A　reliable　mobile　crowdsensing　(MCS)　application　usually　relies　on　sufficient　participants　and　trustworthy　data.　However,　privacy　concerns　reduce　participants＇　willingness　to　participate　in　sensing　tasks.　The　uncertainty　of　participant　behavior　and　heterogeneity　of　sensing　devices　result　in　the　unreliability　of　sensing　data　and　further　bring　unreliable　MCS　services.　Hence,　it　is　crucial　to　estimate　the　reliability　of　sensing　data　and　protect　privacy.　Unfortunately,　most　existing　privacy-preserving　data　estimation　solutions　are　designed　for　single-type　data.　In　practice,　however,　heterogeneous　sensing　data　are　ubiquitous　in　data　integration　tasks.　To　this　end,　we　propose　a　privacy-preserving　reliability　estimation　solution　of　heterogeneous　data　for　MCS,　called　IronM,　which　is　effective　for　text,　number,　and　multimedia　data　(e.g.,　image,　audio,　and　video).　Specifically,　IronM　first　formulates　the　reliability　assessment　of　text,　number,　and　multimedia　data　as　equality　and　range　constraints,　and　then　estimates　the　reliability　of　heterogeneous　data　through　our　proposed　privacy-preserving　hybrid　constraints　assessment　mechanism.　Privacy　analysis　demonstrates　that　IronM　can　not　only　evaluate　the　reliability　of　heterogeneous　data　but　also　protect　data　confidentiality.　The　experimental　results　in　real-world　datasets　show　the　effectiveness　and　efficiency　of　IronM.　©　2014　IEEE.