# Revisiting Privacy Preservation in Brain-Computer Interfaces: Conceptual Boundaries, Risk Pathways, and a Protection-Strength Grading Framework

arXiv: https://arxiv.org/abs/2605.11386 · HTML: https://mlsanity.com/paper/2605.11386/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 92 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 14 of 14)

- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning](https://mlsanity.com/paper/1812.00910/) (2018)
- [Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models](https://mlsanity.com/paper/2201.09370/) (2022)
- [Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning](https://mlsanity.com/paper/2106.03328/) (2021)
- [Federated Transfer Learning for EEG Signal Classification](https://mlsanity.com/paper/2004.12321/) (2020)
- [Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption](https://mlsanity.com/paper/2202.13351/) (2022)
- [A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives](https://mlsanity.com/paper/2508.15031/) (2025)
- [A Framework for Preserving Privacy and Cybersecurity in Brain-Computer Interfacing Applications](https://mlsanity.com/paper/2209.09653/) (2022)
- [Toward Practical Privacy-Preserving Convolutional Neural Networks Exploiting Fully Homomorphic Encryption](https://mlsanity.com/paper/2310.16530/) (2023)
- [Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/2411.19498/) (2024)
- [PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decoding](https://mlsanity.com/paper/2412.11390/) (2024)
- [SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfaces](https://mlsanity.com/paper/2601.05789/) (2026)
- [Cancellable Template Design for Privacy-Preserving EEG Biometric Authentication Systems](https://mlsanity.com/paper/2203.16730/) (2022)
- [Protecting Intellectual Property of EEG-based Neural Networks with Watermarking](https://mlsanity.com/paper/2502.05931/) (2025)

## Abstract (arXiv)

Brain-computer interfaces (BCIs) are moving rapidly from laboratory research into clinical, edge, and real-world settings. Under ISO/IEC 8663:2025, a BCI is a direct communication link between central nervous system activity and external software or hardware systems. This link expands privacy risk beyond raw neural-signal leakage: neural data, derived representations, model assets, and decoded outputs can be re-associated with individuals across collection, transmission, storage, training, inference, and feedback, or used to infer information beyond what a task requires. Starting from the general BCI paradigm, this review deffnes privacy-protection boundaries, protection objects, and the relationship between user data privacy and model privacy within a shared risk pathway. It then proposes a three-dimensional framework - protection object, lifecycle stage, and dominant protection-strength level - to classify existing work into four levels of protection strength. Finally, mental privacy and neuroethical risks are treated as open issues, emphasizing that BCI privacy protection should not only obscure data but also disentangle task-irrelevant sensitive information while preserving downstream utility. Keywords: Brain-computer interface, Neural data privacy, User data privacy, Model privacy, Disentanglement of task-irrelevant sensitive information, Protection-strength grading, Neuroethical risks