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A glance at the advantages and challenges of ZKML for zero-knowledge machine learning
Blockchain technology and machine learning, as two fields that have attracted much attention, lead the technological progress with their decentralized characteristics and data-driven capabilities respectively. ZK (Zero-Knowledge, hereinafter referred to as ZK) in blockchain technology is a concept in cryptography, **refers to a proof or interactive process in which the prover can prove a certain statement to the verifier veracity of this statement without disclosing any specific information about it. **ML (Machine Learning, Machine Learning, hereinafter referred to as ML) is a branch of AI. Machine learning learns from input data, summarizes it to form a model, and makes predictions and decisions.
In this context, ZKML (Zero-Knowledge Machine Learning), which combines the two, has recently flourished. ZKML combines the privacy protection and verification capabilities of zero-knowledge proof with the data processing and decision-making capabilities of machine learning, bringing new opportunities and possibilities for blockchain applications. ZKML provides us with a solution to simultaneously protect data privacy, verify model accuracy, and improve computational efficiency.
This article will introduce ZKML in depth, understand its technical principles and application scenarios, explore this exciting cross-field with developers, and finally reveal how ZKML can build a digital future with more complete privacy, security and efficiency! **
**ZKML: **Zero-knowledge proof combined with machine learning
There are two reasons why zero-knowledge proof and machine learning can be combined on the blockchain:
On the one hand, ZK's zero-knowledge technology not only hopes to realize the efficient verification of on-chain transactions, ZK developers also hope that ZK can be used in a wider ecological field, and the powerful AI support of ML has become a ZK application An excellent booster for ecological expansion.
On the other hand, the entire process from development to use of ML models is faced with the problem of proof of trust**. ZK can help ML realize the proof of validity without leaking data and information, and solve the trust dilemma of ML. The combination of ZKML means that both take what they need and go in both directions, and will also add momentum to the blockchain ecology.
ZK and ML complement each other in terms of development needs and capabilities
ML has a lot of trust issues to solve, and the accuracy, integrity, and privacy of individual workflows need to be proven. ZK can effectively verify whether any kind of computing is running correctly under the premise of ensuring privacy, well solves the long-standing problem of trust proof in machine learning. The integrity of the model is an important trust proof issue in the ML training process, but the privacy protection of the data and information that the ML model is trained and used is equally important. This makes it difficult for ML training to pass the third-party auditing and regulatory agency to complete the trust proof, and the decentralized ZK with zero-knowledge attributes is a trust proof path that is highly compatible with ML.
"AI improves productivity, blockchain optimizes production relations", ML injects higher innovation momentum and service quality into the ZK track, ZK provides ML with verifiability and privacy protection, ZKML and ZKML are in the blockchain environment Complementary operation.
ZKML Technical Advantages
The main technical advantages of ZKML realize the combination of computational integrity, privacy protection and heuristic optimization. From a privacy perspective, the advantages of ZKML are:
Achieving transparent verification
Zero-knowledge proof (ZK) can evaluate model performance without exposing the internal details of the model, enabling a transparent and trustless evaluation process.
Data Privacy Guarantee
ZK can be used to verify public data using a public model or verify private data using a private model to ensure data privacy and sensitivity.
ZK itself ensures the correctness of a certain statement under the premise of ensuring privacy through cryptographic protocols, which solves the defects of computing correctness proof machine learning in privacy protection and homomorphic encryption machine learning in privacy protection . **Integrating ZK into the ML process creates a secure and privacy-preserving platform that addresses the deficiencies of traditional machine learning. ** Not only does this encourage privacy companies to adopt machine learning techniques, Web2 developers are also more motivated to explore the technological potential of Web3.
