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As collaborative robots become more common across healthcare and industrial environments, new research highlights a critical gap in how these systems handle privacy—even when data is encrypted.
A recent study from the University of Waterloo has demonstrated that encrypted communication between a robot and its controller can still be analyzed to infer sensitive information. The findings raise concerns for sectors relying on robots in sensitive roles, such as hospitals, manufacturing lines, and critical infrastructure.
The research focused on script-based robots, which execute pre-programmed tasks without continuous human input. While previous studies explored risks in teleoperation systems, this study examined how command traffic alone—without access to the command content—can still reveal operational details.
Using a Kinova Gen3 robotic arm, the team captured 200 sets of network data during four distinct robotic actions. The researchers applied signal processing techniques, commonly used in audio and communication technologies, to analyze traffic patterns.
According to TechXplore, they found that characteristics such as message timing, frequency, and duration can be correlated with specific robot actions—even when the communication is encrypted. Their method achieved a 97% accuracy rate in identifying tasks, simply by analyzing the traffic’s structure.
This means that a malicious actor monitoring network traffic could infer sensitive operations, such as a patient’s treatment procedure or a factory’s production process, without ever breaking encryption.
The researchers proposed several mitigation strategies, including modifying API timing and implementing smart traffic-shaping algorithms to obscure these patterns during operation. Such measures could make traffic behavior less predictable and reduce the risk of indirect information leakage.
The study underscores the need for stronger privacy protections in robotic systems as they become increasingly network-connected. Without adequate safeguards, even systems using standard encryption protocols may unintentionally expose private or proprietary information.
The research, titled On the Feasibility of Fingerprinting Collaborative Robot Network Traffic, was presented at the 2025 ARES Conference, where it received the Best Research Paper Award. The findings are published in Lecture Notes in Computer Science.
