Person　re-identification　(Re-ID)　has　attracted　extensive　attention　due　to　its　potential　to　identify　a　person　of　interest　from　different　surveillance　videos.　With　the　increasing　amount　of　the　surveillance　videos,　high　computation　and　storage　costs　have　posed　a　great　challenge　for　the　resource-constrained　users.　In　recent　years,　the　cloud　storage　services　have　made　a　large　volume　of　video　data　outsourcing　become　possible.　However,　person　Re-ID　over　outsourced　surveillance　videos　could　lead　to　a　security　threat,　i.e.,　the　privacy　leakage　of　the　innocent　person　in　these　videos.　Therefore,　we　propose　an　efFicient　privAcy-preseRving　peRson　Re-ID　Scheme　(FARRIS)　over　outsourced　surveillance　videos,　which　can　ensure　the　privacy　of　the　detected　person　while　providing　the　person　Re-ID　service.　Specifically,　FARRIS　exploits　the　convolutional　neural　network　(CNN)　and　kernels　based　supervised　hashing　(KSH)　to　extract　the　efficient　person　Re-ID　feature.　Then,　we　design　a　secret　sharing　based　Hamming　distance　computation　protocol　to　allow　cloud　servers　to　calculate　similarities　among　obfuscated　feature　indexes.　Furthermore,　a　dual　Merkle　hash　trees　based　verification　is　proposed,　which　permits　users　to　validate　the　correctness　of　the　matching　results.　The　extensive　experimental　results　and　security　analysis　demonstrate　that　FARRIS　can　work　efficiently,　without　compromising　the　privacy　of　the　involved　person.　CCBY