A Framework for Privacy-Preserving Cluster Analysis Benjamin C. M. Fung Concordia University Canada fung@ciise.concordia.ca Lingyu Wang Mourad Debbabi
preserve the privacy of data publishing such as K-anonymity, ℓ-diversity, t -closeness. K-anonymity can prevent the record linkage but unable to protect attribute linkage. Query Processing with K-Anonymity - Arizona State University Several algorithms have been proposed to provide stronger privacy-preservation over the k-anonymity technique, e.g., l-diversity [12], and t-closeness [11]. The t-closeness technique ensures that the distribution of sensitive values in a single anonymized group is as close as possible to the distribution in the base table. Challenges and techniques in Big data security and privacy Mar 22, 2018
Privacy Preservation of Data in Data mining using K
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.
Apr 07, 2017
Title: KDD Infrastructure Author: hector Last modified by: Hector Garcia-Molina Created Date: 12/31/2001 10:24:08 PM Document presentation format PowerPoint Presentation Summary of Sensitive Items k = 2 [FWR06] K. LeFevre et al. Mondrian Multidimensional k-anonymity, Proceedings of the 22nd International Conference on Data Engineering (ICDE), 2006 Age 20 40 60 Weight 40 60 80 100 GENERALIZATION + HIGH DIMENSIONALITY = UNACCEPTBLE INFORMATION LOSS Gastritis(1) Dyspepsia(1) Flu(1) Dyspepsia(1) Ulcer(1) Pneumonia Big data privacy: a technological perspective and review Nov 26, 2016 A comprehensive review on privacy preserving data mining These include K-anonymity, classification, clustering, association rule, distributed privacy preservation, L-diverse, randomization, taxonomy tree, condensation, and cryptographic (Sachan et al. 2013). The PPDM methods protect the data by changing them to mask or erase the original sensitive one to be concealed.