Thereare number of techniques are researched and proposed for anonymizationof data for privacy preserving data mining Data anonymization techniques forprivacy – preserving data publishing has got the more importance in recent year. In any largeorganization such as hospitals, government agency, private firms there is ahuge micro data is generated on daily basis so, maintaining the privacy andidentify of any entity such like as patient, bank customer is having thehighest priority. Anonymity is the way which the can hide anyone’s identity ormake it concealed. Most common and well known generalization &bucketization are the two techniques which are design for privacy preservingmicro data publishing. The previous study of generalization shows that it losessubsequent amount of data while performing generalization process.Bucketization, on the other hand not applicable for preventing member shipdisclosure. In this paper, we proposed an innovative technique called slicing,using this we can partitions the data both horizontally as well as vertically.In this paper we also shown that this technique preserves improved data utilitythan generalization technique and it also support membership disclosureprotection.
Keywords:-PrivacyPreserving, Membership Disclosure, Data Anonymization, Generalization,Bucketization, Tuples, sensitive attributes ( SAS )