Learned indexes leverage machine learning models to approximate data distributions and predict key positions, offering better performance than traditional index structures such as B+Trees. As data in real-world applications evolve rapidly, the timely and efficient updating of learned indexes has become an increasingly important research problem, attracting growing attention in recent studies. However, under update-intensive workloads with frequent insertions and deletions, existing learned indexes cannot update the model in a timely manner. Moreover, they ignore the impact of deletions on model accuracy. These limitations lead to degraded prediction accuracy and increased query latency, undermining the core advantage of learned indexes. To fill in this gap, in this paper, we design a dynamic learned index (denoted as DynaMind) that is able to timely update the model with the frequent change of data. Specifically, we propose a novel score function to determine the appropriate timing at which a learned index should initiate an update by measuring the influence of updated data on the model accuracy. To enable efficient model updates, we devise a timely learned index update algorithm that implements both lightweight incremental learning for insertions and machine unlearning for deletions together, ensuring the model continuously evolves without full retraining. Extensive experiments on real-world and synthetic datasets show that DynaMind achieves competitive throughput compared to state-of-the-art works while improving the prediction accuracy. The proportion of keys with zero prediction error increases by more than 10% after updates.

A corpus of recent work has revealed that authenticated query services have been under the spotlight due to the untrustworthiness of outsourced service provider. To enrich scalable functionality, there is an increasing demand for dynamically authenticated query. However, when implementing query and update simultaneously, traditional approaches heavily suffer from the inconsistency between verification digest and requested index and therefore are infeasible in reality. Moreover, the efficiency of storage, query, verification, and update is still a huge hinder when processing large scale data. To address these challenging issues, in this paper, we propose a novel idea of authenticated learned index that is carefully designed and actively optimized for authenticated query processing. Specifically, we first propose a version control update mechanism for consistency guarantee by maintaining historical index versions. Following this, we propose two basic authenticated learned indexes, i.e., query-friendly PVL-tree and update-friendly PVLB-tree, to support efficient scalable authenticated range query. Furthermore, to improve the efficiency, we introduce a hybrid index framework HPVL-tree based on two basic indexes. Extensive theoretical and experimental analysis demonstrate that our proposed HPVL-tree outperforms the state-of-the-art approaches by up to$2.28\times, 3.96\times$, and$2.51\times$in search time, update time, and verification time, respectively. Moreover, the storage overhead and communication overhead occupy only 38 % and 2.25 % of existing approach, respectively.

Artificial Intelligence Generated Content (AIGC) has rapidly emerged with the capability to generate different forms of content, including text, images, videos, and other modalities, which can achieve a quality similar to content created by humans. As a result, AIGC is now widely applied across various domains such as digital marketing, education, and public health, and has shown promising results by enhancing content creation efficiency and improving information delivery. However, there are few studies that explore the latest progress and emerging challenges of AIGC across different domains. To bridge this gap, this paper brings together 16 scholars from multiple disciplines to provide a cross-domain perspective on the trends and challenges of AIGC. Specifically, the contributions of this paper are threefold: (1) It first provides a broader overview of AIGC, spanning the training techniques of Generative AI, detection methods, and both the spread and use of AI-generated content across digital platforms. (2) It then introduces the societal impacts of AIGC across diverse domains, along with a review of existing methods employed in these contexts. (3) Finally, it discusses the key technical challenges and presents research propositions to guide future work. Through these contributions, this vision paper seeks to offer readers a cross-domain perspective on AIGC, providing insights into its current research trends, ongoing challenges, and future directions.

Blockchain databases have been exploited in many applications to construct trust and share data among multiple participants. However, maintaining the entire blockchain locally will cause heavy communication and storage overhead for users with limited resources. Alternatively, the user could act as a light node that stores block headers only and delegates queries to full nodes that maintain the entire blockchain. However, introducing a light node raises several concerns about query integrity and privacy. In this paper, we propose EPPVChain, the first scheme that simultaneously achieves efficient, privacy-preserving and verifiable conjunctive query for blockchain databases. EPPVChain resorts to a novel symmetric cryptographic primitive named Symmetric Hidden Vector Encryption (SHVE), and deploys several new techniques to achieve the desired goals. In specific, we design a new SHVE-based authenticated data structure to support privacy-preserving verifiable conjunctive queries. We further propose two improved schemes to aggregate data records to optimize query performance. Finally, we propose a dual-chain key escrow protocol to securely escrow the symmetric key of SHVE without relying on any trusted third party. The security analysis and evaluation confirm EPPVChain’s ability to achieve query privacy and integrity with high efficiency.

Cloud computing is an essential technique to provide item traceability for industrial internet of things (IIoT) systems by providing item data sharing services. However, a malicious cloud server may prevent industrial participants from acquiring accurate traceability of items by providing inconsistent item data. To fix this issue, we propose Acics, an item data consistency auditing scheme in untrusted cloud services for cloud-aided IIoT systems. Acics presents two variants named S-Acics and L-Acics. S-Acics enables industrial participants to audit item data consistency for each item and circularly play the auditing role. L-Acics further enables industrial participants to audit item data consistency for a sampled subset of items while resisting data selection attack via a new separated storage mechanism. Finally, Acics integrates a fair payment mechanism built on smart contract to incentivize the cloud server to provide consistent item data access service for industrial participants. The experiment results show that our solution can audit item data consistency with reasonable cost.

Cloud service is an essential technique to provide product traceability for industrial systems by providing data integration and sharing services. However, a malicious or corrupted cloud service may prevent industrial participants from acquiring accurate and consistent traceability of products. To fix this issue, we propose Acics, a fast consistency auditing scheme for massive industrial data in untrusted cloud services. Our scheme enables industrial participants to circularly play the role of the auditor to audit data consistency of products in real-time. Additionally, we design a separated storage mechanism to improve the auditing efficiency for massive industrial data by utilizing and tailoring ORAM. The evaluation indicates that our solution audits data consistency with reasonable cost.