Person　re-identification　(Re-ID)　is　a　critical　technology　to　identify　a　target　person　from　captured　person　images　by　surveillance　cameras.　However,　person　Re-ID　has　triggered　great　concerns　of　personal　image　privacy.　Although　the　law　(e.g.,　GDPR)　has　stipulated　person　images　are　personal　private　data,　there　is　no　an　efficient　solution　to　tackle　the　image　privacy　concern　for　person　Re-ID.　To　this　end,　we　propose　FREED,　the　first　system　solution　for　privacy-preserving　person　Re-ID,　which　supports　the　state-of-the-art　person　Re-ID　operations　on　encrypted　feature　vectors　of　person　images.　To　handle　the　encryption　of　feature　vectors　effectively　and　enable　person　Re-ID　operations　on　encrypted　feature　vectors　efficiently,　FREED　develops　a　suite　of　batch　secure　computing　protocols　based　on　a　twin-server　architecture　and　the　threshold　Paillier　cryptosystem.　We　demonstrate　our　secure　computing　protocols　are　more　efficient　than　existing　protocols　and　FREED　achieves　a　precision　equal　to　the　state-of-the-art　plaintext　method.