Jialiang Han

Yaofei Wang, Rui Wang, Weilong Pang et al.

Generative linguistic steganography (GLS) enables covert communication by embedding secret messages into the natural language generation process. In practical deployment, however, GLS is vulnerable to tokenization ambiguity: the same surface text may be re-tokenized into a different token sequence at the receiver, breaking the shared decoding state between the communicating parties so that a single local mismatch can propagate into complete extraction failure. Existing solutions either remove ambiguous tokens -- distorting the generation distribution and compromising security -- or preserve the distribution at the cost of substantially reduced embedding capacity or prohibitive runtime overhead. To address this issue, we propose ReTokSync (Re-Tokenization Synchronization), a self-synchronizing disambiguation framework that monitors the receiver-view tokenization during generation and triggers a corrective reset only when ambiguity actually occurs. By confining the effect of tokenization ambiguity to sparse residual bit errors rather than global desynchronization, ReTokSync leaves ambiguity-free positions entirely untouched and remains compatible with the underlying steganographic algorithm. Experiments on both English and Chinese settings show that ReTokSync stays closest to the steganographic baseline in distributional security (zero KL divergence), text quality, embedding capacity, and runtime, while achieving extraction accuracy above 99.7\%. Building on this property, we further develop a two-channel covert communication mechanism in which ReTokSync serves as the primary channel and a reliable auxiliary channel corrects the remaining errors, achieving 100\% end-to-end recovery across all evaluated configurations.

Jialiang Han, Yun Ma

Mobile devices enable users to retrieve information at any time and any place. Considering the occasional requirements and fragmentation usage pattern of mobile users, temporal recommendation techniques are proposed to improve the efficiency of information retrieval on mobile devices by means of accurately recommending items via learning temporal interests with short-term user interaction behaviors. However, the enforcement of privacy-preserving laws and regulations, such as GDPR, may overshadow the successful practice of temporal recommendation. The reason is that state-of-the-art recommendation systems require to gather and process the user data in centralized servers but the interaction behaviors data used for temporal recommendation are usually non-transactional data that are not allowed to gather without the explicit permission of users according to GDPR. As a result, if users do not permit services to gather their interaction behaviors data, the temporal recommendation fails to work. To realize the temporal recommendation in the post-GDPR era, this paper proposes $C^3DRec$, a cloud-client cooperative deep learning framework of mining interaction behaviors for recommendation while preserving user privacy. $C^3DRec$ constructs a global recommendation model on centralized servers using data collected before GDPR and fine-tunes the model directly on individual local devices using data collected after GDPR. We design two modes to accomplish the recommendation, i.e. pull mode where candidate items are pulled down onto the devices and fed into the local model to get recommended items, and push mode where the output of the local model is pushed onto the server and combined with candidate items to get recommended ones. Evaluation results show that $C^3DRec$ achieves comparable recommendation accuracy to the centralized approaches, with minimal privacy concern.