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Marivi Higuero

Publications and source records attributed to Marivi Higuero.

3 recordsLinked to original sources

Quantum Homomorphic Encryption: Towards Practical QOTP-Encrypted Computation on Gate-Based Hardware

As quantum computing moves toward remote and cloud-based access, protecting quantum data during outsourced execution becomes important. Quantum one-time pad (QOTP) encryption provides information-theoretic hiding of quantum state, but evaluating gates on Pauli-encrypted data is nontrivial: Clifford operations allow classical Pauli-frame updates, whereas non-Clifford operations require key-dependent corrections. We introduce QOTPH, a client-side key-tracked compilation framework for encrypted quantum computation. The client retains the QOTP keys and transforms each operation into a corrected gate or decomposition. The resulting circuit is executed non-adaptively by the quantum server, which neither receives the keys nor performs key updates. We develop a unified Pauli frame compilation layer spanning Clifford operations, one- and two-qubit Pauli-generated rotations, explicit key-dependent parametrized controlled rotations, and recursively composed gates, and establish a global correctness result for circuits composed of the formally verified primitive rules. We also characterize the evaluator's view under a hidden logical circuit model, in which the QOTP frame, the uncompiled logical circuit, and the logical-to-physical instruction mapping remain client-side; the security of this model against various algorithms is also analyzed. The implementation is evaluated in Qiskit using noiseless simulation and IBM Quantum hardware, including in-circuit and local-decryption workflows. This work bridges the gap between algebraic QOTP key tracking and practical encrypted quantum computation by providing a unified, hardware-validated compilation framework for current gate-based quantum processors.

quant-ph

Sistema de Reconocimiento Facial Federado en Conjuntos Abiertos basado en OpenMax

Facial recognition powered by Artificial Intelligence has achieved high accuracy in specific scenarios and applications. Nevertheless, it faces significant challenges regarding privacy and identity management, particularly when unknown individuals appear in the operational context. This paper presents the design, implementation, and evaluation of a facial recognition system within a federated learning framework tailored to open-set scenarios. The proposed approach integrates the OpenMax algorithm into federated learning, leveraging the exchange of mean activation vectors and local distance measures to reliably distinguish between known and unknown subjects. Experimental results validate the effectiveness of the proposed solution, demonstrating its potential for enhancing privacy-aware and robust facial recognition in distributed environments. -- El reconocimiento facial impulsado por Inteligencia Artificial ha demostrado una alta precisión en algunos escenarios y aplicaciones. Sin embargo, presenta desafíos relacionados con la privacidad y la identificación de personas, especialmente considerando que pueden aparecer sujetos desconocidos para el sistema que lo implementa. En este trabajo, se propone el diseño, implementación y evaluación de un sistema de reconocimiento facial en un escenario de aprendizaje federado, orientado a conjuntos abiertos. Concretamente, se diseña una solución basada en el algoritmo OpenMax para escenarios de aprendizaje federado. La propuesta emplea el intercambio de los vectores de activación promedio y distancias locales para identificar de manera eficaz tanto personas conocidas como desconocidas. Los experimentos realizados demuestran la implementación efectiva de la solución propuesta.

cs.CV

Towards integrating hardware Data Plane acceleration in Network Functions Virtualization

This paper proposes a framework for integrating data plane (DP) acceleration within the Network Functions Virtualization (NFV) architecture. Data plane programming (DPP) proves to be beneficial for NFV environments, as it provides full packet forwarding flexibility through the use of self-designed algorithms. Additionally, DPP provides high-performance networking, as the DP can be configured to execute specific functions on dedicated hardware. We present an integration of the DP acceleration within the ETSI NFV architecture that leverages custom DP functions implemented in hardware switches using P4 language. Besides, OpenStack and Kubernetes are used as Virtualized Infrastructure Managers (VIMs) and Open Source MANO (OSM) as the Management and Orchestration (MANO) element.

cs.NI