An encryption framework for neurocognitive enhancement Brain Computer interfaces to secure neural data without compromising users’ cognitive autonomy or control over data access.

Brain-Computer Interfaces (BCIs) tailored for neurocognitive enhancement offer unprecedented opportunities for augmenting human cognitive abilities, such as memory, attention, and learning capabilities. However, this emerging technology raises profound concerns regarding privacy and security, given its potential to access and manipulate highly sensitive cognitive and neural data. To address these critical vulnerabilities, this paper proposes a comprehensive encryption framework specifically designed for neurocognitive enhancement BCIs. We systematically identify privacy and security threats unique to these interfaces, emphasizing risks associated with unauthorized cognitive inference, neural data interception, and malicious cognitive manipulation. Our framework integrates advanced encryption techniques, including homomorphic encryption, secure multi-party computation, differential privacy, and encrypted federated learning, to ensure robust and privacypreserving neural data handling. By embedding these encryption mechanisms into the BCI lifecycle, from data acquisition to storage and real-time processing, our approach aims to protect user autonomy, cognitive integrity, and informational self-determination, laying the foundation for secure and ethically responsible neurocognitive enhancement technologies.