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تكنوضاد ذكاء اصطناعي

هجمات استخلاص العضوية | MEMBERSHIP INFERENCE ATTACKS

وهو أسلوب لحماية خصوصية البيانات المستخدمة في نماذج التعلم الآلي. وينطوي على بناء خوارزميات قادرة على استنتاج ما إذا كانت عينة محددة موجودة في مجموعة بيانات التدريب. وكلما ارتفعت دقة استنتاج الخوارزمية، كان نموذج الذكاء الاصطناعي أقلّ حماية للخصوصية. إن أهم ما يميز هذا الأسلوب هو استخدامه لمُخرجات النموذج فقط ودون الحاجة إلى الوصول إلى أوزان النموذج أو عينات الإدخال. كما أنه لا يتطلب إعادة تدريب النموذج، مما يجعل تنفيذه سهلاً ولا يستغرق وقتاً طويلاً.
وقد لجأت شركة جوجل إلى استخدام هذه الطريقة مؤخراً- إلى جانب الخصوصية التفاضلية-لتعزيز خصوصية نماذج التعلم الآلي التي يتم بناؤها باستخدام منصة تنسور فلو (TensorFlow).

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