SparseHE: An Efficient Privacy-Preserving Biomedical Prediction Approach Using Sparse Homomorphic Encryption

Although the emergence of massive data facilitates the applications of machine learning (ML) in precise biomedical-related prediction, there are growing concerns about the privacy and security of not only data but also ML models. It is still of urgent need to develop strategies to preserve privacy and protect the information security of data and models. Homomorphic encryption (HE) is one strategy that protects privacy of data and ML models by enabling direct computation over encrypted data, yet the computation burden of HE hinders its applications and the pre-processing involved in previous HE-based tasks poses the model to be vulnerable to the risk of membership inference attack. In this regard, we propose an integrated algorithm named Membership Inference Perturbation (MIP) - Sparse Homomorphic Encryption (SparseHE) method for privacy-preserving phenotype prediction. We design a novel sparse structure to reduce the computation redundancy of HE interface while being able to defend against membership inference attack via membership inference perturbation. We reduce the computation consumption by deploying a novel sparse homomorphic encryption interface on MIP structure. The theoretical results show that our SparseHE enables privacy-preserving healthcare prediction by tightening the generalization gap of model. Additionally, the proposed SparseHE accelerates the HE interface by reducing the computation redundancy. Our experimental results on four real healthcare datasets demonstrate that SparseHE can improve the ability of model to defend against MIA while reducing computation cost of HE prediction.