Idiobionics: The Unification of Privacy and Intelligent Robotic Prostheses

As robotic prostheses become increasingly sophisticated, they rely on advanced sensors and machine learning to interpret user intent and adapt to changing environments. While this integration improves functionality, it creates a 'tight coupling' between the human body and digital systems. The authors argue that this integration introduces significant privacy risks, as the same data used to control the limb can be exploited to infer sensitive personal information, such as daily routines, health status, and demographic attributes.

The paper defines 'idiobionics' as a dedicated, interdisciplinary research field focused on protecting privacy in autonomous and adaptive bionic limbs. The authors contend that because these devices are often medically necessary and difficult to replace, they require a 'privacy-by-design' approach. By formalizing this field, the researchers aim to help developers build trust, ensure regulatory compliance, and prevent malicious entities from exploiting the data streams inherent to modern bionic technology.

To demonstrate the necessity of this research, the authors conducted an 'Activity Inference Attack' (AIA). Using a tri-axial accelerometer placed on the forearm, they collected movement data from twelve participants performing four activities: walking, jogging, sitting, and standing. By applying transfer learning with a pre-trained model, they achieved an average prediction accuracy of 83%. The study further showed that clustering algorithms could group these activities effectively, and that even consumer-grade devices like smartphones could be used to replicate these results, highlighting the accessibility of such attacks for potential adversaries.

The authors suggest that these privacy risks will likely scale with the complexity of future bionic systems. They identify several potential threat vectors, including the deduction of health status, age-related gait patterns, and even the potential for speech-related privacy attacks via vibration-sensitive sensors. The paper concludes by calling for a collaborative effort between researchers, users, and stakeholders to develop robust, privacy-preserving architectures for the next generation of wearable robotics.