To　alleviate　the　traffic　pressure　on　roads,　reduce　the　appearance　of　road　congestion,　and　avoid　the　occurrence　of　traffic　accidents,　a　privacy-preserving　intelligent　monitoring　(PPIM)　scheme　based　on　intelligent　traffic　was　proposed　in　combination　with　the　safe　and　k-nearest　neighbor　(KNN)　algorithm.　To　ensure　the　security　of　traffic　data,　the　data　content　was　randomly　divided　into　independent　parts　via　the　secure　multi-party　computing　strategy,　and　the　data　components　were　stored　and　encrypted　separately　by　non-colluding　mul-ti-servers.　To　improve　the　accuracy　of　road　condition　monitoring,　an　improved　KNN　traffic　monitoring　algorithm　was　proposed.　By　virtue　of　the　similarity　calculation　of　data,　the　correlation　value　to　measure　the　degree　of　traffic　condition　relationship　between　roads　was　obtained.　And　it　was　integrated　with　the　KNN　as　the　weight　coefficient.　To　speed　up　the　processing　of　dense　data,　a　series　of　data　security　computing　protocols　were　designed,　and　the　data　security　processing　was　realized.　In　addition,　real　traffic　data　were　used　to　verify　the　algorithm.　The　results　show　that　the　improved　KNN　algorithm　is　helpful　to　improve　the　accuracy　of　traffic　monitoring.　The　analysis　shows　that　the　algorithm　can　not　only　guaran-tee　the　safety　of　data　but　improve　the　accuracy　of　traffic　monitoring.　©　2020,　Editorial　Board　of　Journal　on　Communications.　All　right　reserved.