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Register a new userPrivacy Regularization: Joint Privacy-Utility Optimization in LanguageModels
تنظيم الخصوصية: تحسين الخصوصية المضادة للخصوصية في LanguageModels
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من المعروف أن نماذج اللغة العصبية لديها سعة عالية لتحفيظ عينات التدريب.قد يكون لهذا تصرفات خصوصية خطيرة عند نماذج التدريب على محتوى المستخدم مثل مراسلات البريد الإلكتروني.يأتي الخصوصية التفاضلية (DP)، وهو خيار شعبي لتدريب النماذج مع ضمانات الخصوصية، بتكاليف كبيرة من حيث تدهور المرافق والتأثير المتباين على المجموعات الفرعية للمستخدمين.في هذا العمل، نقدم طريقتين مع الحفاظ على الخصوصية لنماذج اللغة التدريبية التي تمكن التحسين المشترك للأداة المساعدة والخصوصية من خلال (1) استخدام تمييزي (2) إدراج مصطلح خسائر ثلاثية جديدة.نقارن أساليبنا مع موانئ دبي من خلال تقييم واسع النطاق.نظهر مزايا المتداولين لدينا مع مفاضلة خصوصية فائدة مواتية، تدرب أسرع مع القدرة على الاستفادة من أساليب التحسين الحالية، وضمان علاج موحد للمجموعات الفرعية الممثلة تمثيلا ممثلي.
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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