With　the　rapid　growth　of　encrypted　image　data　outsourced　to　cloud　servers,　achieving　data　confidentiality　and　searchability　in　cloud-assisted　Internet　of　Things　(IoT)　environments　has　become　increasingly　feasible.　However,　achieving　high　efficiency　and　strong　security　simultaneously　over　large-scale　encrypted　image　datasets　remains　a　challenge.　To　address　this,　we　propose　a　novel　efficient　and　secure　content-based　image　retrieval　scheme　in　cloud-assisted　IoT.　Specifically,　our　scheme　leverages　lattice-based　fully　homomorphic　encryption　and　homomorphic　comparison　techniques,　utilizing　Cheon-Kim-Kim-Song＇s　batch　processing　and　single-instruction-multiple-data　capabilities.　This　approach　significantly　reduces　the　overhead　of　fully　homomorphic　computations,　making　the　query　process　computational　complexity　independent　of　dataset　size　under　certain　conditions.　Moreover,　by　integrating　private　information　retrieval　technology,　the　scheme　enhances　privacy　by　hiding　access　patterns　of　image　data.　Formal　security　analysis　demonstrates　that　our　scheme　achieves　indistinguishability　against　chosen-plaintext　attack　(IND-CPA),　and　extensive　experiments　based　on　real　datasets　confirm　that　our　scheme　is　both　practical　and　efficient　for　real-world　applications.