Hybrid-storage blockchains have become a promising paradigm for scaling up query processing services. This paradigm provides a hybrid on/off-chain approach, where only small metadata is stored on-chain while the raw data is outsourced to off-chain storage. The key concerns for query processing in such a system are security and query integrity. However, existing schemes mostly address an impractical honest-but-curious model, rather than a more realistic malicious model, due to the intricate interplay between security and query integrity. Additionally, supporting efficient dynamic update with forward security is a significant challenge for blockchain systems. To address these issues, in this paper, we first formally define the problem of secure, reliably verifiable, and dynamic Boolean range queries in hybrid-storage blockchains (SRVF). Then, we propose a novel index called Virtual Keyword Forest (VKF), which achieves secure and reliably verifiable query processing in hybrid-storage blockchains. To support dynamic queries with forward security, we propose an adaptive version-control update scheme to integrate into VKF. Furthermore, to reduce communication overhead and gas consumption, we design an aggregation signature algorithm that ensures the verification information is of a fixed size. We provide theoretical security analysis and empirical evaluations, comparing our proposed approaches with the state-of-the-art approaches, to demonstrate their feasibility.

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.

The widespread availability of similarity queries over trajectory data has led to numerous real-world applications, such as traffic management and path planning. With the proliferation of trajectory data, data owners often outsource storage and computation tasks to the cloud due to limited computing and storage resources. However, this scenario raises sharp security concerns, where it is critical to ensure both the integrity of query results and privacy during query processing. Furthermore, most existing works assume a single-user setting where all query users share the same key, which may lead to query privacy leakage. Therefore, in this article, we take the first step in studying the issue of multiuser and secure trajectory similarity range query (MSRQ). Specifically, inspired by the M-tree, we propose a secure index based on a distributed two-trapdoor public-key cryptosystem (DT-PKC), called M*-tree, and devise secure protocols to support multiuser query processing. We also carefully design a filtering strategy and verification scheme to ensure fast search and integrity guarantees. Finally, we theoretically analyze the security and complexity and empirically evaluate the performance and feasibility of our proposed approach.

Dynamic searchable symmetric encryption (DSSE) ensures that outsourced data can be searched and updated without compromising data availability. Recent efforts on DSSE have mainly focused on exact keyword retrieval, but considering that misspellings are common and practical, it is necessary to support the functionality of fuzzy keyword search. However, most existing fuzzy keyword search schemes do not consider malicious cloud servers and forward-privacy guarantees. The former may lead to the implementation of a fraction of search operations or forge the results and the latter may reveal the association between the newly updated data and previous search tokens. Therefore, in this article, we investigate the issue of dynamic and verifiable fuzzy keyword search with forward security (DVFKF). Specifically, DVFKF first uses locality sensitive hash to map approximate keywords into the same hash bucket, then links the hash buckets to obtain the bucket strings. Next, to accelerate the performance and support forward privacy, we propose a chain index for each bucket string through counters. Further, to guarantee the integrity of the results, we integrate the Merkle hash tree and chain index to verify the correctness and completeness of the results. Finally, we conduct formal security analysis and empirical evaluations to demonstrate the feasibility and practicality of our scheme on real datasets.

Blockchain has emerged as a prevailing paradigm for decentralized applications due to its reliability and transparency. To scale up retrieval services, a common strategy is to use a hybrid storage model, where on-chain storage is responsible for small metadata and off-chain storage is for outsourced raw data. However, data security and result authenticity are ongoing challenges in this scenario, and little work has been done due to the difficulty of combining result verification and privacy preservation, especially for dynamic updates while supporting forward privacy. In this paper, we formally define the problem of efficient, verifiable, and secure conjunctive keyword search in hybrid-storage blockchains (vsChain) and propose a novel hybrid index that achieves efficient query and verification while supporting dynamic updates with forward privacy guarantee. Finally, we provide empirical evaluations using real and synthetic datasets to demonstrate the feasibility of our proposed scheme.

Blockchain has emerged as a prevailing paradigm for decentralized applications due to its reliability and transparency. To scale up retrieval services, a common strategy is to use a hybrid storage model, where on-chain storage is responsible for small metadata and off-chain storage is for outsourced raw data. However, data security and result authenticity are ongoing challenges in this scenario, and little work has been done due to the difficulty of combining result verification and privacy preservation, especially for dynamic updates while supporting forward privacy. In this paper, we formally define the problem of efficient, verifiable, and secure conjunctive keyword search in hybrid-storage blockchains (vsChain) and propose a novel hybrid index that achieves efficient query and verification while supporting dynamic updates with forward privacy guarantee. We also design two optimized schemes to improve query and verification performance by using a partition-based method and an obfuscated counting Bloom filter mechanism. Finally, we provide a theoretical security analysis and empirical evaluations using real and synthetic datasets to demonstrate the feasibility of our proposed schemes.

Data outsourcing to the cloud has become increasingly popular for high-speed storage and retrieval. However, privacy and security are pressing concerns that hinder the further development of cloud computing. A common approach is to encrypt data before outsourcing, assuming the cloud is semi-honest. However, in reality, the cloud may be malicious and forge query results unexpectedly. Moreover, most previous schemes are designed for single-user setting, where different users share the same secret key, leading to potential privacy leaks. Therefore, ensuring confidentiality and verifiability in multi-user setting is crucial but has not been well-addressed. In this paper, we formally define the notion of Verifiable and Secure Range Query in Multi-User Setting (VSRQM) and propose a prefix-aware encoding (Pcode) scheme to encode spatial data for query processing. Next, we design a Tree-Aided Verifiable and Secure Index (SATree) on top of the Pcode and symmetric re-encryption scheme. SATree preserves data privacy, provides a mechanism to verify query results’ integrity and achieves sub-linear search time. Additionally, we propose two compression schemes to reduce the space cost of storage and transmission. Finally, we present formal complexity and security analyses and conduct empirical evaluations on real and synthetic datasets to demonstrate our proposed approaches’ practical performance.