简介:Anoutsourcedatabaseisadatabaseserviceprovidedbycloudcomputingcompanies.Usingtheoutsourcedatabasecanreducethehardwareandsoftware'scostandalsogetmoreefficientandreliabledataprocessingcapacity.However,theoutsourcedatabasestillhassomechallenges.Iftheserviceproviderdoesnothavesufficientconfidence,thereisthepossibilityofdataleakage.Thedatamayhasuser'sprivacy,sodataleakagemaycausedataprivacyleak.Basedonthisfactor,toprotecttheprivacyofdataintheoutsourcedatabasebecomesveryimportant.Inthepast,scholarshaveproposedk-anonymitytoprotectdataprivacyinthedatabase.Itletsdatabecomeanonymoustoavoiddataprivacyleak.Butk-anonymityhassomeproblems,itisirreversible,andeasiertobeattackedbyhomogeneityattackandbackgroundknowledgeattack.Lateron,scholarshaveproposedsomestudiestosolvehomogeneityattackandbackgroundknowledgeattack.Buttheirstudiesstillcannotrecoverbacktotheoriginaldata.Inthispaper,weproposeadataanonymitymethod.Itcanbereversibleandalsopreventthosetwoattacks.Ourstudyisbasedontheproposedr-transform.Itcanbeusedonthenumerictypeofattributesintheoutsourcedatabase.Intheexperiment,wediscussedthetimerequiredtoanonymizeandrecoverdata.Furthermore,weinvestigatedthedefenseagainsthomogeneousattackandbackgroundknowledgeattack.Attheend,wesummarizedtheproposedmethodandfutureresearches.
简介:短波战术数据链在现代战争中发挥着重要作用.针对短波时变多径信道特性,提出了一种基于信道估计的Turbo均衡算法.该算法在初次迭代时根据训练序列的自相关特性,采用快速相关信道估计算法估计信道初始状态,并进行基于线性最小均方误差(MMSE)的Turbo均衡;然后利用译码器反馈的软信息,采用最小均方(LMS)迭代信道估计算法优化信道估计值,并进行迭代均衡.仿真结果表明,该算法的误码率性能逼近于假定信道状态已知的Turbo均衡算法,对Turbo均衡在数据链中实际应用具有借鉴意义.