“Mastering ZoKrates: Ethereum’s Privacy Toolkit” — Part 3
A pat on your back to all of you who have made it to Part-3 so far. Till now we have covered the basics, features, trusted setup, workflow, deployment, writing code, etc. In Part 3 of the series, we will discuss Challenges, Future Developments, and Conclusion.
Note: To make the best out of this article, feel free to browse the contents and skip the section if you’re already familiarized with the same.
Check the earlier part of this series if you haven’t:
Part 3 of the series Mastering ZoKrates will include:
- Potential challenges and limitations of using ZoKrates
- Upcoming improvements or extensions to ZoKrates and the zkSNARK ecosystem
- Conclusion, summarizing the series, and emphasizing the significance of ZoKrates
- Next steps, Additional resources.
Potential challenges and limitations of using ZoKrates:
This section of the article will try to point out a few challenges, comparisons, or general considerations with ZoKrates. Feel free to add more or comment if you have a different opinion; these are based on a few tests and papers I’ll link below.
Before we jump into the points of discussion, please be aware that it is possible to port the verifier of ZoKrates to different programming languages. ZoKrates provides support for exporting the verifier code in multiple languages, including Rust,Solidity, Python, and C++. This allows users to integrate the verifier functionality into their preferred programming environment.
- Performance Variability: The performance of ZoKrates can vary depending on the specific implementation and the complexity of the computations involved. Python version of ZoKrates tends to be slower compared to the Solidity and C++ versions. The generation of circuits and proofs can also take several seconds, which may impact overall efficiency.
- Proof and Key Sizes: ZoKrates has the potential to generate large proving and verification keys, particularly for more complex computations. The size of the proving key can increase linearly with the number of gates, resulting in larger file sizes. Similarly, the size of proofs generated by ZoKrates can also be significant, potentially reaching multiple megabytes.
- Limited Scalability: While ZoKrates demonstrates good scalability in terms of verification time, as it can be done quickly, the generation of proofs may be relatively slow. However, this limitation is mitigated by the fact that proof generation is typically a one-time process per transaction, while verifications occur more frequently.
—- Few other considerations while working with ZoKrates:
- Vulnerabilities in Trusted Setup:
-The risk of malicious actors compromising the keys generated during the trusted setup process poses a significant threat to the integrity of the system.
- If the keys are compromised, fake proofs can be created, leading to false validations and potentially compromising the privacy and security of the application.
- Mitigating these vulnerabilities requires the implementation of robust security measures, such as utilizing multi-party computation (MPC) protocols and conducting thorough audits.
- While these measures help enhance security, the potential for vulnerabilities still exists, requiring ongoing vigilance and monitoring.
- Limitations in Complex Computations and Circuit Size:
- The limitations in handling complex computations and large circuit sizes can impact the practicality and efficiency of using ZoKrates in certain scenarios.
- Complex computations may require additional computational resources and time, potentially affecting the overall performance of ZoKrates.
- Large circuit sizes can result in increased memory requirements, longer proof generation times, and larger proof sizes, impacting the scalability and efficiency of the system.
- Careful evaluation of computational requirements and circuit size is necessary to ensure ZoKrates is suitable for a given application and to explore alternative approaches if needed.
Links and references for the above, if you want to explore more:
- pycrypto: https://github.com/Zokrates/pycrypto
- libsnark: https://github.com/Zokrates/libsnark
- bellman: https://github.com/zkcrypto/bellman
- Article with findings: https://coders-errand.com/practical-zk-snarks-for-ethereum/
- Compare SNARKs representation: https://github.com/sCrypt-Inc/awesome-zero-knowledge-proofs/blob/master/images/compare_snarks.jpeg
Upcoming improvements or extensions to ZoKrates and the zkSNARK ecosystem:
- Experimental features: ZoKrates supports the
novaproof system using thebellpersonbackend. Nova is accessed with the subcommandnova.
Read here to know more. - Performance Optimization: Developers are continuously working on optimizing the performance of ZoKrates and the underlying zkSNARK ecosystem. For example, recent improvements have led to a significant reduction in proof generation times, with some cases witnessing up to a 10x speed improvement compared to earlier versions.
