Advanced Cluster-Based Attribute Slicing: A New Approach for Privacy Preservation

Susan, V. Shyamala; Christopher, Thomas W.

doi:10.1007/978-81-322-2674-1_21

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…Susan and Christopher [10] also distributed sensitive attributes values. They applied advanced clustering algorithm (ACA) [11] to distribute and cluster relevant sensitive attributes. But, it was to partition attribute and slice vertically not to distribute highly sensitive values.…”

Section: Issn: 2302-9285 mentioning

confidence: 99%

Extended systematic clustering: Microdata protection by distributing semsitive values

et al. 2020

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Anonymity data for multiple sensitive attributes in microdata publishing is a growing field at present. This field has several models for anonymizing such as k-anonymity and l-diversity. Generalization and suppression became a common technique in anonymize data. But, the real problem in multiple sensitive attributes is sensitive value distribution. If sensitive values do not distribute evenly to each quasi identifier group, it is potentially revealed to sensitive value holder. This research investigated on how the high-sensitive values are distributed evenly into each group. We proposed a novel method/algorithm for distributing high-sensitive values when it forms groups. This method distributes high-sensitive values evenly and varies high-sensitive values in a group. We called our method as extended systematic clustering since it is an extension of systematic clustering method. Diversity metrics was used for evaluating our method. Experiment result showed our method outperformed systematic clustering with average diversity value 0.9719 while systematic clustering 0.3316.

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Section: Issn: 2302-9285 mentioning

confidence: 99%