Implementation of Slicing for Multiple Column Multiple Attributes Privacy Preserving Data Publishing

Latest work shows that abstraction loses amount of information for high spatial data. There are several anonymization techniques like Abstraction, Containerization for privacy preserving small data publishing. Bucketization does not avoid enrollment acknowledgment and does not give clear separation between aspects. We are presenting a technique called slicing for multiple columns multiple attributes which partitions the data both horizontally and vertically. We also show that slicing conserves better data service than generalization and bucketization and can be used for enrollment acknowledgment conservation. Slicing can be used for aspect acknowledgment conservation and establishing an efficient algorithm for computing the sliced data that obey the l-diversity requirement Our workload confirm that this technique is used to prevent membership disclosure and it also used to increase the data utility and privacy of a sliced dataset by allowing multiple column multiple attributes slicing while maintaining the prevention of membership disclosure. DOI: 10.17762/ijritcc2321-8169.150615