A sensitivity-aware privacy budget selection framework for EEG data under local differential privacy

Protecting sensitive biomedical data is a critical security challenge in decentralized healthcare environments without a trusted authority. Electroencephalography (EEG) signals constitute highly sensitive biometric data used for neurological disorder diagnosis, making privacy-preserving analysis essential. Local differential privacy (LDP) addresses this challenge by perturbing data at the source; however, selecting an appropriate privacy budget ε remains nontrivial, as it directly governs the privacy–utility trade-off. This work proposes a sensitivity-aware privacy budget selection framework for EEG-based analysis under LDP, leveraging multi-criteria decision making to jointly consider privacy level, data accuracy, computational cost, and data sensitivity. Unlike existing approaches that rely on a single global privacy budget, the proposed framework performs participant-specific privacy budget selection, accounting for differences in data sensitivity across participating data sources. Privacy budgets are adaptively selected using TOPSIS and Fuzzy TOPSIS, enabling sensitivity-aware calibration without modifying the underlying LDP mechanism. Experiments conducted on the University of Bonn EEG dataset employ time-domain, frequency-domain, nonlinear, and wavelet features, with eight machine learning classifiers evaluated on privacy-preserving data against a noise-free baseline. The results show that Fuzzy TOPSIS consistently selects smaller privacy budgets, achieving stronger privacy guarantees while maintaining a favorable balance between privacy protection and classification performance. Performance remains stable across classifiers, and the additional computational overhead is modest. Overall, the findings demonstrate that sensitivity-aware privacy budget selection enables effective privacy–utility trade-offs for EEG analysis, offering a scalable and ethically grounded solution for privacy-preserving biomedical signal processing in decentralized healthcare systems.