Research Summary
The report discusses the role of privacy in blockchain technology and the potential of technologies like Zero-Knowledge Proofs (ZKPs), Fully Homomorphic Encryption (FHE), and Multi-party Computations (MPC) in managing private data on blockchains. It also highlights the challenges and applications of these technologies in various sectors, including social media, enterprise payments, and decentralized AI training.
Key Takeaways
Privacy in Blockchain Technology
- Trade-off between Transparency and Privacy: While blockchain technology eliminates intermediaries, it often compromises privacy, limiting the expansion of Web 3.0 into areas requiring confidentiality. Technologies like ZKPs, FHE, and MPC have emerged to address these privacy concerns.
- Personal and Shared Private States: Private state on blockchains is categorized into Personal Private State (PPS) and Shared Private State (SPS). PPS is data owned and viewable by a single entity, while SPS is data that multiple parties can access and compute on without compromising privacy.
Zero-Knowledge Proofs (ZKPs)
- Role of ZKPs in Privacy: ZKPs allow data owners to decrypt, modify, and re-encrypt their data locally, then generate a ZKP to validate the changes to the network. This makes ZKPs ideal for managing personal private data and private payment networks.
- Challenges and Applications of ZKPs: Despite usability improvements, zk systems face challenges such as client-side private state computing affecting user experience and the unsuitability for shared private state handling. However, they are ideal for applications requiring strong privacy, such as anonymous social media.
Fully Homomorphic Encryption (FHE)
- Computations on Encrypted Data: FHE enables computations on encrypted data, producing correct encrypted results without decryption during the process. This enhances privacy and security for shared private states.
- Limitations and Applications of FHE: FHE faces limitations such as privacy trust assumptions and computational complexity. However, it is suitable for applications like information-incomplete games, private voting systems, and private AMM or DeFi pools.
Multi-party Computing (MPC)
- Computing on Private Data: MPC enables computing on private data without revealing the data itself, making it suitable for handling private state in blockchain applications such as decentralized AI training.
- Applications of MPC: MPC has been applied in Dark Pool Central Limit Order Books (CLOBs) in DeFi, decentralized inference of proprietary AI models in DeFi and Web 3 Credit scoring, and training open AI models with proprietary data.
Actionable Insights
- Exploring the Potential of Privacy Technologies: Entrepreneurs and developers in the blockchain space should explore the potential of privacy technologies like ZKPs, FHE, and MPC in managing private data on blockchains. These technologies can unlock new use cases in Web 3.0 and accelerate the adoption of blockchain technology in various sectors.
- Combining Different Privacy Technologies: Practical applications often require a combination of ZKPs, MPC, and FHE to leverage their respective strengths and compensate for their limitations. Developers should consider integrating these technologies to provide comprehensive tools for building privacy-centric applications.
- Addressing the Challenges of Privacy Technologies: While privacy technologies offer significant benefits, they also face several challenges, including computational complexity, privacy trust assumptions, and potential information leakage. Developers should focus on addressing these challenges to enhance the usability and effectiveness of these technologies.
