Some　attackers　in　the　Internet　of　Things　submit　falsified　high-quality　data　to　cause　harm　to　users.　To　prevent　malicious　workers　from　reporting　untruthful　data　for　skyline　computation　in　Mobile　Crowd　Sensing,　we　propose　a　double　trust　check-based　spatiotemporal　data　acquisition　scheme,　DTC-MDD.　In　DTC-MDD,　worker　trust　uses　four-way　validation　to　obtain　reliable　worker　trust　evaluations.　Then,　based　on　Probabilistic　Skyline　Calculation,　we　propose　a　worker　selection　algorithm　to　select　high-trust,　high-quality　workers　for　data　reporting.　We　also　introduce　the　Non-Interactive　Encrypted　Integer　Comparison　Protocol　to　safeguard　privacy　between　workers　and　users　from　malicious　attacks.　Finally,　through　extensive　simulations　on　real　datasets,　DTC-MDD　effectively　enhances　the　quality　and　security　of　spatiotemporal　data　acquisition.　DTC-MDD　improved　the　data　quality　and　reliability　of　candidate　worker　sets　by　16.2%　and　49.1%,　respectively,　and　the　data　quality　and　reliability　of　the　first　skyline　worker　by　21.4%　and　320.0%,　respectively.　©　2023　Elsevier　Inc.