Ahmed Harbaoui

Papers

Total Citations

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About

Ahmed Harbaoui is a researcher at the forefront of privacy-preserving machine learning and cloud robotics, with a focused expertise in securing sensitive data within real-time autonomous systems. His most-cited work, "Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study" (2021, 12 citations), addresses a critical bottleneck in modern robotics: the trade-off between computational speed and data security. Harbaoui systematically evaluates encryption algorithms that allow robots to offload face recognition tasks to cloud servers without exposing raw biometric data to potential attacks. This contribution is pivotal for deploying robots in sensitive environments like healthcare and smart homes, where privacy is non-negotiable. Beyond this study, his research bridges the gap between resource-constrained edge devices and scalable cloud infrastructure, proposing frameworks that maintain real-time performance while ensuring end-to-end encryption. With a growing citation footprint, Harbaoui’s work is shaping the next generation of secure, cloud-connected autonomous agents. His achievements highlight a rare ability to synthesize practical robotics challenges with rigorous cryptographic principles, making him a key voice in the emerging field of privacy-aware AI.