אנונימיזציה דיפרנציאלית של תכונות EEG: מחקר קזואי על פריווילגיות פרטיות בקליניקה

תקציר מקורי באנגליתarXiv:2609.11777v1 Announce Type: cross Abstract: Clinical electroencephalography (EEG) data are valuable for healthcare research and for developing artificial intelligence (AI)-based clinical decision-support systems, but EEG recordings and derived features may contain sensitive patient-specific information. This creates privacy risks when data are reused, analyzed, or shared across clinical and research environments. Conventional anonymization methods are often insufficient for high-dimensional biomedical signals, since removing direct identifiers does not necessarily prevent re-identification, linkage, or inference risks. At the same time, strong privacy protection may distort clinically relevant signal characteristics and reduce data utility. This paper studies subject-level differenti