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Home > Archives > Volume 20, No 12 (2022) > Article

DOI: 10.14704/nq.2022.20.12.NQ77008

GaussianNoiseMultiplicativePrivacyForDataPerturbationUnderMultiLevelTrust

RANJEET KUMARRAI*,DR.MANISHVARSHNEY

Abstract

Dataminingistheprocessofexploringandanalyzinglargeblocksofinformationinordertouncovermeaningfulpatternsandtrends.Perturbationisamechanismthathasbeenintroducedinthefieldsofcelestialmechanicsandmathematicalphysics.Eachattributehasaweightassociatedwithit,whichindicateshowaccurateandcompleteitis.Databaseanddatasecurityadministratorsareforcedtoperformadifficultbalancingactwhenitcomestograntingemployeesaccesstoorganizationldata.Forthistousemultiplicativedataperturbationinconjunctionwithsinglelevelandmultilayertrustthegeometrictypeofmultiplicativedataperurbationwillbecarriedoutinthismethod,aswell.Whengeneratingtheperturbedcopy,geometricperturbationinvolvestheorthonormalmatrix,translationalmatrix,andarandomgeneratedGaussiannoisevector,amongotherthings.Inthebeginning,theorthonormalmatrixwillbeusedtoperformtherotationperturbation,andthenthetranslationalmatrixandGaussiannoisecomponentswillbeaddedtoitforthefinalperturbedcopy.Wecansaythatundersingleleveltrust,additiveGaussiandataperturbationproducesperturbedcopiesusinguniformGaussiannoise.Regardlessoftheirtrustratings,alldataminersreceivethesameperturbedcopy.AdditiveGaussiandataperturbationatmulti-leveltrustisstudiedfordataminersatvarioustrustlevels.Dataperturbationisapopularrandomizationapproachthatensures bothaccurate data mining results and privacy.

Keywords

Gaussian,noise, data perturbation,multi-level,trust, etc.

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