Framework quantifies per-layer sensitive information exposure in DNNs via generalization error and evaluates TEE-based protection for the most exposed layers against white-box membership inference.
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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.
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Towards Characterizing and Limiting Information Exposure in DNN Layers
Framework quantifies per-layer sensitive information exposure in DNNs via generalization error and evaluates TEE-based protection for the most exposed layers against white-box membership inference.
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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.