The issue of association privacy leakage has become increasingly critical during data release and usage. However, traditional privacy protection techniques often struggle to address privacy leakage resulting from implicit associations within the data. In this paper, we propose a novel mechanism based on Cobweb Privacy to safeguard association privacy more comprehensively. Firstly, we design the concept of ϵ-Cobweb Privacy (ϵ-CP) specifically to address association privacy leakage. This concept extends the traditional notion of differential privacy by incorporating associated prior knowledge, thereby offering more effective and comprehensive protection of association privacy. We further demonstrate its privacy guarantees through the theoretical analysis of the relationship between ϵ-CP, differential privacy, and pufferfish privacy. Secondly, we quantify the privacy leakage problem mathematically and examine the utility privacy trade off under various priors. Additionally, we present a universal framework for association privacy protection in data aggregation scenarios using the ϵ-CP mechanism. Finally, this framework is integrated with three different noise addition methods and compared against mechanisms based on differential privacy and pufferfish privacy, and its utility is validated through experiments on both non-temporal and temporal real-world datasets. The results show that ϵ-CP provides distinct advantages in the utility privacy trade-off.

Motor imagery (MI) engages a broad network of brain regions to imagine a specific action. Investigating the mechanism of brain network reorganization during MI after spinal cord injury (SCI) is crucial because it reflects overall brain activity. Using electroencephalogram (EEG) data from SCI patients, we conducted EEG-based coherence analysis to examine different brain network reorganizations across different frequency bands, from resting to MI. Furthermore, we introduced a consistency calculation-based residual graph convolution (C-ResGCN) classification algorithm. The results show that the [Formula: see text]- and [Formula: see text]-band connectivity weakens, and brain activity decreases during the MI task compared to the resting state. In contrast, the [Formula: see text]-band connectivity increases in motor regions while the default mode network activity declines during MI. Our C-ResGCN algorithm showed excellent performance, achieving a maximum classification accuracy of 96.25%, highlighting its reliability and stability. These findings suggest that brain reorganization in SCI patients reallocates relevant brain resources from the resting state to MI, and effective network reorganization correlates with improved MI performance. This study offers new insights into the mechanisms of MI and potential biomarkers for evaluating rehabilitation outcomes in patients with SCI.