L'analyse des énormes masses de données apporte des avantages sans précédents, mais menace également la vie privée des personnes. Nous devons faire face au défi paradoxal d'apprendre des informations utiles sur une population [35] sans rien apprendre sur un individu Beaucoup de solutions ont été proposées pour résoudre ce paradoxe, mais pour chaque solution proposée de nouvelles vulnérabilités ont été trouvées. A la différence des paradigmes précédents, la vie privée différentielle nous fournit une avancée prometteuse: peu importe ce que l'adversaire sait sur vous à partir de sources d'information auxiliaires ,
est-à-dire D et D ne diffèrent que par un seul élément, les distributions de probabilités sur les résultats de D et D fournies par la vie privée différentielle sont " essentiellement les mêmes " . Plus formellement, Definition B.1. Un algorithme randomisé A est (, ?)-differentially private ((, ?)-DP en raccourci ) si, pour deux ensembles quelconques de données voisins D and D , et pour tout résultat O ? Range(A) ,
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