Frameworks for Programmable Privacy with Ying Tong and Bryan Gillespie
Jul 3, 2024
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Ying Tong Lai, a researcher at Geometry Research known for her work on Zcash's Halo 2, and Bryan Gillespie from Inversed Tech, who focuses on privacy technologies, dive into the intricacies of programmable privacy. They discuss their research on classifying and implementing privacy in distributed systems. The duo shares insights on zero-knowledge proofs, the evolution of cryptographic systems, and the balance between privacy and usability in decentralized finance. Their personal journeys from academia to the crypto space unravel the interdisciplinary nature of these innovations.
Ying and Brian discuss the interdisciplinary nature of zero knowledge technology, highlighting how diverse academic backgrounds contribute to its development and innovation.
The podcast introduces a framework for programmable privacy in distributed systems, addressing selective disclosure, programmable verifiability, and computation over private data.
They emphasize the impact of community-driven workshops in fostering knowledge sharing and skill development for implementing the Halo 2 protocol in ZK applications.
Deep dives
Introduction to Key Researchers in Zero Knowledge
Ying Tong and Brian Gillespie discuss their backgrounds and journey into the realm of zero knowledge (ZK) technology, particularly their contributions to protocols like Zcash and Halo 2. Ying elaborates on her initial interest in cryptography sparked by her education in physics and her work on ZK roll-ups for Ethereum, while Brian shares his transition from a mathematics PhD to a career in cryptography, influenced by insights from Zcash. Both researchers emphasize the interdisciplinary nature of ZK, highlighting how skills from different fields, such as physics and mathematics, can enhance one’s understanding and contributions to cryptographic protocols. Their experiences reveal how diverse academic backgrounds can converge in the innovative landscape of decentralized finance.
Exploring Programmable Privacy in Distributed Systems
The podcast delves into the concept of programmable privacy, particularly within distributed systems, introduced through the recent work of Ying and Brian. They explain a framework for evaluating applications that utilize ZK technology for limit order auctions, outlining three categories of programmable privacy: selective disclosure, programmable verifiability, and programmable computation over private data. This framework allows for a more nuanced understanding of how different applications leverage ZK proofs to enhance user privacy and maintain data confidentiality. By applying this framework, the researchers aim to create a clearer classification of existing technology and to propose pathways for future development in programmable privacy.
The Role of Workshops in Advancing Zero Knowledge Techniques
Ying discusses the role of workshops she organized on the Halo 2 proving system, emphasizing their impact on the development of ZK applications. These workshops were designed to foster a collaborative environment for learning and sharing knowledge among developers, guiding them through the complexities of implementing the Halo 2 protocol. The enthusiasm and participation from individuals across academic and open-source backgrounds underscore the importance of community-driven educational initiatives in the rapidly evolving space of ZK technology. Such efforts have not only contributed to individual skill-building but also spurred the growth of projects incorporating advanced ZK techniques.
Challenges and Opportunities in Mediated Computation
The researchers identify the critical aspect of mediated computation in the context of privacy-focused applications. They explain that while techniques like secure multi-party computation (MPC) and trusted execution environments (TEEs) have been implemented to facilitate private computations, they also introduce unique security and efficiency considerations. In particular, placing the responsibility of computation either off-chain or on-chain affects the privacy and expressivity of the applications being developed. By analyzing the trade-offs between using these different computational techniques, they emphasize the need for robust design patterns that ensure both privacy and functionality are preserved in future applications.
Future Directions in Zero Knowledge Research
Looking forward, both Ying and Brian express interest in developing a taxonomy for various classes of applications that can benefit from programmable privacy. They reveal aspirations to explore how different design patterns could optimize privacy and security while facilitating generic computations in distributed networks. Their findings underscore a growing recognition of the necessity for safe and efficient protocols tailored for developers who may not have extensive cryptographic expertise. This proactive approach seeks to bridge the knowledge gap and contribute to the establishment of best practices within the broader zero knowledge ecosystem.
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