With　the　rapid　digitalization　of　various　industries,　mobile　crowdsensing　(MCS),　an　intelligent　data　collection　and　processing　paradigm　of　the　industrial　Internet　of　Things,　has　provided　a　promising　opportunity　to　construct　powerful　industrial　systems　and　provide　industrial　services.　The　existing　unified　privacy　strategy　for　all　sensing　data　results　in　excessive　or　insufficient　protection　and　low　quality　of　crowdsensing　services　(QoCS)　in　MCS.　To　tackle　this　issue,　in　this　article　we　propose　a　personalized　privacy　protection　(PERIO)　framework　based　on　game　theory　and　data　encryption.　Initially,　we　design　a　personalized　privacy　measurement　algorithm　to　calculate　users＇　privacy　level,　which　is　then　combined　with　game　theory　to　construct　a　rational　uploading　strategy.　Furthermore,　we　propose　a　privacy-preserving　data　aggregation　scheme　to　ensure　data　confidentiality,　integrity,　and　real-timeness.　Theoretical　analysis　and　ample　simulations　with　real　trajectory　dataset　indicate　that　the　PERIO　scheme　is　effective　and　makes　a　reasonable　balance　between　retaining　high　QoCS　and　privacy.　©　2005-2012　IEEE.