Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
Ciphera, a decentralized biometric identity framework, was developed to address the privacy and security limitations of centralized systems by integrating facial recognition, multi-node verification, IPFS, and blockchain. The framework achieved an 81% functional success rate with sub-second verification latency, demonstrating the potential of decentralized biometric identity. However, the evaluation also revealed challenges related to revocation propagation delays, audit-log inconsistencies, and susceptibility to deepfake and replay attacks, highlighting areas for improvement.
Decentralizing biometric identity is possible, but current implementations still struggle with revocation delays and deepfake attacks.
Centralised biometric identity systems expose users to single points of failure, opaque verification processes, and irreversible biometric compromise. Decentralised Identifiers (DIDs) and Verifiable Credentials (VCs) offer stronger privacy guarantees, yet their integration with biometric authentication and distributed verification remains insufficiently explored. This paper presents Ciphera, a decentralised biometric identity framework combining privacy-preserving facial recognition, multi-node verification, IPFS-based credential metadata storage, and blockchain-anchored revocation. Evaluated across functional, performance, security, and distributed consistency dimensions, Ciphera achieved an 81% functional success rate, with stable enrolment and authentication but measurable revocation propagation delays and occasional audit-log inconsistencies. Performance testing demonstrated sub-second p95 verification latency of approximately 820ms under concurrent multi-node conditions. Security analysis confirmed strong confidentiality and integrity guarantees, though incomplete liveness detection leaves susceptibility to deepfake and replay attacks. The results demonstrate the feasibility of decentralised biometric identity while identifying key engineering challenges for production-grade deployment.