基于随机映射的隐私保护聚类算法

打开文本图片集

关键词：高维数据；隐私保护；聚类；随机映射；K-means

中图分类号：TP309 文献标志码：A 文章编号：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

近年来，随着大数据技术的迅速兴起以及信息技术的飞速发展[1，诸如医疗机构和教育机构等组织每天都会生成大量数据，这些数据涵盖了广泛的领域，通过数据挖掘技术的分析和处理，能够将其转换为具有实际应用价值的信息。（剩余18319字）