Biblio

This paper proposes a novel approach for privacy preserving face recognition aimed to formally define a trade-off optimization criterion between data privacy and algorithm accuracy. In our methodology, real world face images are anonymized with Gaussian blurring for privacy preservation. The anonymized images are processed for face detection, face alignment, face representation, and face verification. The proposed methodology has been validated with a set of experiments on a well known dataset and three face recognition classifiers. The results demonstrate the effectiveness of our approach to correctly verify face images with different levels of privacy and results accuracy, and to maximize privacy with the least negative impact on face detection and face verification accuracy.

In-Network Computing is a promising field that can be explored to leverage programmable network devices to offload computing towards the edge of the network. This has created great interest in supporting a wide range of network functionality in the data plane. Considering a networked robotics domain, this brings new opportunities to tackle the communication latency challenges. However, this approach opens a room for hardware-level exploits, with the possibility to add a malicious code to the network device in a hidden fashion, compromising the entire communication in the robotic facilities. In this work, we expose vulnerabilities that are exploitable in the most widely used flexible framework for writing robot software, Robot Operating System (ROS). We focus on ROS protocol crossing a programmable SmartNIC as a use case for In-Network Hijacking and In-Network Replay attacks, that can be easily implemented using the P4 language, exposing security vulnerabilities for hackers to take control of the robots or simply breaking the entire system.