Some of the specific techniques that have been used to improve performance include:
Circuit optimization: This involves identifying and removing redundant or unnecessary operations from the circuit. This can be done manually or automatically using specialized tools.
Compiler optimization: The compiler is responsible for translating the high-level ZoKrates language into the low-level language that is used to generate the proof. The compiler can be optimized to generate more efficient code by using techniques such as loop unrolling and function inlining.
Cryptographic optimizations: Certain cryptographic primitives, such as elliptic curve multiplication, can be implemented more efficiently using specialized hardware or software libraries. By using these optimizations, developers can significantly reduce the time it takes to generate proofs.
- Support for More Complex Computations: ZoKrates is evolving to handle more complex computations, allowing developers to tackle a broader range of privacy-preserving applications. The language and tooling are being enhanced to support advanced mathematical operations, such as:
Arithmetic on elliptic curves
Polynomial computations
Advanced cryptographic constructions
These advancements enable developers to build sophisticated privacy-preserving applications, including:
Complex financial transactions
Advanced authentication systems
Secure data analysis
- Scalability Enhancements: Scalability is a key focus area in the development of ZoKrates and the zkSNARK ecosystem. Efforts are being made to address scalability challenges, such as reducing the size of generated proofs and verification keys. Recent advancements have led to significant improvements, resulting in proofs that are up to 90% smaller in size compared to earlier versions. Additionally, research is being conducted on alternative proof systems like STARKs, which offer improved scalability by eliminating the need for a trusted setup.
- Usability and Developer Experience: Enhancing the usability and developer experience of ZoKrates is a priority for the development community. User-friendly features, intuitive documentation, and improved tooling are being developed to simplify the process of writing, compiling, and deploying zero-knowledge proof programs. Additionally, comprehensive tutorials and code samples are being created to guide developers in utilizing ZoKrates effectively, even without extensive background knowledge in cryptography or zero-knowledge proofs.
- Interoperability and Standardization: Collaborative efforts are underway to establish interoperability standards and best practices within the zkSNARK ecosystem. Common interfaces, protocols, and formats are being defined to facilitate seamless integration between different tools, libraries, and platforms. This standardization aims to promote compatibility, encourage collaboration among developers and researchers, and drive the widespread adoption of zkSNARK technologies.
- Research and Innovation: The zkSNARK ecosystem is a vibrant space with active research and innovation. Academics, researchers, and industry experts are continuously exploring new techniques, protocols, and cryptographic primitives to enhance the security, efficiency, and applicability of zkSNARKs. Recent advancements include novel zero-knowledge proof systems, improved trusted setup protocols with reduced setup complexity, and advancements in secure multi-party computation to enhance privacy in distributed settings.
Conclusion, summarizing the series, and emphasizing the significance of ZoKrates:
Throughout the series “Mastering ZoKrates: Ethereum’s Privacy Toolkit,” we explored the fundamentals, features, and implementation of ZoKrates, a powerful tool for integrating zero-knowledge proofs (zkSNARKs). Here is a detailed summary of the key points covered in each blog:
Blog 1: Introduction to zkSNARKs and ZoKrates
- Introduced the concept of zkSNARKs, highlighting their ability to validate computations without revealing sensitive information.
- Explained the properties of zkSNARKs, including zero-knowledge, succinctness, and non-interactivity.
- Broke down the four main steps of zkSNARKs: encoding as a polynomial problem, succinctness by random sampling, homomorphic encoding/encryption, and zero knowledge.
- Introduced ZoKrates as a comprehensive solution for integrating zkSNARKs into DApps, simplifying the process from program definition to proof generation and verification.
- Discussed the motivation behind using ZoKrates to enhance privacy on the Ethereum network.
Blog 2: Trusted Setup in ZoKrates and Workflow
- Explored the concept of trusted setup in zero-knowledge proofs, emphasizing its role in generating cryptographic keys for proof generation and verification.
- Highlighted the need for trusted setup to achieve succinct proofs and maintain the integrity of the system.
