network. They provide privacy protection for blockchain using cryptography or trusted hardware. The use cases of these projects are no longer limited to the privacy protection of on-chain data; their emergence fills the gap in privacy issues during data computation, creating more possibilities for usable use cases in the real world. Currently, the commercial value of privacy computing is highlighted in the global data market, with numerous vertically segmented fields in the data market, such as data trading, AI, big data, and cloud computing.
In the current data trading encryption market, most projects loudly proclaim the goal of breaking data silos, but this is essentially a false proposition. The premise for data to be traded is a clear division of ownership and usage rights, allowing users to control their information. If the source and use of data do not have clear direction or permission, data can be freely reproduced and replicated, leading to data redundancy or arbitrary forgery. Therefore, when building a data market with good positive incentives, it is essential to establish a trustworthy privacy protection mechanism, standardize data collection, clarify ownership, ensure information privacy, and maintain transaction transparency. Only then can data become the user's asset rather than an accessory, and data silos can truly be resolved.
The AI industry is one of the existing commercial applications that interacts with the most data, but the entire AI market is currently facing a significant bottleneck: data is relatively fragmented, and to improve the overall model's accuracy, as much data as possible needs to be obtained; however, due to data privacy issues, it is becoming increasingly difficult to access user data. This "contradiction" has already become apparent throughout the AI industry. Privacy computing can significantly alleviate the current "contradiction" in the AI market, thus presenting a larger emerging market.
Currently, the prevalent business model in the internet industry involves collecting data at a lower cost and then utilizing big data analysis to create and monetize that data. For example, if a user clicks on a product on social media, platforms like Taobao or Pinduoduo will promote that product on their homepage. Undoubtedly, users' data is "passively" stripped of ownership at the outset of using the product, and this data is stored on third-party platforms, continuously generating profits for the platform, while users face the risk of information leakage. Blockchain privacy computing can effectively intervene here; privacy computing can first encrypt users' raw data before performing big data calculations, while providing economic incentives to users who provide data. Data demanders can purchase data, constructing a data market that facilitates positive circulation and incentives.
However, current privacy computing technologies still face bottlenecks of high costs and low efficiency. In scenarios involving big data and AI, the sample size for training a model may be around 100,000, and the feature quantity may require even more computational resources. The extensive computations lead to inefficiencies, and practical commercial implementation may still take several years to complete.
4.4 Comparison of Privacy Projects
5. Summary
Privacy security technology has gained significant attention and development in the blockchain field, with numerous projects choosing different directions and paths based on their characteristics and technical capabilities. Theoretically, FHE, NIZK, and MPC can all achieve good privacy protection, but there is still considerable room for optimization in terms of efficiency and cost. Additionally, the development difficulty of these technologies is high; for instance, Enigma, which initially planned to use secure multi-party computation, has largely adopted TEE technology in practice despite listing MPC as its core technical means in its white paper. While TEE currently leads in efficiency, it compromises on security and privacy.
Overall, the development of privacy security technology in various projects is still in the continuous research and engineering implementation stage, requiring ongoing iterations and testing, and there is still a long way to go and much work to be done before true implementation. Considering the aforementioned pain points regarding privacy issues, we need to pay attention to the development of various projects in the following areas: 1) How to better connect privacy technologies with underlying protocols, making privacy protection a default complete protocol feature; 2) Continuously optimizing efficiency and cost to achieve practicality; 3) Supporting programmability to be more developer-friendly, promoting broader commercial scenarios.
The competition in the privacy track is still ongoing. While it remains uncertain which technological solution and project will ultimately prevail, currently, PlatON, Oasis Labs, and Ethereum appear to have the greatest potential.
Ultimately, the competition in the privacy track will hinge on general privacy. Among the three development directions and strategies mentioned earlier, the most promising are privacy computing public chains with core encryption technologies and the self-upgrading of existing leading public chains. The reasons are as follows: • Possessing core encryption technology means that the project employs cutting-edge encryption technologies, but more importantly, the core team must have relevant technical strength. Currently, PlatON and Oasis Labs meet this condition. Through privacy computing, these projects can meet the computational resource requirements for high-intensity calculations and solve data sharing issues, thereby empowering the AI and big data industries, making them vertical public chains deeply engaged in these two fields. Their strategies do not require direct competition with existing public chains and can serve as layer two solutions for other public chains, providing privacy computing capabilities, making their competition more flexible and diverse.
• Privacy computing public chains explore more specialized and general application scenarios starting from core encryption computing technologies, while Ethereum adopts a different strategy. Ethereum has consistently followed a gradual protocol upgrade path, addressing key issues step by step. In the initial stage, it built a first-mover advantage and ecological advantage based on security and centered around smart contracts, becoming the undisputed leading public chain; in subsequent iterative upgrades, it gradually resolves scalability and privacy issues. Currently, Ethereum has found breakthroughs for scalability and privacy issues through ZK Rollup, and if it can successfully iterate and upgrade, a winner-takes-all situation will be inevitable for the vast majority of public chains.
• Compared to leading public chains and privacy computing public chains, the core teams of other privacy smart contract public chains exhibit relatively weak technical capabilities, and project planning often remains at the conceptual stage; in terms of developer and user ecosystems, they lack competitive advantages. Due to the strong network effects of foundational public chains, these trailing projects find it challenging to attract developers from mature public chain ecosystems and beyond, making it extremely difficult to stand out.
• As for other privacy tools and protocols, they resemble transitional solutions that may possess certain practicality and value at a specific stage, but in the long run, they will likely be replaced.
In summary, privacy is one of the important foundational issues in the entire industry. Projects that achieve technological breakthroughs and provide comprehensive solutions the fastest can establish a foothold in the competition for the entire infrastructure. From the current competitive landscape, the most potential in this track lies with projects possessing core privacy technologies and those with the strongest overall competitiveness. The former is expected to serve as decentralized cloud computing for computation-intensive industries, while the latter may become a decentralized global computer supporting a broader range of commercial scenarios.
