Multimodal Recommendation Systems (MRSs) boost traditional user-item interaction-based methods by incorporating multimodal information. However, existing methods ignore the inherent noise brought by (1) noisy semantic priors in multimodal content, and (2) noisy user interactions in history records, therefore diminishing model performance. To fill this gap, we propose to denoise MRSs by jointly EValuating structure Effectiveness and mitigating Noisy links (EVEN). Firstly, for semantic prior noise in multimodal content, EVEN builds item homogeneous consistency and denoises it by evaluating behavior-driven confidence. Secondly, for noise in user interactions, EVEN updates user feedback by denoising observed interactions following implicit contribution evaluation of high-order representations. Thirdly, EVEN performs cross-modal alignment through self-guided structure learning, reinforcing task-specific inter-modal dependency modeling and cross-modal fusion. Through extensive experiments on three widely-used datasets, EVEN achieves an average improvement of 8.95% and 5.90% in recommendation accuracy compared with LGMRec and FREEDOM, respectively, without extending the total training time.

Graph Neural Networks (GNNs) have demonstrated remarkable potential in various downstream tasks by effectively capturing the relational dependencies among nodes in graphs. However, this capability also brings significant privacy risks: when GNNs encode topological information and node features into their output, sensitive information can be inadvertently exposed, leading to severe privacy breaches. Existing privacy-preserving GNNs primarily focus on protecting the existence of individual nodes or edges, overlooking practical scenarios where nodes and edges are often publicly accessible and only specific sensitive attributes require protection, resulting in a lack of consideration for attribute sensitivity and challenges in balancing privacy and utility. In this paper, we study the problem of hiding sensitive information during GNN training and limiting its exposure in the outputs, while better defending against attribute inference attacks (AIAs) and achieving improved performance. To achieve this, we propose an attribute-oriented differentially private graph neural network (AODP-GNN) that enforces attribute-specific privacy guarantees through dynamic privacy budgets and relevance-aware noise injection, optimizing the balance between privacy and utility. Specifically, we design a neighborhood-aware private embedding generation mechanism and a mutual information minimization-based optimization strategy that operate before the deep interactions of feature interaction and model optimization to strengthen defense against AIAs. To enhance the balance between privacy and utility, we further develop a relevance-grained noise adaptation technique that dynamically allocates higher noise to less relevant attributes. Theoretical analysis shows that the AODP-GNN satisfies privacy guarantees. Extensive experiments conducted on four real-world datasets demonstrate that our approach can achieve up to around 10.04% and 9.21% higher accuracy compared to the state-of-the-art centrally differentially private GNN ProGAP and DPDGC, and also shows a higher defense capability against AIAs.

Differential-Private Graph Neural Networks (DP-GNNs) have generated remarkable research results, enabling them to effectively tackle the privacy leakage problem in graph learning. However, most DP-GNNs do not consider the temporal-dimensional scenarios. In tasks involving spatiotemporal graph training, such as wireless social networks analysis, the sensitive interactive information in each time graph should be protected. Therefore, we propose spatio-temporal dynamic graph learning with enhanced local differential privacy (LDP-STG). First, we design a weighted graph perturbation encoder based on the Bernoulli distribution and Laplace mechanism, which protects the structures of weighted graphs in time series under edge-level local differential privacy. Second, we employ an attention mechanism to learn dynamic graph node embedding from spatial and temporal dimensions. The theoretical analysis proves that our LDP-STG realizes differential privacy guarantees. We conduct experiments mainly on two communication datasets (i.e., Enron and UCI), which shows that our LDP-STG can achieve better privacy utility tradeoffs compared with traditional mechanisms in terms of snatiotemnoral dimensions.