ZippyChain integrates advanced cryptographic methods to address privacy concerns in blockchain technology.
Zero-Knowledge Rollups (ZK-Rollups) are utilized to batch multiple transactions off-chain and post a single ZK proof to the main chain without revealing internal data.
ZippyChain leverages ZK-Rollups, Decentralized Identifiers (DIDs), and Federated Learning to improve privacy in decentralized applications.
DIDs enable selective disclosure of identity attributes, empowering users with control over their identity.
Federated Learning allows devices to train shared machine learning models without sharing raw data, enhancing privacy in sectors like healthcare and finance.
The combination of ZK-Rollups, DIDs, and Federated Learning enables verifiable, decentralized AI applications.
ZippyChain's approach emphasizes privacy, scalability, and interoperability as core components of decentralized technology.
ZippyChain provides developer tools for creating privacy-focused applications.
ZippyChain aims to enable dApp development, identity management, and AI model deployment without compromising user privacy.