K-MNSOA: K-Anonymity Model for Privacy in the Presence of Multiple Numerical Sensitive Overlapped Attributes

Abstract

AbstractKnowledge is the main discussing and explored topic of today’s era. Everyone is working toward improving information and tries to consider it as a ladder to move forward. Data is the main object to get information, and data is considered as a big data nowadays as it contains numerous information in all directions. As knowledge is bliss, it is also possible that an adversary can use this information to harm an individual. To protect data from an adversary privacy preserving data publishing techniques is used. But when multiple sensitive data present in a data set which is correlated to each other’s several model are unable to protect data in an efficient way. In this paper, a novel model K-MNSOA is proposed for privacy preserving data publishing, which protect sensitive data privacy breach, even if the data set contains multiple sensitive numerical overlapped attributes. A proposed model assumes that all sensitive attributes are not actually sensitive, so when data is protected, information loss will increase. To overcome this issue, new model suggests to divide sensitive data into levels of sensitivity and apply generalization only for the privacy of high sensitive attribute.KeywordsPrivacy preservation techniquesK-anonymityMembership disclosurePrivacy breach