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Volume 20 No 12 (2022)
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GaussianNoiseMultiplicativePrivacyForData PerturbationUnderMultiLevelTrust
RANJEET KUMARRAI, DR.MANISHVARSHNEY
Abstract
Dataminingistheprocessofexploringandanalyzinglargeblocksofinformationinordertouncovermeani
ngfulpatternsandtrends.Perturbationisamechanismthathasbeenintroducedinthefieldsofcelestialmec
hanicsandmathematicalphysics.Eachattributehasaweightassociatedwithit,whichindicateshowaccu
rateandcompleteitis.Databaseanddatasecurityadministratorsareforcedtoperformadifficultbalancin
gactwhenitcomestograntingemployeesaccesstoorganizationaldata.Forthistousemultiplicativedatap
erturbationinconjunctionwithsinglelevelandmultilayertrustthegeometrictypeofmultiplicativedatape
rturbationwillbecarriedoutinthismethod,aswell.Whengeneratingtheperturbedcopy,geometricpertur
bationinvolvestheorthonormalmatrix,translationalmatrix,andarandomgeneratedGaussiannoisevec
tor,amongotherthings.Inthebeginning,theorthonormalmatrixwillbeusedtoperformtherotationpertur
bation,andthenthetranslationalmatrixandGaussiannoisecomponentswillbeaddedtoitforthefinalpert
urbedcopy.Wecansaythatundersingleleveltrust,additiveGaussiandataperturbationproducesperturb
edcopiesusinguniformGaussiannoise.Regardlessoftheirtrustratings,alldataminersreceivethesamep
erturbedcopy.AdditiveGaussiandataperturbationatmultileveltrustisstudiedfordataminersatvarioustrustlevels.Dataperturbationisapopularrandomizationap
proachthatensures bothaccurate data mining results and privacy.
Keywords
Gaussian,noise, data perturbation,multi-level,trust, etc.
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