Examinando por Autor "Daniel Palacios-Alonso"

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Partially homomorphic framework for secure privacy-preserving ID creation
(Sage, 2025-05-21) Nikola Hristov-Kalamov; Raúl Fernández-Ruiz; Cristina Conde; Agustín Álvarez-Marquina; Francisco Domínguez-Mateos; Pedro Gómez-Vilda; Daniel Palacios-Alonso
Homomorphic encryption has seen limited application in real-world settings due to its high computational costs, which often impede practical use in latency-sensitive scenarios. Addressing this challenge, this research work presents an efficient, partially homomorphic encryption framework for privacy-preserving ID creation tailored to biometric applications, such as border control or access to secure restricted facilities. The proposed solution leverages the additive homomorphic properties of Paillier and Elliptic Curve ElGamal encryption schemes to encapsulate biometric data within a secure cryptographic layer, enabling rapid verification while minimizing computation and storage demands. Paillier encryption ensures robust security with maximal accuracy, while Elliptic Curve ElGamal optimizes for minimal ciphertext sizes, both meeting rigorous ISO biometric security standards. Experimental results demonstrate that the framework achieves high accuracy with reduced memory and bandwidth requirements, with encrypted IDs as compact as 4 KiB, making it suitable for scalable deployment. This research work represents a key advancement in homomorphic encryption applications, balancing privacy and efficiency without the usual overhead, and making it feasible for real-time biometric processing. In summary, this framework offers a pioneering solution in secure biometric verification, setting a new standard for privacy-preserving applications, positioning it as a promising model for future secure identification systems.