Differential　privacy　provides　strong　privacy　preservation　guarantee　in　information　sharing.　As　social　network　analysis　has　been　enjoying　many　applications,　it　opens　a　new　arena　for　applications　of　differential　privacy.　This　article　presents　a　comprehensive　survey　connecting　the　basic　principles　of　differential　privacy　and　applications　in　social　network　analysis.　We　concisely　review　the　foundations　of　differential　privacy　and　the　major　variants.　Then,　we　discuss　how　differential　privacy　is　applied　to　social　network　analysis,　including　privacy　attacks　in　social　networks,　models　of　differential　privacy　in　social　network　analysis,　and　a　series　of　popular　tasks,　such　as　analyzing　degree　distribution,　counting　subgraphs　and　assigning　weights　to　edges.　We　also　discuss　a　series　of　challenges　for　future　work.