Hong Kong University of Science and Technology
Hong Kong University of Science and Technology Institutional RepositoryNot a member yet
162821 research outputs found
Sort by
Attribute-based publicly verifiable secret sharing
No full textCan a dealer share a secret without knowing the shareholders? We provide a positive answer to this question by introducing the concept of an attribute-based secret sharing (AB-SS) scheme.With AB-SS, a dealer can distribute a secret based on attributes rather than specific individuals or shareholders. Only authorized users whose attributes satisfy a given access structure can recover the secret. Furthermore, we introduce the concept of attribute-based publicly verifiable secret sharing (AB-PVSS). An AB-PVSS scheme allows external users to verify the correctness of all broadcast messages from the dealer and shareholders, similar to a traditional PVSS scheme. Additionally, AB-SS (or AB-PVSS) distinguishes itself from traditional SS (or PVSS) by enabling a dealer to generate shares according to an arbitrary monotone access structure.To build an AB-PVSS scheme, we first implement a decentralized ciphertext-policy attribute-based encryption (CP-ABE) scheme, though not a fully-fledged one.We then incorporate non-interactive zero-knowledge (NIZK) proofs to enable public verification of the CP-ABE ciphertext. Based on the CP-ABE and NIZK proofs, we construct an AB-PVSS primitive.Finally, we conduct security analysis and comprehensive experiments on the proposed CP-ABE and AB-PVSS schemes. The results demonstrate that both schemes exhibit plausible performance compared to related works.</p
未来海平面变化对红树林空间分布的影响分析——以深圳市为例
No full text探明海平面变化对红树林分布的影响，对红树林保护与可持续利用具有重要意义。以深圳市沿海地区红树林为研究对象，结合边界预测模型与情景分析法，计算并分析了2020—2120年期间未来海平面上升情景下深圳湾和珠江口区域红树林的空间分布及其面积变化。结果表明：未来海平面变化对深圳红树林面积及群落结构均存在影响。在当前潮滩淤积速率情况下，未来全球海平面上升0.55 m和0.70 m情景下，深圳红树林面积分别增加29.3%和5.0%，各潮位的红树植物均可能向海迁移；未来全球海平面上升0.87 m和0.98 m情景下，深圳红树林面积分别减少21.3%和41.7%，红树林向陆地扩展受到堤岸阻碍，可能导致低中潮位红树植物分布面积减少，高潮带红树植物则可能减少甚至消失。Understanding the impact of sea level changes on the distribution of mangrove forests is of great significance for the protection and sustainable use of mangroves. The mangroves in coastal areas of Shenzhen were studied by combining the boundary prediction model and scenario analysis method to calculate and analyze the spatial distribution and area change of mangroves in Shenzhen bay and Pearl River estuary under the scenario of future sea level rise during 2020-2120. The results indicate that future sea level changes will have an impact on the area and community structure of mangrove forests in Shenzhen. Under the current sedimentation rate of tidal flats, the global sea level will rise by 0.55m and 0.70 m in the future, and the mangrove area in Shenzhen will be increased by 29.3% and 5.0% respectively. Mangrove plants at all tide levels may migrate to the sea; Under the scenarios of global sea level rise of 0.87 m and 0.98 m in the future, the area of mangrove forests in Shenzhen will be decreased by 21.3% and 41.7% respectively. The expansion of mangrove forests to land may be hindered by embankments, which may lead to a reduction in the distribution area of mangrove plants at low to medium tide levels, and a decrease or even disappearance of mangrove plants at high tide zones
Hi-CBM: Mitigating information leakage via hierarchical concept bottleneck modeling
No full textConcept Bottleneck Models (CBMs) enhance interpretability and facilitate effective intervention by explicitly mapping input features to labels through human-understandable concepts. However, existing CBM frameworks often suffer from information leakage, wherein latent unintended information bypasses the concept layer, undermining interpretability and contaminating downstream predictions. To address this challenge, we propose Hi-CBM, a refined CBM framework that explicitly safeguards the two inherent mappings in CBMs—features→concepts and concepts→classes—to prevent leakage. Specifically, a Concept-Bottleneck Pooling mechanism regulates the feature-to-concept mapping by selectively aggregating latent features into semantic concepts to filter out irrelevant signals, while a binary Intervention Matrix governs the concept-to-class mapping by constraining concept–class associations, preventing unintended information encoded in concept probabilities from influencing final predictions. Extensive experiments across multiple datasets show that Hi-CBM substantially mitigates information leakage and produces concept representations that are both interpretable and intervenable, while maintaining strong predictive performance.</p
A Nanobody Collection for the Identification of Client Binding Sites on the Sorting Adaptor Vps74p
No full text
