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

Zhaoyang He; Wenti Yang; Longfei Wu; Zhitao Guan

doi:10.1016/j.dcan.2025.08.006

›› 2026, Vol. 12 ›› Issue (5) :743 -754. DOI: 10.1016/j.dcan.2025.08.006

research-article

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

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^a School of Control and Computer Engineering, North China Electric Power University , China

^b Pengcheng Laboratory , Shenzhen, 518066, China

^c Department of Mathematics and Computer Science, Fayetteville State University , Fayetteville, 28301, USA

^* E-mail addresses: he1506044760@163.com (Z. He),

yangwt@pcl.ac.cn (W. Yang),

lwu@uncfsu.edu (L. Wu),

guan@ncepu.edu.cn (Z. Guan).

Zhaoyang He received the M.Eng. degree in computer technology from North China Electric Power University, Beijing, China, in 2025. His research interest is privacy-preserving machine learning.

Wenti Yang received the Ph.D. degree in Information Security from the North China Electric Power University, Beijing, China, in 2024. She is currently an Assistant Researcher with the Department of New Networks, Pengcheng Laboratory (PCL), Shenzhen, Guangdong, China. Her research focuses on privacy-preserving machine learning and security in computing power networks.

Longfei Wu received his Ph.D. degree in Computer and Information Sciences from Temple University in July 2017. He obtained his B.E. degree in Telecommunication Engineering from the Beijing University of Posts and Telecommunications in July 2012. He is currently an Associate Professor in the Department of Mathematics and Computer Science at Fayetteville State University. His research interests include the security and privacy of networked systems and trustworthy machine learning.

Zhitao Guan is currently a professor at the School of Control and Computer Engineering, North China Electric Power University. He received his BEng degree and PhD in Computer Application from Beijing Institute of Technology, China, in 2002 and 2008, respectively. His current research focuses on smart grid security, system security, AI security, and privacy computing.

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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.

Keywords

Homomorphic encryption / Secure inference / Smart healthcare / Wearable health technology / Artificial Intelligence of Things

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Zhaoyang He, Wenti Yang, Longfei Wu, Zhitao Guan. SecureBadger: a homomorphic encryption-based framework for secure medical inference^✩. , 2026, 12 (5) : 743-754 DOI:10.1016/j.dcan.2025.08.006

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CRediT authorship contribution statement

Zhaoyang He: Writing -- original draft, Methodology, Investigation, Conceptualization. Wenti Yang: Writing -- review & editing, Validation, Software. Longfei Wu: Writing -- review & editing, Validation, Resources. Zhitao Guan: Supervision, Investigation, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgements

The work is supported by the National Natural Science Foundation of China under Grant 62372173.

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