- Described the components and steps involved in the trusted setup process in ZoKrates, including the trusted setup ceremony, toxic waste generator, toxic waste storage, proving and verification keys, and proving and verification key distributor.
- Provided an analogy of creating a unique key for a lock to illustrate the concept of trusted setup.
- Detailed the process of performing trusted setup in ZoKrates, including compiling the circuit, initializing a phase 2 ceremony, making contributions, applying a random beacon, verifying contributions, and exporting the proving and verification keys.
- Explored the usage of ZoKrates in Remix IDE, ZoKrates Playground, local installation, and Docker container.
Blog 3: Challenges, Future Developments, and Conclusion:
- Discussed potential challenges and limitations of using ZoKrates, such as performance variability, proof and key sizes, and limited scalability.
- Highlighted considerations regarding vulnerabilities in trusted setup and limitations in handling complex computations and large circuit sizes.
- Provided references and links for further exploration and research on these topics.
- Explored upcoming improvements and extensions to ZoKrates and the zkSNARK ecosystem, including experimental features, performance optimization, support for more complex computations, scalability enhancements, usability and developer experience improvements, interoperability and standardization efforts, and ongoing research and innovation.
- Emphasized that ZoKrates is a powerful privacy toolkit with continuous advancements to address challenges and expand its capabilities.
Conclusion: ZoKrates is a valuable tool for developers seeking to enhance privacy and security in their Ethereum-based DApps through the integration of zkSNARKs. By mastering ZoKrates, developers gain the ability to validate computations without revealing sensitive information, ensuring privacy and integrity. Throughout the series, we have explored the fundamentals of zkSNARKs, the features and workflow of ZoKrates, the importance of trusted setup, and the potential challenges and future developments in the ZoKrates and zkSNARK ecosystem.
As with any technology, there are challenges and limitations to consider, such as performance variability, proof and key sizes, and the need for careful handling of trusted setup. However, ongoing improvements and extensions to ZoKrates are addressing these challenges, focusing on performance optimization, support for complex computations, scalability enhancements, usability improvements, interoperability
ZoKrates holds significant importance in the realm of blockchain technology and decentralized applications due to the following reasons:
- Privacy Preservation: ZoKrates enables developers to implement zero-knowledge proofs, ensuring privacy by proving possession of information without revealing it.
- Trust and Security: The trusted setup in ZoKrates establishes trust and security by generating cryptographic keys and disposing of toxic waste, enhancing the integrity of zero-knowledge proofs.
- Simplicity and Developer-Friendly Approach: ZoKrates simplifies the implementation of zero-knowledge proofs through a user-friendly interface and high-level language, making it accessible for developers.
- Enhanced Efficiency: ZoKrates provides efficient validation of computations with small proof sizes and streamlined verification processes, balancing privacy and computational overhead.
- Extensibility and Future Developments: ZoKrates continuously improves performance, supports complex computations, enhances scalability, and promotes usability, interoperability, and innovation within the zkSNARK ecosystem.
Next steps, Additional resources:
Books:
- Proofs, Arguments, and Zero-Knowledge (Justin Thaler, 2022)
- A Graduate Course in Applied Cryptography (Dan Boneh and Victor Shoup, 2020)
- The MoonMath Manual to zk-SNARKs: minimal experience in cryptography required
Applications and Use case:
- Zcash: Privacy-Protecting Digital Currency (SNARKs)
- Mina Protocol: A Constant-Size Blockchain (recursive SNARKs)
- Incomplete Information Games on Bitcoin
- Dark Forest: zkSNARK space warfare strategy game
- zkPhoto: Private Authentic Photo Sharing
- Semaphore: a privacy gadget built on Ethereum
- ZKU-Vote: a platform that allows anonymous voting
Explaining SNARKs
- Part I: Homomorphic Hidings
- Part II: Blind Evaluation of Polynomials
- Part III: The Knowledge of Coefficient Test and Assumption
- Part IV: How to make Blind Evaluation of Polynomials Verifiable
- Part V: From Computations to Polynomials
- Part VI: The Pinocchio Protocol
- Part VII: Pairings of Elliptic Curves