ZK Empowers ML: Provides on-chain infrastructure
The shackles of computing power on the ML chain and ZK-SNARKs
The reason why ML, which is relatively mature off-chain, has just entered the chain is because the computing power cost of the blockchain is too high. Many machine learning projects cannot directly run in the blockchain environment represented by EVM due to computing power limitations. At the same time, although the validity verification of ZK is more efficient than double calculation, this advantage is limited to the transaction data processing native to the blockchain. When ZK's complex cryptographic calculations and interactions face a large number of ML calculations, the low TPS problem of the blockchain is exposed, and the low computing power of the blockchain has become the biggest shackle that hinders ML on-chain. **
The emergence of ZK-SNARKs alleviates the problem of high computing power requirements of ML. ZK-SNARKs is a cryptographic construction of zero-knowledge proof, and its full name is "Zero-Knowledge Succinct Non-Interactive Argument of Knowledge". It is a technique based on elliptic curve cryptography and homomorphic encryption for efficient zero-knowledge proofs. ZK-SNARK is characterized by high compactness. By using ZK-SNARKs, the prover can generate a short and compact proof, and the verifier only needs to perform a small amount of calculation to verify the validity of the proof without having to communicate with the prover many times. interact. This nature requires only one interaction between the prover and the verifier, which makes ZK-SNARKs efficient and practical in practical applications, which is more suitable for ML's on-chain computing power requirements. Currently, ZK-SNARKs are the main form of ZK in ZKML.
ML's on-chain infrastructure requirements and corresponding projects
The empowerment of ZK to ML is mainly reflected in the zero-knowledge proof of the whole process of ML, which is the interaction between ML and the functions on the chain. The two major problems that need to be solved in this interaction are to connect the data forms of the two and provide computing power for the ZK proof process.
ML Empowering ZK: Enriching Web3 Application Scenarios
*ZK solves the trust proof problem of ML and provides ML with an opportunity to be chained. Many Web3 fields urgently need the productivity or decision support of AI ML. ZKML enables on-chain applications to realize the empowerment of AI under the premise of ensuring decentralization and effectiveness. *
DeFi
ZKML can help DeFi to be more automated, one is the automation of protocol parameter updates on the chain; the other is the automation of trading strategies.
DID
ZKML can help the construction of Web3 decentralized identity DID. Previously, identity management modes such as private keys and mnemonics made Web3 user experience poor. The real DID construction can be completed through ZKML to identify the biological information of Web3 subjects. At the same time, ZKML can guarantee the security of user biological information privacy.
game
ZKML can help Web3 games to achieve full-featured on-chain. ML can bring differentiated automation to game interaction and increase the fun of the game; while ZK can make ML's interaction decisions on-chain.
Health Care and Legal Advice
Healthcare and legal consulting are areas with high privacy and require a large number of case accumulations. ZKML can help users make decisions and ensure that users' privacy is not leaked.
ZKML challenges
ZKML is currently developing vigorously, but because it is not native to the blockchain and requires a lot of computing power, ZKML will mainly face the following two challenges in the future:
*Most ML uses floating-point numbers to represent the parameters of the model, while ZK circuits need to use fixed-point numbers. In the process of digital type conversion, the precision of ML parameters will be reduced, which will lead to distortion of ML output results to a certain extent.
The high computing power requirements of its large model ZK proof:
At present, the computing power of the blockchain cannot cope with large-scale and high-calculation ZKML on the chain. **The current popular ZK-SNARKs only support small-scale and small-scale ML zero-knowledge proofs. **Computing power limitation is a key factor affecting the development of ZKML blockchain applications.
**The stage of ZK generating proofs has high computational complexity and requires a lot of computing power resources. **Due to the high correlation between the data that usually needs to be accessed and processed in the ZK proof stage, this process is difficult to be distributed, and it cannot be "parallelizable". Distributing this process may introduce additional complexity and even degrade overall performance. At present, to solve the problem of ZK computing efficiency, the mainstream research direction is more on algorithm optimization and hardware acceleration.
Conclusion
ZKML is a two-way journey between zero-knowledge proof and machine learning. The recent continuous development of blockchain technology ZK helps ML solve the problem of trust proof and provides an on-chain environment for ML; mature AI technology ML helps ZK realize Web3 Ecological expansion and application innovation.
The development of ZKML faces some challenges, such as parameter distortion problems and high computing power requirements for large models, but these problems can be solved through technological innovation and hardware acceleration. With the continuous emergence and development of ZKML projects, we can foresee that it will bring more innovation and value to the Web3 ecosystem in the fields of DeFi, DID, games, and healthcare. **
In the future, ZKML is expected to become the key to truly unlock the cross-integration of Web3 + AI, providing strong support for further building security, privacy protection and efficient blockchain applications. By combining ZK's zero-knowledge and ML's data processing capabilities, we will surely be able to create a more open, intelligent and trustworthy digital world!