Emerging ZK Use Cases
Emerging ZK Use Cases
In this article we explore the emerging ZK use cases.
Intro
In the past few months, we have witnessed significant hype surrounding Zero Knowledge use cases, including zkEVMs, zkBridges, and more. However, new use cases are emerging in such a fast-paced environment where Zero Knowledge Proofs (ZKPs) play a crucial role. This article aims to explore the areas where ZKPs are gaining more traction in the minds of individuals.
zkML – Zero Knowledge Machine Learning
ZKML (Zero Knowledge Machine Learning) is an emerging technology that holds tremendous potential for various applications yet to be fully explored. From computational integrity and privacy preservation to transparency in ML-as-a-Service and decentralized inference or training, ZKML opens new doors for innovation. Here are some of the potential use cases for zkML:
Verifiability with ZKML: ZKML leverages validity proofs like SNARKs and STARKs to ensure the accurate execution of computations, particularly in machine learning. By employing these proofs, ML model inference can be verified, confirming the output’s origin based on a given input. This capability allows deploying machine learning models off-chain while conveniently verifying ZK proofs on-chain.
Privacy Preservation in ZKML: Privacy is a critical concern in machine learning, and ZKML offers solutions. It enables proving the accuracy of models on test data without revealing the weights used, ensuring data privacy. Furthermore, ZKML facilitates privacy-preserving inference, allowing sensitive medical diagnostics, such as cancer test results, to be shared with patients without compromising their data to third parties.
Enhancing Transparency in ML-as-a-Service: ZKML plays a vital role in improving transparency in ML-as-a-Service. By providing validity proofs associated with ML model APIs, users can verify the authenticity of the models they utilize. This addresses concerns related to traditional APIs, often perceived as black boxes, and promotes trust in the provided models.
Decentralized Inference and Training: ZKML enables the execution of machine learning tasks in a decentralized manner. The model can be compressed by leveraging zero-knowledge proofs, allowing for public participation and data submission during inference or training processes. This opens up new avenues for collaborative and distributed machine learning.
Attestation with ZKML: Attestation is an essential aspect of ZKML, allowing for the incorporation of verifiable attestations from trusted external parties. By utilizing zero-knowledge proofs to verify digital signatures from reliable sources, ZKML ensures the authenticity and provenance of attested information. This enables the verification and utilization of trusted references in various applications.
ZK in Gaming
Zero Knowledge Proofs (ZKPs) offer numerous advantages for in-game architecture and gameplay experiences. By integrating ZKPs, game developers can benefit from reduced costs, enhanced privacy protection, efficient anti-cheating measures, and improved scalability.
In terms of game architecture, ZKPs significantly impact online multiplayer games by minimizing sensitive data stored on centralized servers, resulting in lower expenses for maintaining client-server architectures. Additionally, ZKPs strengthen privacy and security in Peer to Peer (P2P) gaming systems, eliminating the need for expensive servers while ensuring the authenticity of in-game events and preventing cheating.
ZKPs also address the challenge of game scalability by reducing server overhead in client-server architectures and enabling verifiable proofs of game actions in P2P multiplayer games. This expands the number of concurrent players that can be supported, leading to more enjoyable gaming experiences and attracting a more extensive player base.
In terms of gameplay, ZKPs offer solutions for various game genres. In First Person Shooters (FPS), ZKPs can verify players’ skill levels without revealing sensitive information, thus improving matchmaking accuracy. In Massively Multiplayer Online Role-Playing Games (MMORPG), ZKPs enable secure ownership and exchange of virtual assets, creating a unified economy across different games. ZKPs also find applications in gambling and casino games, providing verifiably random results and protecting players’ privacy. Furthermore, ZKPs can secure players’ progress and high scores in puzzle and strategy games while keeping sensitive information private.
Adopting ZKPs in the gaming industry presents opportunities for enhanced gaming experiences, increased player engagement, and higher revenue. As the use of ZKPs expands, specialized hardware may become essential to meet the growing demand for computing power.
ZK ID – Decentralising ID
Establishing digital trust and secure identity verification is crucial in today’s online landscape. The emergence of zero-knowledge proofs (ZKPs) has revolutionized this space by offering heightened privacy and security. Here, we will explore the properties and advantages of zero-knowledge proofs and their potential to redefine the verification of digital identities.
• Enhanced Privacy Protection: Zero-knowledge proofs excel in safeguarding sensitive information. Users can verify their identities or credentials without disclosing specific details such as personal identification numbers or addresses. ZKPs establish a secure framework that allows trustless interactions while preserving confidentiality.
• Secure and Verifiable Interactions: Zero-knowledge proofs empower users to engage with smart contracts and verify information on the blockchain while keeping their private data hidden. Off-chain credentials like passports or educational degrees can generate zero-knowledge proofs. These proofs can then be used for trustless verifications on the blockchain, ensuring data integrity while maintaining privacy.
Lately, Polygon has introduced Polygon ID, a zero-knowledge decentralized identity solution. Polygon ID harnesses the power of zero-knowledge proofs, allowing users to verify their identities without compromising sensitive information. It offers a comprehensive toolset, including the Verifier SDK, Issuer Node, Wallet SDK, and Wallet App, empowering developers to integrate decentralized identity solutions into their applications seamlessly.
Conclusion
To sum up, the diversity of areas ZK sees as having a positive impact shows the potential of the technology to bring mass adoption to the blockchain industry. Indeed, most of these applications – zkML, ZK gaming, ZK ID – remain in their nascent stages, some still largely in the realm of theory. Nevertheless, it’s only a matter of time before we begin seeing more activity and products coming from the zkML, the ZK gaming or the ZK ID space. As these fields mature, we anticipate a vibrant ecosystem of ZK-based applications that will redefine our notions of privacy, security, and verifiability.
