Enhancing Privacy Preservation Using Hybrid Approach Of K

The need for anonymising the network while publishing has been discussed in [4] A model for anonymising is ‘k-anonymity’, proposed in [2] An implementation of k-anonymity is proposed in [1] using minimum DFS code in [3] An isomorphism algorithm used for checking the similarity of neighborhoods which is proposed in [5] has been studied. Effective privacy preserving data publishing by Jul 01, 2020 [PDF] Protecting privacy when disclosing information: k

## A table satisfying this requirement is called k-anonymous. In a k- anonymous table, each record is indistinguishable from at least k− 1 other records with respect to QID. Consequently, the probability of linking a victim to a specific record through QID is at most 1/k. k- anonymity cannot be replaced by the privacy models in attribute linkage.

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### This paper provides a discussion on several anonymity techniques designed for preserving the privacy of microdata. This research aims to highlight three of the prominent anonymization techniques

Corpus ID: 2181340. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression @inproceedings{Samarati1998ProtectingPW, title={Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression}, author={Pierangela Samarati and Latanya Sweeney}, year={1998} } So, k-anonymity provides privacy protection by guaranteeing that each released record will relate to at least k individuals even if the records are directly linked to external information. This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity. Although privacy preservation in data publishing has been studied extensively and several important models such as k-anonymity [35]andl-diversity [27] as well as many efﬁcient algorithms have been proposed, most of the existing studies can deal with relational data only. Those methods cannot be applied to social network data straightforwardly. individual privacy? • Models: K-Anonymity (Sweeney), Output Perturbation • K-Anonymity: attributes are suppressed or generalized until each row is identical with at least k-1 other rows. At this point the database is said to be k-anonymous. • K-Anonymity thus prevents definite database linkages. A table satisfying this requirement is called k-anonymous. In a k- anonymous table, each record is indistinguishable from at least k− 1 other records with respect to QID. Consequently, the probability of linking a victim to a specific record through QID is at most 1/k. k- anonymity cannot be replaced by the privacy models in attribute linkage.