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Secure itemset hiding in smart city sensor data

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Abstract

Sensor data that is often collected in the Internet of Things (IoT) or any smart city environment should be protected against security and privacy concerns. Often, sensor data that is shared by devices in smart cities contains sensitive or private information that can often be shared over different networks and by different smart applications. In the last decade, the area of Privacy-Preserving Data Mining (PPDM) has received a lot of attention as the amount of data received and collected daily is huge. Unfortunately, PPDM mostly applies to binary data. To improve the usefulness of PPDM, we present a more usable version for smart cities called Privacy-Preserving Utility Mining (PPUM), in the form of a Maximal Sensitive Utility-Maximal Sensitive ConflIct (MSU-MSI) algorithm. MSU-MSI finds any conflicting items that may contain sensitive itemsets with high-utility and sanitizes them, stripping them of sensitive and private information while maintaining utility. Any transactions encountered that contain sensitive itemsets are first fed through sanitization processes. This is followed by calculating the total number of items that conflict, and then removing them so sanitization processes can operate more efficiently so as to not redo known sanitization processes. We conduct an in-depth experimental analysis, where our detailed methodology is compared directly with state-of-the-art frameworks such as MSU-MIU, MSU-MAU, HHUIF and MSCIF. Our proposed MSU-MSI shows a higher performance in missing cost, in particular when dealing with highly dense or highly sparse datasets. Moreover, our novel framework is shown to achieve an excellent performance with regards to similarity in database structure and database utility.

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Data Availability

Data available on request from the authors.

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Code available on request from the authors.

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Acknowledgements

This work is partially supported by the Natural Sciences Research Council of Canada (NSERC) through their Discovery Grants program held by Dr. Gautam Srivastava (RGPIN-2020-05363)

Funding

This work is partially supported by the Natural Sciences Research Council of Canada (NSERC) through their Discovery Grants program held by Dr. Gautam Srivastava (RGPIN-2020-05363)

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Brandon University, Brandon, R7A 6A9, Canada

    Gautam Srivastava

  2. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Jerry Chun-Wei Lin & Guo Lin

  3. Research Center for Interneural Computing, China Medical University, Taichung, 40402, Taiwan, ROC

    Gautam Srivastava

  4. Dept of Computer Science and Math, Lebanese American University, Beirut, 1102, Lebanon

    Gautam Srivastava

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  1. Gautam Srivastava
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  2. Jerry Chun-Wei Lin
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  3. Guo Lin
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Contributions

GS: Conceptualization, Methodology, Software, Data curation, Validation, Investigation, Visualization, Writing - original draft. JC-WL: Supervision, Conceptualization, Methodology, Investigation, Writing - review & editing. GL: Methodology, Validation, Writing - review & editing.

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Correspondence to Gautam Srivastava.

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Srivastava, G., Lin, J.CW. & Lin, G. Secure itemset hiding in smart city sensor data. Cluster Comput 27, 1361–1374 (2024). https://doi.org/10.1007/s10586-023-04000-2

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