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Classification of Datasets Used in Data Anonymization for IoT Environment

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Advances and Trends in Artificial Intelligence. Theory and Applications (IEA/AIE 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14748))

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Abstract

The integration of Internet of Things (IoT) devices in smart cities facilitates the collection, sharing, and analysis of data to optimize city services, enhance residents’ well-being, and enable more efficient decision-making processes. Balancing the benefits of IoT data collection and sharing with the imperative of preserving user privacy involves implementing robust security measures and anonymization techniques to ensure the safety, security, and trustworthiness of IoT technologies. Anonymization techniques enable service providers to collect, store, and share data while preserving user privacy, with various methods applicable across different types. This paper presents a literature review focusing on data anonymization in the IoT environment, aiming to classify datasets used to evaluate newly proposed anonymization methods, examining their types, availability, size, and applicability in research. The study aims to provide insights into the datasets employed in data anonymization research for IoT environments and determine the level of using relational datasets in such studies.

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Acknowledgements

This study is supported by the SPEV project 2024 run at the Faculty of Informatics and Management, University of Hradec Králové, Czech Republic. The assistance provided by Dominik Palla with data collection is greatly appreciated.

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Authors and Affiliations

  1. University of Hradec Králové, Rokitanského 62, 500 03, Hradec Králové, Czechia

    Jana Medková

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  1. Jana Medková
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Correspondence to Jana Medková .

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Editors and Affiliations

  1. Malaysia-Japan International Institute of Technology (MJIIT), University of Technology Malaysia, Kuala Lumpur, Malaysia

    Hamido Fujita

  2. University of Hradec Kralove, Hradec Kralove, Czech Republic

    Richard Cimler

  3. Meiji University, Tokyo, Japan

    Andres Hernandez-Matamoros

  4. Department of Computer Science, Texas State University, San Marcos, TX, USA

    Moonis Ali

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Medková, J. (2024). Classification of Datasets Used in Data Anonymization for IoT Environment. In: Fujita, H., Cimler, R., Hernandez-Matamoros, A., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2024. Lecture Notes in Computer Science(), vol 14748. Springer, Singapore. https://doi.org/10.1007/978-981-97-4677-4_8

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  • DOI: https://doi.org/10.1007/978-981-97-4677-4_8

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  • Print ISBN: 978-981-97-4676-7

  • Online ISBN: 978-981-97-4677-4

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