Mobile　crowdsensing　(MCS)　systems　typically　struggle　to　address　the　challenge　of　data　aggregation,　incentive　design,　and　privacy　protection,　simultaneously.　However,　existing　solutions　usually　focus　on　one　or,　at　most,　two　of　these　issues.　To　this　end,　this　paper　presents　CROWDFA,　a　novel　paradigm　for　privacy-preserving　MCS　through　federated　analytics　(FA),　which　aims　to　achieve　a　well-rounded　solution　encompassing　data　aggregation,　incentive　design,　and　privacy　protection.　Specifically,　inspired　by　FA,　CRWODFA　initiates　an　MCS　computing　paradigm　that　enables　data　aggregation　and　incentive　design.　Participants　can　perform　aggregation　operations　on　their　local　data,　facilitated　by　CROWDFA,　which　supports　various　common　data　aggregation　operations　and　bidding　incentives.　To　address　privacy　concerns,　CROWDFA　relies　solely　on　an　efficient　cryptographic　primitive　known　as　additive　secret　sharing　to　simultaneously　achieve　privacy-preserving　data　aggregation　and　privacy-preserving　incentive.　To　instantiate　CROWDFA,　this　paper　presents　a　privacy-preserving　data　aggregation　scheme　(PRADA)　based　on　CROWDFA,　capable　of　supporting　a　range　of　data　aggregation　operations.　Additionally,　a　CROWDFA-based　privacy-preserving　incentive　mechanism　(PRAED)　is　designed　to　ensure　truthful　and　fair　incentives　for　each　participant,　while　maximizing　their　individual　rewards.　Theoretical　analysis　and　experimental　evaluations　demonstrate　that　CROWDFA　protects　participants’　data　and　bid　privacy　while　effectively　aggregating　sensing　data.　Notably,　CROWDFA　outperforms　state-of-the-art　approaches　by　achieving　up　to　22　times　faster　computation　time.　IEEE