The Most Prominent Technique for Privacy Preservation in Mining Micro Data (NCCC-13)
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Publication date: 2019-11-25
Eurasian J Anal Chem 2018;13(3):em2018148
The data are increasingly being collected and used. Privacy preserving data mining tries to strike a balance between two opposing forces: the objective of discovering valuable information and knowledge, verse the responsibility of protecting individual’s privacy. Several anonymization techniques, such as generalization and bucketization, have been designed for preserving privacy in micro data publishing. But generalization loses considerable amount of data. On the other side, bucketization does not prevent membership disclosure and there is no clear isolation of quasi identifiers and sensitive attributes. We present a novel data anonymization technique called slicing which partitions the data both horizontally and vertically. Our empirical result shows that slicing protects individual entity with high degree of data utility than generalization and suppression. Slicing also provides attribute and membership disclosure protection. And our algorithm satisfies ℓ-diverse requirement.