Key protected classification for collaborative learning

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2020-08-01

Author

Sariyildiz, Mert Bulent
Cinbiş, Ramazan Gökberk
Ayday, Erman

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© 2020Large-scale datasets play a fundamental role in training deep learning models. However, dataset collection is difficult in domains that involve sensitive information. Collaborative learning techniques provide a privacy-preserving solution, by enabling training over a number of private datasets that are not shared by their owners. However, recently, it has been shown that the existing collaborative learning frameworks are vulnerable to an active adversary that runs a generative adversarial network (GAN) attack. In this work, we propose a novel classification model that is resilient against such attacks by design. More specifically, we introduce a key-based classification model and a principled training scheme that protects class scores by using class-specific private keys, which effectively hide the information necessary for a GAN attack. We additionally show how to utilize high dimensional keys to improve the robustness against attacks without increasing the model complexity. Our detailed experiments demonstrate the effectiveness of the proposed technique. Source code will be made available at https://github.com/mbsariyildiz/key-protected-classification.

Subject Keywords

Signal Processing, Software, Artificial Intelligence, Computer Vision and Pattern Recognition

URI

https://hdl.handle.net/11511/56186

Journal

Pattern Recognition

DOI

https://doi.org/10.1016/j.patcog.2020.107327

Collections

Department of Computer Engineering, Article

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Citation Formats

M. B. Sariyildiz, R. G. Cinbiş, and E. Ayday, “Key protected classification for collaborative learning,” Pattern Recognition, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56186.