|Title||DeepObfuscator: Obfuscating Intermediate Representations with Privacy-Preserving Adversarial Learning on Smartphones|
|Publication Type||Conference Paper|
|Year of Publication||2021|
|Authors||A Li, J Guo, H Yang, FD Salim, and Y Chen|
|Conference Name||Iotdi 2021 Proceedings of the 2021 International Conference on Internet of Things Design and Implementation|
Deep learning has been widely applied in many computer vision applications, with remarkable success. However, running deep learning models on mobile devices is generally challenging due to the limitation of computing resources. A popular alternative is to use cloud services to run deep learning models to process raw data. This, however, imposes privacy risks. Some prior arts proposed sending the features extracted from raw data (e.g., images) to the cloud. Unfortunately, these extracted features can still be exploited by attackers to recover raw images and to infer embedded private attributes (e.g., age, gender, etc.). In this paper, we propose an adversarial training framework, DeepObfuscator, which prevents the usage of the features for reconstruction of the raw images and inference of private attributes. This is done while retaining useful information for the intended cloud service (i.e., image classification). DeepObfuscator includes a learnable encoder, namely, obfuscator that is designed to hide privacy-related sensitive information from the features by performing our proposed adversarial training algorithm. The proposed algorithm is designed by simulating the game between an attacker who makes efforts to reconstruct raw image and infer private attributes from the extracted features and a defender who aims to protect user privacy. By deploying the trained obfuscator on the smartphone, features can be locally extracted and then sent to the cloud. Our experiments on CelebA and LFW datasets show that the quality of the reconstructed images from the obfuscated features of the raw image is dramatically decreased from 0.9458 to 0.3175 in terms of multi-scale structural similarity (MS-SSIM). The person in the reconstructed image, hence, becomes hardly to be re-identified. The classification accuracy of the inferred private attributes that can be achieved by the attacker is significantly reduced to a random-guessing level, e.g., the accuracy of gender is reduced from 97.36% to 58.85%. As a comparison, the accuracy of the intended classification tasks performed via the cloud service is only reduced by 2%. We also demonstrate the efficiency of DeepObfuscator, showcasing real-time performance of the deployed models on smartphones.