  This study examines privacy risks in collaborative robotics, focusing on the
potential for traffic analysis in encrypted robot communications. While
previous research has explored low-level command recovery, our work
investigates high-level motion recovery from command message sequences. We
evaluate the efficacy of traditional website fingerprinting techniques (k-FP,
KNN, and CUMUL) and their limitations in accurately identifying robotic actions
due to their inability to capture detailed temporal relationships. To address
this, we introduce a traffic classification approach using signal processing
techniques, demonstrating high accuracy in action identification and
highlighting the vulnerability of encrypted communications to privacy breaches.
Additionally, we explore defenses such as packet padding and timing
manipulation, revealing the challenges in balancing traffic analysis resistance
with network efficiency. Our findings emphasize the need for continued
development of practical defenses in robotic privacy and security.
