With　the　rapid　development　of　deep　learning,　face　recognition　technology　based　on　deep　learning　has　been　widely　developed　in　recent　years.　However,　during　the　training　of　the　deep　learning　model,　there　is　a　risk　of　privacy　leakage.　If　an　attacker　obtains　private　data,　such　as　tags　of　the　training　data,　the　face　images　may　be　restored,　and　private　information　is　leaked.　To　pro-tect　the　private　information　of　the　face　recognition　model,　we　introduce　differential　privacy　technology　to　propose　a　privacy　-　preserving　face　recognition　scheme　using　the　Siamese　Network　framework　called　DP-Face.　Unlike　other　privacy-preserving　face　recognition　methods,　we　can　adjust　the　balance　between　privacy　and　utility　through　privacy　budgets　according　to　actual　needs.　Experimental　results　show　that　the　effectiveness　and　privacy　of　the　proposed　DP-Face　can　be　well　guaranteed.　©　2021　IEEE.