Privacy　inference　poses　a　significant　threat　to　users　of　online　social　networks　(OSNs).　To　deal　with　this　issue,　a　number　of　privacy-enhancing　technologies　have　been　proposed　with　the　goal　of　achieving　a　balance　between　the　protection　of　privacy　and　the　utility　of　data.　Previous　studies,　however,　failed　to　take　into　consideration　the　impact　of　the　interdependency　of　privacy　(IoP),　which　dictates　that　privacy　decisions　made　by　some　users　may　affect　the　privacy　of　some　other　users.　The　implication　of　IoP　is　that　too　much　privacy　may　be　disclosed　when　multiple　individuals　share　data　with　the　same　data　accessor　because　privacy　conflicts　resulting　from　independent　privacy　decisions　would　make　it　possible　for　adversaries　to　infer　the　privacy　of　the　target　user.　Ideally,　cooperation　that　preserves　privacy　should　allow　OSN　users　to　respect　each　other＇s　privacy　specifications　so　as　to　resolve　such　privacy　conflicts　caused　by　independent　privacy　decisions　of　individuals.　To　facilitate　the　design,　we　propose　a　privacy-preserving　cooperation　framework　based　on　the　evolutionary　game　theory　to　facilitate　such　cooperation.　Based　on　the　framework,　the　dynamics　of　user　strategies　regarding　whether　to　participate　in　the　cooperation　are　analyzed　and　an　evolutionary　stable　state　is　derived　to　serve　as　the　basis　for　incentivizing　users　to　participate　in　cooperative　privacy　protection.　Experiments　based　on　real　OSN　data　show　that　the　proposed　cooperation　framework　is　effective　in　modeling　the　behaviors　of　users　and　that　the　proposed　incentive　allocation　method　can　incentivize　users　to　participate　in　the　cooperation.　The　proposed　cooperation　framework　can　not　only　helps　lower　the　threat　to　user　privacy　resulting　from　privacy　inference　by　data　accessors　but　also　allows　OSN　service　providers　to　design　effective　privacy　protection　policies.