SecureBadger: a homomorphic encryption-based framework for secure medical inference

Document Type

Article

Publication Date

2026

Abstract

With the rapid development of Artificial Intelligence of Things (AIoT) technology, its adoption in the field of smart healthcare is becoming increasingly pervasive. Leading cloud service providers like IBM Watson Health now offer neural network inference services tailored for smart healthcare applications - users simply need to send data to the server to get the diagnosis results. However, a growing concern arises regarding the potential compromise of user privacy. Currently, researchers propose the use of secure multi-party computation and homomorphic encryption techniques to address this issue. Nevertheless, further exploration and improvement are needed to mitigate the side effects, such as increased latency and challenges in meeting real-time monitoring requirements. In this paper, we propose a secure homomorphic encryption-based inference framework named SecureBadger for two typical medical inference scenarios: disease diagnosis based on image analysis and health monitoring with smart wearable devices. We design two inference modes—large-scale batch inference and small-scale low-latency inference. Additionally, different ciphertext packaging schemes are designed to enhance inference efficiency for different inference modes, different input data types and different network layers. Experimental evaluations are conducted on several datasets, and the results indicate that SecureBadger can significantly reduce the inference time overhead in both inference modes. © 2025 Chongqing University of Posts and Telecommunications.

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