CosPEEPChain: Blockchain-Secured Privacy-Preserving Face Recognition Using Eigenface Perturbation and CosFace

Face recognition technology implemented on blockchain platforms enhances the security and integrity of face embeddings (the numerical representations extracted from facial images). However, it encounters unique privacy challenges due to the transparent and immutable nature of blockchains. Face embeddings hold sensitive biometric data that, once compromised, cannot be changed like conventional passwords. This study offers a new framework for using the Internet Computer Protocol (ICP), a decentralized blockchain platform, to implement CosPEEPChain (blockchain-secured privacy-preserving face recognition using eigenface perturbation and CosFace). CosPEEPChain integrates eigenface decomposition with local differential privacy (LDP) to ensure the privacy of face embeddings, CosFace for cosine margin learning’s discriminative ability on perturbed eigenface representations, and blockchain to ensure transparent and tamper-proof storage of face recognition models. We present CosPEEP (privacy-preserving face recognition using eigenface perturbation and CosFace), which shows substantial improvements and maintains consistent performance over baseline PEEP (privacy using eigenface perturbation), with a mean accuracy of 96.77 ± 0.85% and stability (std = 0.31–1.28%) across a range of privacy budgets (ϵ∈[0.5,8.0]) on the LFW dataset. Statistical significance testing confirms CosPEEP surpasses PEEP in 11/16 privacy budgets (p < 0.05) with a mean improvement of +1.92%. We also present ArcPEEP, which uses additive angular margin loss (ArcFace) to compare margin-based improvements. We verify the attributes of the models on the chain. In total, CosPEEPChain uses fewer cycles compared to the baseline ICP face recognition.