Slicing: A New Access to Isolation Preserving Data Publishing

Abstract

There are several anonymizing techniques like Abstraction, Containerization for isolation preserving small data publishing. The Abstraction loses amount of information for high spatial data. Containerization does not avoid enrollment acknowledgment and does not give clear separation between aspects. We are presenting a technique called slicing which partitions the data both horizontally and vertically. We also show that slicing conserves better data service than abstraction and can be used for enrollment acknowledgment conservation. One more important advantage of slicing is that it can handle high-spatial data. Slicing can be used for aspect acknowledgment conservation and establishing an efficient algorithm for computing the sliced data that obey the l-diversity concern. Our experiments confirm that slicing conserves better service than abstraction and is more active than containerization in loads affecting the conscious aspect and also demonstrate that slicing can be used to avoid enrollment acknowledgment.
DOI: 10.17762/ijritcc2321-8169.160452