X @Polyhedra

Problem Statement - Traditional ID checks are vulnerable to AI-driven forgeries, posing a significant risk to identity verification processes [1] - Centralized biometric databases present ongoing data leak risks, jeopardizing user privacy and security [1] - Regulated industries face challenges in age/identity verification without wanting to store sensitive user data, creating a compliance burden [1] Solution: Polyhedra i-D - Polyhedra i-D utilizes local inference and Zero-Knowledge Proofs (zkML) for face recognition, ensuring user data privacy [2] - The system generates cryptographic proofs instead of uploading raw photos, protecting sensitive information [2] - Service providers receive verification results without accessing underlying user data, enhancing data protection [2] - On-chain verification allows smart contracts to directly verify proofs, enabling decentralized identity management [2] Technology and Roadmap - zkML proves facial verification locally without exposing inputs, enhancing privacy [4] - Expander Prover accelerates proof generation, improving efficiency [4] - zkPyTorch turns PyTorch ML models into ZK circuits, enabling advanced applications [4] - Trusted Execution Environments (TEE) securely store biometrics in line with C2PA standards, ensuring compliance [4] - A web demo is live, with a mobile app release and SDK for third-party integrations planned [4] - Expansion into finance, age verification, and broader digital identity use cases is underway [4] Key Benefits - Users can prove legitimacy without handing over sensitive documents, enhancing privacy and control [4] - Businesses avoid the liability of storing identity data, reducing risk and compliance burdens [4] - Polyhedra i-D establishes a default login layer for the AI-native internet, combining compliance with data protection [4]