Image　denoising　is　a　fundamental　tool　in　the　fields　of　image　processing　and　computer　vision.　With　the　rapid　development　of　multimedia　and　cloud　computing,　it　has　become　popular　for　resource-constrained　users　to　outsource　the　storage　and　denoising　of　massive　images.　However,　it　may　cause　privacy　concerns　and　response　delays.　In　this　scenario,　we　propose　an　efFicient　privAcy-preseRving　Image　deNoising　schEme　(FARINE)　for　outsourcing　digital　images.　By　introducing　a　key　conversion　mechanism,　FARINE　allows　removing　noise　from　a　given　noisy　image　using　a　non-local　mean　way　without　leaking　any　information　about　the　plaintext　content.　Due　to　its　low　computational　latency/communication　cost,　edge　computing　is　considered　to　improve　the　user　experience.　To　achieve　a　dynamic　user　set　efficiently,　we　design　a　fine-grained　access　control　mechanism　to　support　user　authorization　and　revocation　in　multi-user　scenarios.　Extensive　experiments　over　several　benchmark　data　sets　show　that　FARINE　obtains　comparable　performance　to　plaintext　image　denoising.　©　The　Author(s)　2024.