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Register a new userTowards Task-Agnostic Privacy- and Utility-Preserving Models
نحو نماذج الخصوصية والفائدة المرجعية للمهمة
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تعتمد نماذج التعلم العميق الحديثة لمعالجة اللغة الطبيعية بشكل كبير على كميات كبيرة من النصوص المشروح.ومع ذلك، قد يكون الحصول على مثل هذه النصوص صعبة عندما تحتوي على معلومات شخصية أو سرية، على سبيل المثال، في المجالات الصحية أو القانونية.في هذا العمل، نقترح طريقة لإلغاء تحديد المستندات النصية النموذج الحرة من خلال تقسيم البيانات الحساسة بعناية فيها.نظهر أن طريقتنا تحافظ على الأداة المساعدة للبيانات لتصنيف النصوص ووضع التسلسل والتسجيل الإجابة على المهام.
Modern deep learning models for natural language processing rely heavily on large amounts of annotated texts. However, obtaining such texts may be difficult when they contain personal or confidential information, for example, in health or legal domains. In this work, we propose a method of de-identifying free-form text documents by carefully redacting sensitive data in them. We show that our method preserves data utility for text classification, sequence labeling and question answering tasks.
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Neural language models are known to have a high capacity for memorization of training samples. This may have serious privacy im- plications when training models on user content such as email correspondence. Differential privacy (DP), a popular choice
to train models with privacy guarantees, comes with significant costs in terms of utility degradation and disparate impact on subgroups of users. In this work, we introduce two privacy-preserving regularization methods for training language models that enable joint optimization of utility and privacy through (1) the use of a discriminator and (2) the inclusion of a novel triplet-loss term. We compare our methods with DP through extensive evaluation. We show the advantages of our regularizers with favorable utility-privacy trade-off, faster training with the ability to tap into existing optimization approaches, and ensuring uniform treatment of under-represented subgroups.
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