Verifiable Privacy-Preserving Heart Rate Estimation Based on LSTM

Remote heart rate (HR) estimation via facial videos has emerged as an attractive application for healthcare monitoring. Long short-term memory (LSTM) is a kind of deep recurrent neural network architecture used for modeling sequential information, which can be utilized for remote HR estimation. Outsourcing computation is an emerging paradigm for enterprises or individuals with a huge volume of private data but limited computing power for data modeling and analysis, but the risks of privacy leakage cannot be ignored. Prior privacy-preserving LSTM-based protocols only protect sensitive data or model parameters, or approximate nonlinear functions with somewhat utility degradation. To mitigate the aforementioned issues, we propose a verifiable outsourcing protocol of LSTM training for HR estimation (VOLHR) based on batch homomorphic encryption, which not only ensures the confidentiality of the local data and model parameters but also guarantees the verifiability of the model predictions to resist the deceptive attacks. Theoretical analysis proves that VOLHR provides a higher level of privacy protection than the prior works. Computational cost analysis demonstrates that VOLHR can reduce the computational overhead greatly compared with the original LSTM models with different structures. To exhibit the practical utility, we implement VOLHR on two real-world data sets for HR estimation. Extensive evaluations demonstrate that VOLHR achieves almost the same performance as the original LSTM model and outperforms prior related protocols.