基于隨機映射的隱私保護聚類算法

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關鍵詞：高維數據；隱私保護；聚類；隨機映射；K-means

中圖分類號：TP309 文獻標志碼：A 文章編號：1001-3695（2025）08-035-2511-07

doi：10. 19734/j.issn.1001-3695.2024.10.0503

Privacy-preserving algorithm for clustering high-dimensional data based on random mapping

He Lili a，b， c，Zhang Chenglina，b，c，Cao Mingzenga，bc， Zhang Lei a.b，et （aScloffadEeoicogcalboaofuooutellgee&fr cessing，c.JiusiKbotoftelittoo&qeEeengecsiUJi longjiang 154007，China）

Abstract：Toaddress thechalengeof increasing privacycosts withrisingdata dimensions inclustering privacyprotectionalgorithms，this paper proposed arandom projection-based privacypreserving algorithm（RPPP）.RPP selected relevant features usingthesymmetricaluncertaintymethodandgeneratedrandommatricesthroughindependentlyandidenticalldistributed Gaussiansequences.Tostrengthen distance-preservingproperties，itappied Gram-Schmidtorthogonalization toensuretheorthogonalityof therandom matrices.These matriceswere decomposed intomultipleindependentsub-matrices to map thereduced-dimensionalfeatures，andcreatedafeature-matchingdomainandanoise-perturbeddomain.To further enhanceprivacy protectin，thealgorithminjectedrandomnoiseintothenoise-perturbeddomain.ExperimentalresultsdemonstratethatRPPP efectivelydefendsagainstprivacyatacks.TestsconductedontheCancerandDiabetes datasetsshowthatRPPPoutperforms traditional algorithmsinbothprivacyprotectionandclustering eficiency.Specifically，RPPPimproves clustering efficncyby approximately 16.34% ， 23.44% ，and 32.94% compared with UPA，GCCG，and AKA algorithms，respectively. Overall，RPPP significatlyehanesprivacyprotectionwhileboostingclustering eficiency，confirming itseffctivenessandpracticalaplicability.

Key Words：high-dimensional data；privacy protection；clustering；random projection；K-means

0 引言

近年來，隨著大數據技術的迅速興起以及信息技術的飛速發(fā)展[1，諸如醫(yī)療機構和教育機構等組織每天都會生成大量數據，這些數據涵蓋了廣泛的領域，通過數據挖掘技術的分析和處理，能夠將其轉換為具有實際應用價值的信息。（剩余18319字）