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• W4293690755 abstract "The infrastructure required for data storage and processing has become increasingly feasible, and hence, there has been a massive growth in the field of data acquisition and analysis. This acquired data is published, empowering organizations to make informed data-driven decisions based on previous trends. However, data publishing has led to the compromise of privacy as a result of the release of entity-specific information. Privacy-Preserving Data Publishing [1] can be accomplished by methods such as Data Swapping, Differential Privacy, and the likes of k-Anonymity. k-Anonymity is a well-established method used to protect the privacy of the data published. We propose a clustering-based novel algorithm named SAC or the Score, Arrange, and Cluster Algorithm to preserve privacy based on k-Anonymity. This method outperforms existing methods such as the Mondrian Algorithm by K. LeFevre and the One-pass K-means Algorithm by Jun-Lin Lin from a data quality perspective. SAC can be used to overcome temporal attack across subsequent releases of published data. To measure data quality post anonymization we present a metric that takes into account the relative loss in the information, that occurs while generalizing attribute values." @default.
• W4293690755 created "2022-08-31" @default.
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• W4293690755 date "2022-07-08" @default.
• W4293690755 modified "2023-09-23" @default.
• W4293690755 title "Enhanced k-Anonymity model based on clustering to overcome Temporal attack in Privacy Preserving Data Publishing" @default.
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• W4293690755 doi "https://doi.org/10.1109/conecct55679.2022.9865682" @default.
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