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.

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.

Influence Maximization(IM), which aims to select a set of users from a social network to maximize the expected number of influenced users, has recently received significant attention for mass communication and commercial marketing. Existing research efforts dedicated to the IM problem depend on a strong assumption: the selected seed users are willing to spread the information after receiving benefits from a company or organization. In reality, however, some seed users may be reluctant to spread the information or need to be paid higher to be motivated. Furthermore, the existing IM works pay little attention to capture users’ influence propagation in the future period. In this paper, we target a new research problem named,ReconnectingTop-$l$lRelationships(RT$l$R) query, which aims to find$l$number of previous existing relationships but being estranged later such that reconnecting these relationships will maximize the expected number of influenced users by the given group in a future period. We prove that the RT$l$R problem is NP-hard. An efficient greedy algorithm is proposed to answer the RT$l$R queries with the influence estimation technique and the well-chosen link prediction method to predict the near future network structure. We also design a pruning method to reduce unnecessary probing from candidate edges. Further, a carefully designed order-based algorithm is proposed to accelerate the RT$l$R queries. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness and efficiency of our proposed methods.

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.

Socially tenuous groups (or simply tenuous groups) in a social network/graph refer to subgraphs with few social interactions and weak relationships among members. However, existing studies on tenuous group queries do not consider the user profiles (keywords) of the members whereas in many social network applications, e.g., finding reviewers for paper selection and recommending seed users in social advertising, keywords also need to be considered. Thus, in this paper, we investigate the problem of keywords-based socially tenous group (KTG) queries. A KTG query is to find top N tenuous groups in which the members of each group jointly cover the most number of query keywords. To address the KTG problem, we first propose two exact algorithms, namely KTG-VKC and KTG-VKC-DEG, which give priority to the valid keyword coverage and the combination of valid keyword coverage and degree, respectively, to select members to form a feasible group by adopting a branch and bound (BB) strategy. Moreover, we propose keyword pruning and k-line filtering to accelerate the algorithms. To yield diversified KTG results, we also study the problem of diversified keywords-based socially tenous group (DKTG) queries. To deal with the DKTG problem, we propose a DKTG-Greedy algorithm by exploiting a greedy heuristic in combination with KTG-VKC-DEG. Furthermore, we design two alternative indexes, namely NL and NLRNL, to efficiently check whether the social distance of any two members is greater than the social constraint k in the above algorithms. We conduct extensive experiments using real datasets to validate our ideas and evaluate the proposed algorithms. Experimental results show that the NLRNL index achieves a better performance than the NL index.

With the boom in cloud computing, data outsourcing in location-based services is proliferating and has attracted increasing interest from research communities and commercial applications. Nevertheless, since the cloud server is probably both untrusted and malicious, concerns about data security and result integrity have become on the rise sharply. In addition, in the single-user situation assumed by most existing works, query users can capture query content from each other even though the queries are encrypted, which may incur the leakage of query privacy. Unfortunately, there exists little work that can commendably assure data security and result integrity in the multi-user setting. To this end, in this article, we study the problem of multi-user, secure, and verifiable$k$nearest neighbor query (MSV$k$kNN). To support MSV$k$NN, we first propose a novel unified structure, called verifiable and secure index (VSI). Based on this, we devise a series of secure protocols to facilitate query processing and develop a compact verification strategy. Given an MSV$k$NN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preservingdata privacy,query privacy,result privacy, andaccess patterns privacy; 2) authenticating thecorrectnessandcompletenessof the results; 3) supportingmulti-userwith different keys. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approach are empirically evaluated and demonstrated.

With the boom in cloud computing, data outsourcing in location-based services is proliferating and has attracted increasing interest from research communities and commercial applications. Nevertheless, since the cloud server is probably both untrusted and malicious, concerns of data security and result integrity have become on the rise sharply. However, there exist little work that can commendably assure the data security and result integrity using a unified way. In this paper, we study the problem of secure and verifiable k nearest neighbor query (SVkNN). To support SVkNN, we first propose a novel unified structure, called verifiable and secure index (VSI). Based on this, we devise a series of secure protocols to facilitate query processing and develop a compact verification strategy. Given an SVkNN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preserving the privacy of data, query, result and access patterns; 2) authenticating the correctness and completeness of the results without leaking the confidentiality. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approaches are empirically evaluated and demonstrated.

In recent years, spatial keyword query has attracted wide-spread research attention due to the popularity of the location-based services. To efficiently support the online spatial keyword query processing, the data owners need to outsource their data and the query processing service to cloud platforms. However, the outsourcing services may raise privacy leaking issues because the cloud server on the platforms may not be trusted for both data owners and query users. Therefore, in this work, we first propose and formalize the problem of privacy-preserving boolean spatial keyword query under the widely accepted Known Background Thread Model. And then, we devise a novel privacy-preserving spatial-textual Bloom Filter encoding structure and an encrypted R-tree index. They can maintain both spatial and text information together in a secure way while answering the encrypted spatial keyword queries without the need for data decryption. To further accelerate the query processing, a compressed encrypted index is provided to deal with the challenges of the large dimension expansion and the expensive space consumption in the encrypted R-tree index. In addition, we develop the corresponding algorithms based on the designed index, and present the in-depth security analysis to show our work's satisfaction meeting the strong secure scheme. Finally, we demonstrate the performance of our proposed index and algorithms by conducting extensive experiments on four datasets under various system settings.