کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4970159 | 1450030 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Differential privacy and generalization: Sharper bounds with applications
ترجمه فارسی عنوان
حریم خصوصی و تعمیم دیفرانسیل: مرزهای واضح با برنامه ها
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
In this paper we deal with the problem of improving the recent milestone results on the estimation of the generalization capability of a randomized learning algorithm based on Differential Privacy (DP). In particular, we derive new DP based multiplicative Chernoff and Bennett type generalization bounds, which improve over the current state-of-the-art Hoeffding type bound. Then, we prove that a randomized algorithm based on the data generating dependent prior and data dependent posterior Boltzmann distributions of Catoni (2007) [10] is Differentially Private and shows better generalization properties than the Gibbs classifier associated to the same distributions. With this aim, we also exploit a simple example. Finally, we discuss the advantages of using the Thresholdout procedure, one of the main results generated by the DP theory, for Model Selection and Error Estimation purposes, and we derive a new result which exploits our new generalization bounds.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 89, 1 April 2017, Pages 31-38
Journal: Pattern Recognition Letters - Volume 89, 1 April 2017, Pages 31-38
نویسندگان
Luca Oneto, Sandro Ridella, Davide Anguita,