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Lightweight and Practical Privacy-Preserving Image Masking in Smart Community

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Information and Communications Security (ICICS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13407))

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Abstract

Advances in the Internet of Things (IoT) and telecommunications technologies (e.g., 5G) have contributed to the development of smart cities and nations (collectively referred to as smart communities). In a smart community, IoT devices can collect significant information about urban residents (e.g., a large number of images collected by cameras containing sensitive information), and such information may be shared with intermediate nodes. In real-world deployment, intermediate nodes are not completely trusted, where the information collected may be used for commercial purposes (e.g., user profiling and advertising) or malicious activities (e.g., covert surveillance). In this paper, we introduce an approach to ensure privacy-preserving image masking. Specifically, before the image is transmitted to the camera owner or the monitoring cloud platform, only sensitive areas instead of the entire image will be processed according to the camera owner’s settings, which allows to significantly reduce the computational cost. Then, in order to reduce the interactions between the community data center and the IoT camera, the monitoring cloud platform performs proxy re-encryption. This allows the community data center to recover the original image without relying on the IoT device’s private key. Our evaluation indicates the utility and efficiency of our approach, as compared with similar schemes.

Supported in part by the Natural Science Foundation of China under Grant 62072133, in part by the Key Projects of Guangxi Natural Science Foundation under Grant 2018GXNSFDA281040, in part by the Innovation Project of Guangxi Graduate Education under Grant YCSW2022279.

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Authors and Affiliations

  1. Guangxi Key Laboratory of Trusted Software, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China

    Zhen Liu & Yining Liu

  2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark

    Weizhi Meng

Authors
  1. Zhen Liu
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  2. Yining Liu
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  3. Weizhi Meng
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Correspondence to Yining Liu .

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Editors and Affiliations

  1. University of Malaga, Malaga, Spain

    Cristina Alcaraz

  2. University of Surrey, Guildford, UK

    Liqun Chen

  3. University of Kent, Canterbury, UK

    Shujun Li

  4. University of Milan, Milan, Italy

    Pierangela Samarati

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Liu, Z., Liu, Y., Meng, W. (2022). Lightweight and Practical Privacy-Preserving Image Masking in Smart Community. In: Alcaraz, C., Chen, L., Li, S., Samarati, P. (eds) Information and Communications Security. ICICS 2022. Lecture Notes in Computer Science, vol 13407. Springer, Cham. https://doi.org/10.1007/978-3-031-15777-6_13

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  • DOI: https://doi.org/10.1007/978-3-031-15777-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15776-9

  • Online ISBN: 978-3-031-15777-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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