With　the　increasing　popularity　of　GPS-equipped　mobile　devices　in　cloud-assisted　fog　computing　scenarios,　massive　spatio-textual　data　is　generated　and　outsourced　to　cloud　servers　for　storage　and　analysis.　Existing　privacy-preserving　range　query　or　ranked　keyword　search　schemes　does　not　support　a　unified　index,　and　are　just　applicable　for　the　symmetric　environment　where　all　users　sharing　the　same　secret　key.　To　solve　this　issue,　we　propose　a　Privacy-preserving　Ranked　Spatial　keyword　Query　in　mobile　cloud-assisted　Fog　computing　(PRSQ-F).　Specifically,　we　design　a　novel　comparable　product　encoding　strategy　that　combines　both　spatial　and　textual　conditions　tightly　to　retrieve　the　objects　in　query　range　and　with　the　highest　textual　similarity.　Then,　we　use　a　new　conversion　protocol　and　attribute-based　encryption　to　support　privacy-preserving　retrieval　and　malicious　user　traceability　in　the　asymmetric　environment　where　different　query　users　have　different　keys.　Furthermore,　we　construct　an　R-tree-based　index　to　achieve　faster-than-linear　retrieval.　Our　formal　security　analysis　shows　that　data　security　can　be　guaranteed.　Our　empirical　experiments　using　a　real-world　dataset　demonstrate　the　efficiency　and　feasibility　of　PRSQ-F.