اقترح مخطط التعلم الخاص Texthide مؤخرا لحماية البيانات النصية الخاصة أثناء مرحلة التدريب عبر ترميز المثيل المزعوم.نقترح هجوم إعادة الإعمار الجديد لكسر Texthide من خلال استعادة بيانات التدريب الخاص، وبالتالي تكشف النقاب عن مخاطر الخصوصية على ترميز المثيل.لقد صادقنا تجريبيا فعالية هجوم إعادة الإعمار مع مجموعات بيانات شائعة الاستخدام لتصنيف الجملة.إن هجومنا ستقدم تطوير التعلم في الحفاظ على الخصوصية في سياق معالجة اللغة الطبيعية.
A private learning scheme TextHide was recently proposed to protect the private text data during the training phase via so-called instance encoding. We propose a novel reconstruction attack to break TextHide by recovering the private training data, and thus unveil the privacy risks of instance encoding. We have experimentally validated the effectiveness of the reconstruction attack with two commonly-used datasets for sentence classification. Our attack would advance the development of privacy preserving machine learning in the context of natural language processing.
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