Mobile　crowdsensing　has　emerged　as　a　popular　platform　to　solve　many　challenging　problems　by　utilizing　users＇　wisdom　and　resources.　Due　to　user　diversity,　the　data　provided　by　different　individuals　may　vary　significantly,　and　thus　it　is　important　to　analyze　data　quality　during　data　aggregation.　Truth　discovery　is　effective　in　capturing　data　quality　and　obtaining　accurate　mobile　crowdsensing　results.　Existing　works　on　truth　discovery　either　cannot　protect　both　task　privacy　and　data　privacy,　or　introduce　tremendous　computational　costs.　In　this　paper,　we　propose　an　efficient　and　strong　privacy-preserving　truth　discovery　scheme,　named　EPTD,　to　protect　users＇　task　privacy　and　data　privacy　simultaneously　in　the　truth　discovery　procedure.　In　EPTD,　we　first　exploit　the　randomizable　matrix　to　express　users＇　tasks　and　sensory　data.　Then,　based　on　the　matrix　computation　properties,　we　design　key　derivation　and　(re-)encryption　mechanisms　to　enable　truth　discovery　to　be　performed　in　an　efficient　and　privacy-preserving　manner.　Through　a　detailed　security　analysis,　we　demonstrate　that　data　privacy　and　task　privacy　are　well　preserved.　Extensive　experiments　based　on　real-world　and　simulated　mobile　crowdsensing　applications　show　EPTD　has　practical　efficiency　in　terms　of　computational　cost　and　communication　overhead.