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Pmir: an efficient privacy-preserving medical images search in cloud-assisted scenario

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Abstract

With the advancement of content-based medical image retrieval (CBMIR) technology which is used as a convenient assistant for medical diagnosis. However, the potential risk of privacy disclosure in CBMIR remains a concern due to the involvement of patients’ sensitive information in medical images. To address this issue, we have proposed an efficient scheme to achieve privacy-preserving medical image retrieval, named PMIR. The primary objective of PMIR is to enhance the accuracy of medical image retrieval while ensuring privacy protection. In PMIR, medical institutions with large repositories of medical images can securely upload to the cloud server with their image data and encryption indexes. Subsequently, users who successfully registered from medical institutions are able to enjoy convenient image retrieval services without revealing their sensitive information and query attributes to the cloud server. The proposed approach emphasizes a privacy-preserving policy mechanism, which empowers users with the right to choose rather than relying solely on service providers. Through rigorous security analysis, it is demonstrated that PMIR can effectively withstand various known security threats. Additionally, experimental results highlight the substantial reduction in communication overhead achieved by PMIR, ultimately providing a seamless and secure search experience for users.

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Data availability

The IDRID datasets generated during and/or analyzed during the current study are available in the [IEEEDataPort] Datasets, [https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid]. The COVID-19 datasets generated during and/or analyzed during the current study are available in the [BIMCV Proyectos] Datasets, [https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711].

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Acknowledgements

This work is supported in part by the Sichuan Key Laboratory of Smart Grid (Sichuan University) under grant 2023-IEPGKLSP-KFYB01, in part by the Natural Science Foundation of China under grants 62302068 and 62072061, in part by the Natural Science Foundation of Chongqing under grant CSTB2022NSCQ-MSX1627, in part by the China Postdoctoral Science Foundation under grant 2021M700588, in part by the project of Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education under grant 2021FF09, in part by the Fundamental Research Funds for the Central Universities under grant 2023CDJXY-039, in part by the Chongqing Postdoctoral Science Foundation under grant 2021XM1006, in part by the National Key R&D Program of China under grant 2020YFB1805400 and 2022YFC3801700.

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, People’s Republic of China

    Dong Li, Qingguo Lü & Keke Zhang

  2. Key Laboratory of Smart Grid, Sichuan Province, Chengdu, 610065, People’s Republic of China

    Dong Li & Qingguo Lü

  3. College of Mathematics and Physics, Guangxi Minzu University, Guangxi, 530006, People’s Republic of China

    Yanling Wu

  4. School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, 2052, Australia

    Zheng Wang

  5. Peng Cheng Laboratory, Shenzhen, 518055, People’s Republic of China

    Jiahui Wu

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  1. Dong Li
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Correspondence to Qingguo Lü or Zheng Wang.

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Li, D., Wu, Y., Lü, Q. et al. Pmir: an efficient privacy-preserving medical images search in cloud-assisted scenario. Neural Comput & Applic 36, 1477–1493 (2024). https://doi.org/10.1007/s00521-023-09118-3

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