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Achieving Anonymization Constraints in High-Dimensional Data Publishing Based on Local and Global Data Suppressions

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Abstract

With the RFID data collection, it is an important data collection that is proposed to utilize in applications such as smart farms, healthcares, and transportations. Aside from these applications, it can also be utilized by data analysts. With such a data utilization of the RFID data collection, it can lead to being privacy violation issues. To address these issues, LKC-Privacy is proposed. That is, before trajectory datasets are released for public use, all at-most-L-unique subsequence paths are suppressed to be at least K indistinguishable paths, such that all protected sensitive values are related to every indistinguishable subsequence path; they have the probability of successful re-identifications to be at most C. Although LKC-Privacy can address privacy violation issues in RFID data collection, it often leads to being the issue of more information losses and more using execution time. To rid these vulnerabilities of LKC-Privacy, a new privacy preservation model is proposed in this work, such that it is also based on LKC-Privacy constraints. Moreover, the proposed model is evaluated by extensive experiments. From the experimental results, they show that the proposed model is highly effective and efficient than LKC-Privacy.

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Authors and Affiliations

  1. Maejo University (MJU), Chiangmai-Phrao Road, Maejo, Sansai, Chiang Mai, 50290, Thailand

    Surapon Riyana & Noppamas Riyana

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  1. Surapon Riyana
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Correspondence to Surapon Riyana.

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Riyana, S., Riyana, N. Achieving Anonymization Constraints in High-Dimensional Data Publishing Based on Local and Global Data Suppressions. SN COMPUT. SCI. 3, 3 (2022). https://doi.org/10.1007/s42979-021-00936-7

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