A comparative study of various MDAV algorithms

International Journal of Research and Engineering

View Publication Info
 
 
Field Value
 
Title A comparative study of various MDAV algorithms
 
Creator Rawat, Gajendra Singh
Borah, Dr. Bhogeshwar
 
Subject Statically Disclosure Control, Information Loss, Disclosure Risk, micro-data, anonymity, micro-aggregation
 
Description Microaggregation is an efficient Statistical Disclosure Control (SDC) perturbative technique for microdata protection. It is a unified approach and naturally satisfies k-Anonymity without generalization or suppression of data. Various microaggregation techniques: fixed-size and data-oriented for univariate and multivariate data exists in the literature. These methods have been evaluated using the standard measures: Disclosure Risk (DR) and Information Loss (IL). Every time a new microaggregation technique was proposed, a better trade-off between risk of disclosing data and data utility was achieved. Though there exists an optimal univariate microaggregation method but unfortunately an optimal multivariate microaggregation method is an NP hard problem. Consequently, several heuristics have been proposed but no such method outperforms the other in all the possible criteria. In this paper we have performed a study of the various microaggregation techniques so that we get a detailed insight on how to design an efficient microaggregation method which satisfies all the criteria.
 
Publisher IJRE Publisher
 
Date 2015-09-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Identifier https://digital.ijre.org/index.php/int_j_res_eng/article/view/100
 
Source International Journal of Research and Engineering; Vol 2 No 9 (2015): September 2015 Edition; 24-28
2348-7860
2348-7852
 
Language en
 
Rights Copyright (c) 2016 International Journal of Research and Engineering (IJRE)
http://creativecommons.org/licenses/by/4.0
 

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