Abstract
Deep learning model training on cloud platforms typically require users to upload raw input data. However, uploading raw image data to cloud service providers raises serious privacy concerns. To address this problem, we propose a Cycle-Gan based-image transformation scheme that leverages convolutional autoencoder image encoding for domain translation. Our Cycle-GAN based image transformation scheme enhances privacy of deep neural networks while preserving model utility. In this paper, we demonstrate that our Cycle-GAN based image transformation scheme protects visual feature information of sensitive image data. We evaluate the effectiveness of our proposed method to preserve model utility using classification accuracy and robustness against reconstruction attacks using structural similarity index measure (SSIM). The classification accuracy of encoded images using our proposed method is 92.48, 91.05, 90.37 for Chest X-ray, Dermoscopy and OCT datasets, respectively. The SSIM scores for reconstruction attacks where the attacker only has access to the encoded data and corresponding labels are 0.1002, 0.0995 and 0.0329 for Chest X-ray, Dermoscopy and OCT datasets, respectively. Our results demonstrate that the Cycle GAN based encoding scheme effectively enhance privacy while preserving model utility.
Research supported in part by NSF CREST Grant HRD-1736209 (RK) and NSF CAREER Grant CNS-1553696 (RK).
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Rodriguez, D., Krishnan, R. (2023). A Cycle-GAN Based Image Encoding Scheme for Privacy Enhanced Deep Neural Networks. In: Muthukkumarasamy, V., Sudarsan, S.D., Shyamasundar, R.K. (eds) Information Systems Security. ICISS 2023. Lecture Notes in Computer Science, vol 14424. Springer, Cham. https://doi.org/10.1007/978-3-031-49099-6_11
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