The Approximate Nearest Neighbor (ANN) search is a cornerstone in high-dimensional data processing, which, however, remains challenging in outsourced scenarios due to potential result and data tampering. Recent studies on verifiable search have partially offered verification guarantees, while they suffer from limited scalability or fail to address result integrity simultaneously. In this paper, we propose VLAH, a lightweight and verifiable framework for ANN search. VLAH integrates locality-sensitive hashing with a compressed trie structure for efficient bucket verification and employs the in-bucket HNSW index to accelerate candidate retrieval. To ensure result integrity, we design a hybrid verification object that combines Merkle-based prefix proofs, neighbor-bucket consistency, and graph search trace validation. By integrating blockchain technology, we further enhance the integrity of the scheme. We theoretically analyze the complexity of our scheme and the correctness and integrity guarantees of the query results in lightweight verification. Experimental results on real high-dimensional datasets show that VLAH achieves competitive search performance with significantly reduced verification costs, bridging the gap between efficiency and verifiability in large-scale ANN systems.

With the advances in positioning techniques, trajectories are emerged with semantic information such as location-based activities and sign-ins. Essential for applications such as trip recommendations, range queries on semantic trajectories are utilized to identify trajectories that satisfy specific spatio-temporal conditions as well as keyword-related criteria. To reduce the costs of query processing services, data owners often outsource the data services to public platforms, of which the fog-based cloud can offer enhanced storage and computing capabilities and support efficient access to user data. However, outsourcing data in plaintext may raise privacy concerns. To this end, we study the problem of secure range queries on semantic trajectories on the fog-based cloud platform. Initially, to effectively organize semantic trajectories, we design a secure index structure within fog-based cloud framework by distributing spatio-temporal data of trajectories across the fog servers based on semantic contents. Subsequently, we propose a secure range query scheme where keyword pruning prioritizes the fog servers containing query keywords, and then secure spatio-temporal pruning is performed in parallel to obtain the candidate trajectories satisfying spatio-temporal constraints. Finally, on cloud servers, secure timespan verification is applied to ensure that the final results include all query keywords within the specified time span. A comprehensive analysis of the proposed scheme is provided in terms of computational complexity and security guarantees. Leveraging the keyword-first-pruning strategy and parallel processing on fog servers, the query scheme is evaluated to show stable and efficient performance through extensive experiments on real datasets.

Abstract Social question‐and‐answer (Q&A) communities are becoming increasingly important for knowledge acquisition. However, some users withhold knowledge, which can hinder the effectiveness of these platforms. Based on social exchange theory, the study investigates how different types of questions influence knowledge withholding, with question difficulty and user anonymity as boundary conditions. Two experiments were conducted to test hypotheses. Results indicate that informational questions are more likely to lead to knowledge withholding than conversational ones, as they elicit more fear of negative evaluation and fear of exploitation. The study also examines the interplay of question difficulty and user anonymity with question type. Overall, this study significantly extends the existing literature on counterproductive knowledge behavior by exploring the antecedents of knowledge withholding in social Q&A communities.

Yuhao Feng, Yanghui Rao, Yuyao Tang, Ninghua Wang, He Liu. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.