TL;DR
A integrated focus on privacy-preserving dyslexia detection using QEEG features is integrated, and explainability in the encrypted domain is achieved via a perturbation-based SHapley Additive exPlanations (SHAP)-like approximation.
This paper suggests an effective privacy-preserving strategy for patient healthcare data from IoT devices in order to anticipate diseases in the contemporary medical field and achieves 93.47% accuracy, 6.53% error, and 93.45% precision in encrypted data performance metrics.
FHEON is presented, an open-source configurable framework for developing privacy-preserving neural network models for inference using the CKKS scheme of HE, and outperform all state-of-the-art HE inference works in both latency and memory utilization.
This work proposes a practical non-interactive encrypted retrieval framework for RAG based on threshold selection, and introduces a precision-stable mask polarization method that ensures accurate recovery of selected documents.
The overall performance of the linear regression model improved for encrypted data, and the error is also reduced while maintaining data privacy.
This work designs a secure multiparty deep learning system that enables privacy-preserving modeling from distributed medical time-series data without centralizing raw information or exposing model parameters, and successfully bridges the utility-privacy gap.
Experimental results indicate that TMI-VFL achieves an effective trade-off between privacy protection and model utility, providing a practical solution for secure VFL.
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