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.

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.

Directed fuzzing is an effective software testing method that guides the fuzzing campaign towards user-defined target sites of interest, enabling the discovery of vulnerabilities relevant to those sites. However, even though the generated test cases cover the code near the target sites, complex vulnerabilities remain untriggered. By focusing only on test cases that cover new edges, the program states related to the targets are overlooked, resulting in insufficient testing of the targets and failure to capture complex vulnerabilities. In this paper, we propose a novel directed fuzzing solution named CSFuzz, which considers program states associated with the targets. First, CSFuzz extracts critical variables related to the target sites from the program using static analysis. Then, CSFuzz monitors the runtime values of these critical variables and infers the program states associated with the targets by adaptively partitioning the range of variable values. This allows CSFuzz to store interesting seeds in the state corpus that trigger new states near the target sites. Lastly, CSFuzz employs dynamic scheduling techniques to guide the fuzzing campaign in selecting different corpora and prioritizing seeds. This ensures more adequate testing of the target sites. We have implemented a prototype of CSFuzz and evaluated it on 2 benchmarks and widely fuzzed real-world software. Evaluation results show that CSFuzz outperforms state-of-the-art fuzzers in terms of vulnerability detection capability, achieving a maximum speedup of 219%. Moreover, CSFuzz has discovered 4 new bugs, including 2 CVE IDs assigned.

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.

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.

Bridging the gap between visual and textual modalities effectively has consistently been a key challenge in cross-modal retrieval. Fine-grained matching approaches improve performance by precisely aligning salient region features in visual modality with word embeddings in textual modality. However, how to effectively and efficiently filter out irrelevant features (e.g., irrelevant background regions and nonmeaningful prepositions) in multimodality remains a significant challenge. Furthermore, capturing key cross-modal relationships while minimizing misalignment interference is crucial for effective cross-modal retrieval. In this work, we propose a novel approach called the selective filter and alignment network (SFAN) to tackle these challenges. First, we propose modality-specific selective filter modules (SFMs) to selectively and implicitly filter out redundant information within each modality. We then propose the state-space models (SSMs)-based selective alignment module (SAM) to selectively capture key correspondences and reduce the disturbance of irrelevant associations. Finally, we utilize a fusion operation to combine these embeddings from both SFM and SAM to derive the final embeddings for similarity computation. Extensive experiments on the Flickr30k, MS-COCO, and MSR-VTT datasets reveal that our proposed SFAN can effectively learn robust patterns, significantly outperforming the state-of-the-art (SOTA) cross-modal retrieval methods by a wide margin.

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.

Abstract The key to deep learning vulnerability detection framework is pre-processing source code and learning vulnerability features. Traditional source code representation techniques take a complete normalization to user-defined symbols but ignore the semantic information associated with vulnerabilities. The current mainstream vulnerability feature learning model is Recurrent Neural Network (RNN), whose time-series structure determines its insufficient remote information acquisition capability. This paper proposes a new vulnerability detection framework to solve the above problems. We propose a new data normalization method in the source code pre-processing phase. The user-defined symbols are clustered using the unsupervised clustering algorithm K-means. The normalized classification is performed according to the clustering results, which preserves the primary semantic information in the source code and ensures the smoothness of the sample data. In the feature extraction stage, we input the source code after performing text representation into Bidirectional Encoder Representations for Transformers (BERT) for feature automation learning, which enhances semantic information extraction and remote information acquisition. Experimental results show that the vulnerability detection precision of this method is 18.3% higher than that of the current mainstream vulnerability detection framework in the real-world data collected by ourselves. Further, our method improves the precision of the state-of-the-art method by 4.2%.

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.

Due to the ubiquity of computer software, software vulnerability detection(SVD) problem is essential to protect cyber system from attacks. Recently, deep learning-based vulnerability detection has achieved outstanding performance, relieving experts from tedious task of manually defining vulnerability features as well. However, its detection capability is compromised when facing with the scarcity of labeled data. One possible solution is to leverage training data with adequate labels from other domains, but the data distributions in different domains differ significantly. On the other hand, function level detection is too coarse-grained and not able to capture inter-procedure vulnerability patterns. In this paper, we propose a systematic Snippet-Oriented Cross-Domain Vulnerability Detection Framework with Domain Adaptation, which is the first time to detect cross-project vulnerabilities at a finer granularity than function. Firstly, we generate Code Snippets from 5 real-world projects and 3 types of CWE in NVD and SARD for cross-project and cross-type detection; Secondly, we propose an novel and effective approach to obtain deep features for domain adaptation; Finally, we employ the domain adaptation algorithm on these deep features to reduce the divergence between different domains and get the final result. Experimental results show that our framework outperforms other state-of-the-art approaches.

