This paper surveys privacy risks in brain-computer interfaces (BCIs), arguing that privacy extends beyond raw neural signal leakage to include derived representations, model parameters, decoded outputs, and re-identification risks across the entire data lifecycle (collection, transmission, storage, training, inference, feedback). The authors propose a three-dimensional framework to classify existing BCI privacy protections by protection object, lifecycle stage, and protection strength (four levels). They emphasize that BCI privacy should not just obscure data but also disentangle task-irrelevant sensitive information while preserving task utility, and they flag mental privacy and neuroethical risks as open challenges.
Main takeaways:
- BCI privacy risk isn't just about raw neural signals — it includes derived features, model assets, decoded outputs, and re-identification across the full system lifecycle.
- The paper defines protection boundaries, objects (user data vs. model privacy), and a shared risk pathway linking both.
- They propose a three-dimensional grading framework: protection object × lifecycle stage × protection strength (four levels).
- Effective BCI privacy should disentangle task-irrelevant sensitive information while preserving downstream task performance, not just encrypt or anonymize.
- Mental privacy and neuroethical risks remain open issues beyond current technical protections.