What Is Zero-Knowledge Machine Learning (ZKML)?
The combination of Artificial Intelligence and blockchain opens up new technological possibilities. In this field, Zero-Knowledge Machine Learning (ZKML) is a growing trend that enables effective use of data without risking privacy.
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Zero-Knowledge Machine Learning Explained
ZKML is a research and development field that bridges Machine Learning with Zero-Knowledge algorithms to enable privacy-centric solutions. Through this approach, it becomes possible to train machine learning models and build data-empowered applications.
ZK Proofs
This is a computational model to demonstrate the correctness of a statement without revealing any further information. ZK Proofs provide scalability and security for blockchain transactions by moving computation off-chain. This technology relies entirely on mathematical approaches and is based on cryptographic proof types, such as STARK, and zkSync.
A STARK-based ZK rollup. Source: Polygon Community Forum
Machine Learning
Machine Learning is a subset of Artificial Intelligence that focuses on teaching computer systems to make decisions based on data. There are different machine learning models, one of the most popular ones being the Large Language Model, used in Chat GPT, and Bard. ML has a transformative impact across industries. It’s used in recommendation systems, content creation, algorithmic trading, and many other applications. The machine learning pipeline includes three steps: data collection, training, and usage, which is also called inference.
The three main steps in the machine learning pipeline. Source: github.com
Use Cases of Zero-Knowledge Machine Learning
Machine Learning has many useful applications, but its rapid growth raises concerns about privacy and intellectual property. There are problems with unauthorized use of data, deepfakes, and copyright issues. Another matter is that the technology requires large servers and resources for processing. ZKML can become the key to these problems by providing on-chain verification. In its turn, ML brings new use cases for blockchain enabling the creation of applications where a smart contract gains AI capabilities, analyzing data and acting based on it. Below are some promising use cases of ZKML:
- Healthcare. In a data-sensitive industry like healthcare, identity verification, and data analytics require privacy mechanisms. ZKML protects patients’ right to confidentiality while enabling the development of personalized medical recommendations and treatment plans. The technology can be used by research institutions to collaborate on data analysis and build diagnostic tools while keeping patients’ identities private.
- Finance. ZKML provides solutions to protect the financial sector from information leaks and privacy concerns. By employing ZKML, financial institutions can detect fraud and assess risks. The method offers a completely private KYC (Know Your Customer) process, where identity verification is possible without exposing personal information. Users can input encrypted prompts for recommendations, and make transactions without revealing their identity.
- E-commerce. In online shopping, ZKML provides data-driven insights without trading off privacy. The technology makes it possible for marketplaces to provide users with personalized product suggestions without having access to their personal information.
- Gaming. Zero-knowledge proofs and Machine Learning unlock new interaction ways for players. Through ZKML, they gain ownership of in-game items represented as NFTs and can automatically evaluate various in-game situations. The integration of ZKML enhances player autonomy. It creates dynamic gaming experiences where decisions are driven by advanced algorithms and data analysis.
- Governance. In governance, ZKML offers a new approach to achieving transparency, accountability, and privacy. Decentralized governance protocols can analyze large amounts of data through confidential voting and make an informed decision. Processes can be monitored by an AI agent, which will ensure the protocol’s security.
Challenges and Future Outlook of ZKML
ZKML is an evolving concept with its high growth potential and existing challenges. The volume and complexity of data are increasing, making the implementation of ZKML algorithms difficult. Besides, there are engineering issues that need to be addressed. However, given the dynamics and potential, ZKML is expected to reshape data privacy and security sectors over time.
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