Private and Energy-Efficient Decision Tree-Based Disease Detection for Resource-Constrained Medical Users in Mobile Healthcare Network

Abstract

In mobile healthcare networks (MHN), outsourced disease detection services demand the privacy preservation of medical users and health service providers (health clouds). This necessitates the use of a fully homomorphic encryption (FHE) while providing disease detection services, such as decision tree-based disease detection. However, the existing homomorphic encryption schemes utilized in decision tree-based disease detection that ensure the privacy of the medical user and health cloud are computationally-intensive and energy-hungry at the edge devices. Hence the medical user finds it difficult to exploit the existing private decision tree-based disease detection services due to restrictions on battery capacity and computing resources. Therefore, this work proposes a protocol for private decision tree classification with low resource consumption (PDTC-LRC) on edge devices of medical users by considering decision tree parameters as confidential to the health cloud. An energy-efficient, additively homomorphic, symmetric key-based FHE-compatible Rivest scheme (FCRS) is developed for implementing PDTC-LRC. FCRS can be decrypted homomorphically at the health cloud to support additive and multiplicative homomorphism. Also, an energy and bandwidth-efficient secure integer comparison protocol is developed for realizing PDTC-LRC. Experiments on the Raspberry Pi 3B+ board validate the improved energy efficiency and real-time applicability of the proposed secure integer comparison protocol and decision tree classifier compared with similar schemes available in the literature. Simulation and mathematical analysis ensure that user and health cloud privacy requirements are achieved by maintaining the classification accuracy same as that of decision tree classification in the plain domain.