Abstract Website fingerprinting attacks are critical for extracting website information and identifying illegal websites visited by users in anonymous networks such as Tor. However, existing attacks struggle to extract effective features from unmonitored websites due to the diversity. Although increasing unmonitored training data can improve effectiveness, it also increases attacker costs. To address this, we propose a novel website fingerprinting attack that leverages unsupervised Out-of-Distribution detection. We exclusively use monitored website data for model training, eliminating the need for extensive unmonitored samples. For feature extraction, we utilize a combination of Long Short-Term Memory and Convolutional Neural Networks for robust feature extraction of each monitored website. We also introduce a new loss function to maximize differentiation between features of various websites. Furthermore, we employ Singular Value Decomposition to effectively segregate monitored from unmonitored websites. It allows the model to focus on dominant components in the feature vectors, facilitating a clear distinction between monitored and unmonitored website traffic. The experimental results confirm that our method outperforms existing techniques without requiring unmonitored training data.

Federated learning (FL) is vulnerable to gradient-based privacy attacks, where malicious attackers reconstruct training data from exchanged gradients. While existing differential privacy (DP) defenses mitigate this, they often cause excessive additive noise due to the inequality scaling in the theoretical analyses, which degrades the model's utility or fail under adaptive attacks. To address this issue, we proposeFedMSBA, a layer-wise privacy-preservation method that adaptively allocates privacy budgets via Rényi DP (RDP) and modified sensitivity. FedMSBA dynamically scales noise to model intricacies and adaptively choose the better applied DP mechanisms, which provides a tighter mathematical bound and finally prevents non-convergence while resisting reconstruction attacks. Experiments demonstrate superior privacy-utility trade-offs compared to state-of-the-art defenses. FedMSBA achieves an approximately 2% improvement in accuracy and a 5% enhancement in privacy preservation. Furthermore, FedMSBA's performance remains nearly unaffected by variations in the privacy budget$\epsilon$and failure rate$\delta$.

In recent years, federated learning (FL) has continued to evolve with the advent of big data and the large language model (LLM), but it has also exposed numerous security and privacy issues. As a form of distributed machine learning, FL systems are more susceptible to poisoning attacks because training data are dispersed across different participants; additionally, the training achievement of FL may be subject to low-cost theft by some free-riders. Existing works have addressed defenses against the aforementioned two types of threats, but they often focus on defending against only one type and fail to effectively integrate defenses against multiple types of threats. However, in real-world Internet of Things (IoT) systems, the types of threats are not limited to just one category. In this work, we try to maintain the performance of the global model under poisoning attacks, preserve the privacy of the server under free-riders, and explore the balance between these two aspects. Therefore, this work proposes Federated Multi-Stage Asynchronous Roll-back (FedMAR), ensuring the quality of local updates; in addition, this work also provides privacy preservation in the global update process based on Rinyi Differential Privacy (RDP), and offers a certain basis for detecting free-riders. To validate the generalization of the proposed method, we conducted relevant experiments on both image and text datasets, and further investigated the robustness of the proposed method against poisoning attacks, model inversion attacks, data heterogeneity, and other aspects. The testing accuracy of the global model can even be improved by 7.2%.

The next location recommendation is a significant task in spatio-temporal data mining (STDM), leading to an increased interest in the inherent dynamics in large-scale trajectory data. However, existing methods often prioritize the transition of locations while overlooking the collaborative signals between users, resulting in further impacts on modeling the higher-order effects between locations. Additionally, they struggle to extract useful patterns from long sequences, let alone capturing the long sequences that demonstrate the collaborative effects between users and locations. In light of these challenges, we construct a temporal graph based on the order of user movement, enabling both users and locations to utilize collaborative filtering signals. This greatly alleviates issues related to data sparsity and high-quality representation. Simultaneously, we introduce a Dual-View Graph Transformer model(TrajGraph), which samples sequences from spatial and temporal views by a dual-view sequence sampling method, independently encoding each view with a graph transformer to obtain effective representations of visited nodes, effectively addressing the high complexity of the transformer and ensuring efficiency and effectiveness. Extensive experiments on three public location-based service datasets demonstrate that our model can consistently outperform all baselines. Elaborate ablation studies further prove the effectiveness of spatial and temporal factors.

Semantic network is a network of concepts connected by semantic relations. It contains two forms ofbinary semantic networkandmultiplex semantic network. The associated fact prediction is a link prediction task that aims to infer the implicitly connected facts by mining the high-level representation of the network. Previous methods for associated fact prediction put much emphasis on the topological feature of network but not utilize the information of semantic expression. This paper proposes aSemanticNetworkEncoder (SemNE), which learns a feature mapping function from the binary semantic networks and can be applied to the multiplex semantic networks in a pre-training manner. SemNE is a two-stage framework that contains an embedding encoder and a prediction decoder. It jointly models the semantic information and network topology to enrich the network representation. A word self-organization method based on the factual boundary is proposed to unify the topological feature and the semantic feature representations. Experimental results on binary semantic networks show that SemNE achieves the state-of-the-art results in associated fact prediction and experimental results on multiplex semantic networks show that SemNE is scalable and can effectively improve the performance of existing models.