When Disentanglement Is Not Enough: Identifiability and Semantic Privacy in Learned Communications

Advances in representation learning allow for new and previously unfeasible approaches for efficient and secure data transmission over noisy channels. A wireless communication system based on separate source coding, i.e., compression, encryption, and channel coding, i.e., error correcting coding, is proven to be asymptotically optimal and is currently widely used in modern secure wireless networks. Joint source-channel coding is an alternative with a range of advantages over the separation based scheme, including a better communication performance in short data blocklengths and graceful performance degradation with worsening channel quality. Although the integration of encryption, including feature-selective encryption, into the joint source-channel coding is addressed in the literature, it is not studied in the context of capabilities of the existing representation learning models. In this work, we first prove that zero information leakage about the private semantic concept from public latent components is generally not achievable in unsupervised representation learning. We then show that weakly supervised disentangled representation learning models based on variational autoencoders can overcome this limitation and can be used as a base model for semantic factor disentanglement in a joint source-channel-encryption coding.