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سی و یکمین کنفرانس بین المللی مهندسی برق
Privacy-Preserving Learning using Autoencoder-based Structure
نویسندگان :
Mohammad Ali Jamshidi
1
Hadi Veisi
2
Mohammad Mahdi Mojahedian
3
Mohammad Reza Aref
4
1- Sharif university of technology
2- University of Tehran
3- Sharif university of technology
4- Sharif university of technology
کلمات کلیدی :
Privacy،Utility،Deep Neural Networks،Autoencoders
چکیده :
The need for privacy makes data centers not provide their datasets to inference centers. On the other hand, inference centers need more data to train learning algorithms and provide suitable and acceptable services. Therefore, the existence of a structure that can keep the data confidential while maintaining its usefulness for utility providers is of great importance. In this paper, by modifying the structure of the autoencoder, a method is presented that manages the trade-off between utility and privacy. Moreover, the performance of the proposed method has been evaluated by simulation.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.7.2
