2012
DOI: 10.1109/tkde.2010.236
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Slicing: A New Approach for Privacy Preserving Data Publishing

Abstract: Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasiidentifying attributes and sensitive attributes.In this paper, we present a novel technique called slic… Show more

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Cited by 243 publications
(220 citation statements)
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“…A novel technique called slicing [14] was investigated using generalization and bucketization to prevent membership disclosure. In [15], privacy preserving for location-based query problems were investigated to introduce a security model.…”
Section: Related Workmentioning
confidence: 99%
“…A novel technique called slicing [14] was investigated using generalization and bucketization to prevent membership disclosure. In [15], privacy preserving for location-based query problems were investigated to introduce a security model.…”
Section: Related Workmentioning
confidence: 99%
“…A new approach for building classifiers using anonymized data by modeling anonymized data as uncertain data is proposed in [17]. In [18], a novel technique called slicing is proposed, which preserves better data utility than generalization and can be used for attribute disclosure protection and membership disclosure protection.…”
Section: Anonymization Technique:-mentioning
confidence: 99%
“…Slicing handles high dimensional data by partitioning the attributes in columns and thus helps in protecting the private information Thus slicing in combination with correlation analysis has the high data utility and preserves the privacy. In [2] author introduces a new approach slicing which partitions attributes so that highly correlated attributes are in the same column. In case of data utility, grouping highly correlated attributes helps in breaking the correlations among those attributes.…”
Section: Related Workmentioning
confidence: 99%