The Cryptonomist conducted an interview with Eric Vreeland, CSO at Polyhedra Network to talk about Web3, zero Knowledge (ZK) proof and artificial intelligence (AI).
Can you provide an overview of Polyhedra Network’s mission and vision, especially in the context of Zero-Knowledge (ZK) evidence, and how you see it transforming the web3 and web2 ecosystems?
Our vision is to unlock exponential improvements in computing power and cross-platform interoperability through the use of zero-knowledge (ZK). Although zero-knowledge proofs (ZKP) are best known for their privacy features, ZKPs’ ability to compress large data sets into small proofs has enormous potential to revolutionize computation. Instead of having to verify each individual calculation or transaction, a ZKP can aggregate them so they can be quickly checked by a verifier. This could lead to massive performance improvements in compute-intensive applications such as AI and machine learning.
As traditional financial institutions continue to explore blockchain technologies, the ability to provide verifiable computation and maintain data privacy will make zero-knowledge an area of growing importance.
Until now, the biggest barrier to zero-knowledge adoption has been the complexity of the underlying technology, along with performance (speed) limitations. Polyhedra has continued to invest heavily in ZK research and continues to push the boundaries of how quickly zero-knowledge proofs can be generated. Our latest pilot system, Expander, is currently the fastest pilot system in the world and makes zero-knowledge applications much more practical. Our focus now is on making development more accessible with these high-performance pilot systems, so that every developer can realize the benefits of zero-knowledge.
How do Zero-Knowledge proofs enable secure, reliable transactions, and what are the key benefits of these transactions for users and developers in both web3 and web2 environments?
Zero-knowledge facilitates secure and reliable transactions by allowing one party to prove the validity of a statement to the other party without revealing any underlying information. For developers, this reduces the need for trust mechanisms or third-party intermediaries, leading to a reduction in costs.
In web3, ZKPs relieve the burden on calculations and minimize data in the chain. Computing on-chain is expensive and processing the majority of compute off-chain results in significant cost savings. In web2, zero-knowledge improves data security and privacy, enabling minimum-trust interactions without the need for intermediaries.
In what ways do ZK proofs contribute to system scalability and operational cost reduction, and can you share specific examples or use cases where these benefits are most evident?
Zero-knowledge proofs contribute to system scalability and operational cost reduction by enabling efficient verification of large computations and data sets without requiring full access to the underlying information.
This reduces the computing load on primary systems and minimizes data transfer, leading to faster processing times and lower infrastructure costs. Specific examples include zk rollups in blockchain technology, where numerous transactions are processed off-chain and only a brief proof is verified on-chain, significantly improving throughput and reducing gas costs.
In web2 environments, ZKPs are used in privacy-preserving data analytics, allowing companies to perform computations on encrypted data without compromising user privacy, reducing the need for extensive data processing and security measures.
What are the key benefits of reliable cross-chain bridges, and how does Polyhedra Network leverage these bridges to improve interoperability and security between different blockchain networks?
The main benefit is the elimination of additional third-party trust assumptions. Alternate bridges usually use a validator network or a middle chain to confirm the status of one chain and pass it to another.
With zero knowledge, this can be eliminated by simply generating a proof of the state of one chain and verifying that proof in the other chain. Removing these additional trust assumptions reduces the attack surface and minimizes trust, requiring only trust in the data and consensus of the blockchain itself.
How can integrating ZK proofs with blockchain technology improve the integrity and reliability of AI models, and what potential applications do you foresee in this intersection of technologies?
Zero-knowledge improves the integrity and reliability of AI models by providing tamper-resistant verification that a model’s training processes were performed correctly without exposing the underlying data. This gives end users confidence that models are built on reliable data, improving the user experience and increasing credibility in what is typically a very opaque area.
The fraud-resistant aspect of zero-knowledge also significantly reduces the risk of fraud. Applications with sensitive data, such as financial services or healthcare, are industries where zero-knowledge could provide immediate benefits.
Can you discuss some of the recent developments at Polyhedra Network and share any upcoming projects or innovations that you are particularly excited about? What future directions do you see for the network in the ZK space?
We are the authors of the zkBridge whitepaper and creators of www.zkbridge.com, the most widely used zero-knowledge secured bridge. We connect more than 25 blockchains and have facilitated more than 20 million cross-chain transactions.
Recently we are working on Proof Cloud, a cloud proofing platform that reduces costs and increases the efficiency of the proof generation process. It will be open to the public at the end of this month.
Finally, we open sourced our record-breaking Expander pilot system. Orders of magnitude faster than alternative proof systems, developers using Expander will see incredible improvements in both the speed and cost of proof generation.
Over the next twelve months, we plan to devote substantial resources to exploring how zero-knowledge can be applied to AI and machine learning, with a number of AI-specific developer tools and applications in the pipeline.