Crowdsensing　is　regarded　as　a　critical　component　of　the　Internet　of　Things　(IoT)　and　has　been　widely　applied　in　smart　city　services.　Incentive　mechanism　design,　data　reliability　evaluation,　and　privacy　preservation　are　the　research　focuses　of　crowdsensing.　However,　most　existing　incentive　mechanisms　fail　to　protect　data　privacy　and　evaluate　data　credibility,　simultaneously.　Moreover,　traditional　privacy　and　reliability-aware　incentive　schemes　are　usually　challenging　to　realize　real-time　reward　distribution.　To　this　end,　we　first　point　out　a　single-time　slice　of　failure　problem　in　real-time　incentive　mechanisms　and　propose　a　two-layer　truth　discovery　model　(TLTD)　to　resolve　this　problem.　Then,　a　reliability-aware　real-time　incentive　mechanism　(RRIM)　is　designed　based　on　the　proposed　TLTD.　In　order　to　evaluate　data　reliability　in　a　privacy-preserving　manner,　we　build　a　privacy-preserving　truth　discovery　solution　(PriTD)　based　on　secure　computation　protocols.　Finally,　our　proposed　system　[privacy　and　reliability-aware　real-time　incentive　system　for　crowdsensing　(PRICE)]　integrating　the　aforementioned　protocols　realizes　real-time　reward　distribution,　data　reliability　evaluation,　and　privacy　protection,　simultaneously.　Theoretical　analysis　and　experimental　evaluations　on　a　synthetic　and　real-world　data　set　demonstrate　the　feasibility　and　efficiency　of　the　proposed　PRICE.　©　2014　IEEE.