Person　re-identification　(Person　Re-ID)　is　widely　regarded　as　a　promising　technique　to　identify　a　target　person　through　surveillance　cameras　in　the　wild.　Nevertheless,　person　Re-ID　leads　to　severe　personal　image　privacy　concerns　as　personal　images　are　stipulated　by　laws　and　guidelines　as　private　data.　To　address　these　concerns,　this　article　explores　the　first　solution　for　building　a　privacy-preserving　person　Re-ID　system.　Specifically,　this　article　formulizes　privacy-preserving　person　Re-ID　as　similarity　metrics　of　encrypted　feature　vectors　because　the　underlying　operation　of　person　Re-ID　is　to　compute　the　similarity　of　feature　vectors　that　are　extracted　from　person　images　by　a　machine　learning　model.　However,　feature　vectors　are　generally　denoted　by　floating-point　numbers.　To　this　end,　this　article　exploits　a　series　of　new　encoding　mechanisms　and　secure　batch　computing　protocols　to　encrypt　floating-point　feature　vectors　and　achieve　the　underlying　operation　of　person　Re-ID.　Rigorous　theoretical　analyses　demonstrate　that　this　work　achieves　person　Re-ID　without　compromising　any　personal　image　privacy.　Furthermore,　the　proposed　secure　batch　protocols　significantly　enhance　the　performance　of　privacy-preserving　person　Re-ID　while　outputting　the　same　precision　as　the　previous　method.