RoboHub Research: Robots are prone to privacy leaks despite encryption

A new study from the University of Waterloo has unveiled major privacy weaknesses in collaborative robots — calling for stronger defences.

In recent years, the use of robotics has become widespread in public and private spheres. Hospitals are employing robots as surgical assistants due to their high precision and dexterity, and various manufacturing firms are increasingly using robots, especially for dangerous and hazardous tasks. Not only can robots build high-quality products at a consistent and fast rate, but they can also improve workplace safety.

Despite their popularity, collaborative robots could be exploited in malicious attacks. If a hacker notices any command patterns during a procedure, they could infer sensitive patient information, such as their illness or medication schedules— even when commands are encrypted.

These privacy concerns prompted RoboHub Research Team member Prof. Yue Hu to reach out to her former co-op student, Cheng Tang, and Drs. Diogo Barradas and Urs Hengartner, computer science researchers and fellow members of the University of Waterloo’s Cybersecurity and Privacy Institute (CPI), a RoboHub Community Partner, to explore ways to address the problem collaboratively.

Ultimately, the researchers discovered that robot commands can create traffic sub-patterns, which can be detected by common signal processing techniques, particularly signal correlation and convolution. Notably, their technique identified the Kinova robot’s actions 97 per cent of the time, despite being encrypted.

These results suggest that robots could easily leak private information from industry secrets to patient confidentiality, calling for the robotic community to build better security defenses.

However, certain design choices could prevent leakage and make a system’s network steadier. Some of the researchers’ proposals include changing the system’s interface like its application programming interface (API) timing or employing a smart traffic shaping algorithm at run-time.  

This groundbreaking research earned the team the Best Research Paper Award at the 20th International Conference on Availability, Reliability and Security (ARES), one of the most reputable conferences in IT security and privacy.

The research, On the Feasibility of Fingerprinting Collaborative Robot Network Traffic, was published in the proceedings of ARES 2025.

For more details, the full article can be found on the Waterloo News website.