MADE: a Masked Autoencoder Based Desensitization Framework for Encrypted Traffic

In recent years, encryption algorithms have become widely adopted as a trusted means of securing data transmission, effectively safeguarding against unauthorized access. However, providing raw encrypted traffic directly to researchers can still pose privacy risks, making it essential to implement desensitization measures before sharing such data. The main challenge is to desensitize the encrypted traffic in a privacyprotection way while preserving as much of the original traffic characteristics as possible to ensure the downstream tasks run properly. In this paper, we propose MADE, an encrypted traffic desensitization framework based on Masked AutoEncoder (MAE). This framework transforms the traffic desensitization task into a classifier-guided image reconstruction problem, using supervised signals to achieve state-aware traffic reconstruction, which ensures the utility as well as the safety of the desensitized traffic for downstream tasks. Moreover, MADE is highly scalable, permitting users to customize which data attributes to preserve in the traffic being processed, simply by modifying the classifier. Experimental results reveal that our proposed desensitization framework not only outperforms baseline methods but also exhibits high applicability across various scenarios.