Abstract
Privacy preservation models are often proposed to address privacy violation issues in datasets that are focused on performing one-time data releasing. Thus, if datasets are allowed to update (modify) the data of them when the new data become available and released on performing multiple times, privacy preservation models could be insufficient. For this reason, the aims of this work are to identify the vulnerabilities of privacy preservation models in dynamic datasets which are based on data updating (data modifying), and further propose a new algorithm that can address privacy violation issues in the re-publication of modified datasets.
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Riyana, N., Riyana, S., Nanthachumphu, S., Sittisung, S., Duangban, D. (2021). Privacy Violation Issues in Re-publication of Modification Datasets. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_79
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DOI: https://doi.org/10.1007/978-3-030-68154-8_79
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