A K-Anonymity Based Schema for Location Privacy Preservation
Privacy preservation is the rising issue in the social networks that are the hot spots where information theft instances are very common. The present approach focuses the protection of sensitive information based on k-anonymity. K-anonymity is one of the most popular approaches privileged by graphs and nodes functionality. Privacy Preserving in Data Mining K-anonymity is technique which gives the new and more efficient ways for anonymized data and it preserve patterns during whole anonymization. The k-anonymity model defines the whole privacy of output of process and that process is not by itself. It is simple and well understood model [10,12,16]. K- anonymity is main privacy protection model. Privacy Preserving Data Publishing Based on k-Anonymity by -anonymity to provide privacy preservation via generalization and suppression. Sweeney and Samarati introduce k- anonymity in which each quasi identifier attribute domain is partitioned into set of intervals by replacing the attribute value with corresponding intervals. PowerPoint Presentation
(alpha, k)-anonymity: an enhanced k-anonymity model for
Jun 16, 2010 · To protect privacy against neighborhood attacks, we extend the conventional k-anonymity and l-diversity models from relational data to social network data. We show that the problems of computing optimal k-anonymous and l-diverse social networks are NP-hard. We develop practical solutions to the problems. Jan 04, 2015 · 5 Related work given in Base Paper In this paper the K-Anonymity technique used for preserve the publish data with the comparison with other technique given below. 1> L-Diversity 2> T-Closeness K-anonymity:- The data base said to be k-anonymous where attribute are suppressed or generalized until each row is identical with at least k-1 other row.
ILLIA: Enabling $k$ -Anonymity-Based Privacy Preserving
PPDP-MLT: K−ANONYMITY PRIVACY PRESERVATION FOR