Dictionary Learning-Enabled Privacy Preserving Semantic Communication System

For deep learning-enabled semantic communication, existing privacy protection methods only take into account the presence of eavesdropper while ignoring malicious receiver aiming to detect confidential information. Only information-theoretical security can transmitter defend against malicious receiver. However, private information are always entangled with pragmatic information in feature space, which leads global perturbation to degrade communication performance. To handle these difficulties, in this paper a privacy preserving semantic communication system is proposed. Different from traditional paradigm, a novel privacy preserving semantic encoder is designed to realize targeted privacy protection while remaining useful information unaffected. Within proposed privacy preserving semantic encoder, feature decoupling module aims to disentangle semantic information by learning two sets of basis vectors which can express private and pragmatic information of data, respectively. Accordingly differential privacy mechanism is employed to provide information-theoretical security. Experimental results demonstrate that proposed method not only achieves better communication performance in both data recovery and pragmatic task, but also more effectively degrades the accuracy of malicious receiver to infer sensitive information than global perturbation does.