Government data sharing, a cornerstone of digital governance, is strategically vital for enhancing public services and maximizing data utility. While Ciphertext-Policy Attribute-Based Encryption (CP-ABE) offers a promising cryptographic solution for secure government data sharing due to its flexible access control, existing schemes face substantial limitations in policy privacy, dynamic permission management, and verifiability of query results, thus hindering real-world e-governance deployment. To address these challenges, we propose GovLink, an auditable, full policy hiding CP-ABE scheme that supports dynamic revocation and verifiable query results. Specifically, by integrating the matrix representation of a linear secret sharing scheme with hidden vector encryption, GovLink achieves fine-grained access control and robust policy hiding. Furthermore, we design an efficient dynamic revocation mechanism utilizing puncturable encryption and Lagrange interpolation, guaranteeing system forward security. To enhance auditability, GovLink introduces lightweight data update verification (using hash and XOR operations) and employs a multiset accumulator to ensure verifiable data update legitimacy and query result integrity. Security analysis confirms that GovLink meets stringent security requirements, including indistinguishability under chosen-plaintext attack and full policy indistinguishability. Empirical evaluations conducted using the JPBC cryptographic library demonstrate GovLink’s practical feasibility and efficiency in its core operations.

Mammogram image is important for breast cancer screening, and typically obtained in a dual-view form, i.e., cranio-caudal (CC) and mediolateral oblique (MLO), to provide complementary information for clinical decisions. However, previous methods mostly learn features from the two views independently, which violates the clinical knowledge and ignores the importance of dual-view correlation in the feature learning. In this paper, we propose a dual-view correlation hybrid attention network (DCHA-Net) for robust holistic mammogram classification. Specifically, DCHA-Net is carefully designed to extract and reinvent deep feature maps for the two views, and meanwhile to maximize the underlying correlations between them. A hybrid attention module, consisting of local relation and non-local attention blocks, is proposed to alleviate the spatial misalignment of the paired views in the correlation maximization. A dual-view correlation loss is introduced to maximize the feature similarity between corresponding strip-like regions with equal distance to the chest wall, motivated by the fact that their features represent the same breast tissues, and thus should be highly-correlated with each other. Experimental results on the two public datasets, i.e., INbreast and CBIS-DDSM, demonstrate that the DCHA-Net can well preserve and maximize feature correlations across views, and thus outperforms previous state-of-the-art methods for classifying a whole mammogram as malignant or not.