The paper synthesizes BCI privacy risks and introduces a three-dimensional framework that grades existing protection methods into four strength levels while flagging mental privacy as an unresolved neuroethical issue.
CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy[C]//Proceedings of the 33rd International Conference on Machine Learning Workshops
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Revisiting Privacy Preservation in Brain-Computer Interfaces: Conceptual Boundaries, Risk Pathways, and a Protection-Strength Grading Framework
The paper synthesizes BCI privacy risks and introduces a three-dimensional framework that grades existing protection methods into four strength levels while flagging mental privacy as an unresolved neuroethical issue.