Over the years, there has been an increase in the use of wearable sensors for high-precision Human Activity Recognition (HAR), ranging from personal fitness to remote patient monitoring. However, the continuous collection of biometric information poses a privacy risk, potentially leading to user profiling and data misuse. Today's state-of-the-art approaches use standard encryption techniques to protect data in transit but leave it vulnerable during computation. For accurate HAR while maintaining the privacy of users’ data during collaborative analysis, we propose a hybrid, domain-agnostic framework that integrates Homomorphic Encryption (HE) with Secure Multi-Party Computation (SMPC). The proposed approach enables healthcare providers and device manufacturers to collaboratively analyze data without ever disclosing raw biometrics by utilizing HE for data encryption and secret sharing for collaborative computation. While this framework applies to general HAR scenarios, it will be crucially useful in the high-stakes domain of eldercare, where data privacy and regulatory compliance are critical. Reliable, Privacy-Aware Human Activity Sensing (RAHAS) achieved \ (89. 24 0. 95\%\) accuracy when tested on the widely used PAMAP2 dataset for HAR. Our analysis demonstrates that our privacy-preserving design provides side-channel resilience while maintaining utility comparable to clear-text baselines.
Aryendu et al. (Tue,) studied this question.