Deep learning-based vulnerability detection methods have become one of the mainstream methods of vulnerability detection. The vulnerability type information is of great value in helping vulnerability location and vulnerability remediation. This paper proposes a framework for Vulnerability Type Identification based on Code Property Graph with Fusion Representation. First, this paper uses code property graph information. Code property graph(CPG) is a joint data structure that combines Abstract Syntax Trees(AST), Control Flow Graphs (CFG), and Program Dependency Graphs (PDG). We encode CPG information. Secondly, we use Convolutional neural network combined with Recurrent Neural Network(CNN-RNN) and Attention-Based Bidirectional Gate Recurrent Unit (Att-BiGRU) to extract AST and CFG combined with PDG information. We fuse the extracted features to obtain an effective representation. And then, we perform multi-classification to derive the predicted value of the vulnerability type. Finally, we use 59 vulnerabilities with third-level CWE-ID for evaluation. The experiments show that this paper’s code property graph information can better represent the type information of vulnerabilities. Compared with the classical RNNs, our model in this paper has a more accurate identification effect.

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.

The proliferation of code reuse and the prevalence of CPU architecture and compilation environment diversity inevitably lead to many similar cross-platform binary vulnerability codes. This paper designs a deep learning model based on slice semantic and node order to detect similarity vulnerabilities. Firstly, it traverses the program dependence graph (PDG) forward and backward from the library/API function node to generate the binary slice and then uses the bidirectional long short-term memory (BLSTM) network and attention mechanism to form the semantic feature vector of the binary slice. Secondly, it extracts the order information of the slice nodes in the PDG and forms the adjacency matrix, which is then fed into the convolutional neural network (CNN) to form the order feature vector. Finally, the semantic and order feature vector are fused and inputted into the siamese network for similarity vulnerability detection. The detection results show that our method can effectively detect vulnerability.

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.

Vulnerability detection is an essential means to ensure the normal operation of various software tools and system security. The Recurrent Neural Networks (RNNs) have achieved remarkable results in vulnerability detection, but the sequence-based code representation has great limitations in feature expression and propagation. In this paper, we propose a fine-grained code vulnerability detection framework based on Gated Graph Neural Network (GGNN). Firstly, we process the source code into fine-grained slices. Secondly, graph embedding of code slices is constructed by clustering neighborhood information. Finally, GGNN is used to learn the syntax and semantic information of vulnerability codes for graph-level classification. Furthermore, we theoretically analyze that GGNN has a strong inductive learning ability. This means that the model requires only a small amount of training data to obtain sufficient advanced features, which is significant for vulnerability detection tasks that are difficult to collect data sets. We carry out conventional experiments and inductive experiments with manually collected data sets, and the results show that the framework is superior to RNNs in vulnerability detection performance. Moreover, our framework performs better than RNNs under inductive conditions.

In space, the impact of radiative particles, such as neutrons and heavy ions, can change the node states of a flip-flop, thus resulting in loss of data. In this paper, a Highly reliable and Low power Radiation-hardened-by-design (RHBD) Flip-Flop cell, namely HLRFF, completely hardened against double-node-upsets (DNUs), is proposed for aerospace applications. The HLRFF is a master-slave structure. The master latch is mainly constructed from two 2-input C-elements (CEs) and one 2-input clock-gating based CE, while the slave latch has an additional keeper at the output stage. The verification results demonstrate that the proposed HLRFF is completely DNU-tolerant. Furthermore, compared to the state-of-the-art radiation-hardened FF cells, the proposed HLRFF can reduce power consumption by approximately 69%. However, only the proposed HLRFF is not only completely DNU-tolerant but also insensitive to high-impedance-state.

Spatial keyword query has attracted wide-spread academic and industrial concerns due to the popularity of location-based services and Internet of Things. To efficiently support the online query processing, the data owners need to outsource their data to cloud platforms. However, the outsourcing services may raise privacy leaking issues. Moreover, access control, another important security concern, is largely ignored. Therefore, we first propose and formalize the problem of secure boolean spatial keyword query under lightweight access control while guaranteeing the widely acceptedadaptive indistinguishability against chosen keyword attackmodel. Then, we devise a novel hybrid Bloom filter encoding strategy, including a linear embedding and exponent-based transformation schemes and a secure index structure, calledSAGTree. They can maintain both geo-text and access policy information together in a secure way while answering the encrypted queries under access control without decryption. Finally, we present the in-depth security analysis and demonstrate the performance of our proposed algorithms.

With the rapid prosperity of wireless communication, a new era of smart cities has arisen. During the implementation of smart cities, electricity supply plays a profound role and needs to be guaranteed. To do this, a health model with terminal sensors data is usually adopted to enable the security of the transmission line. However, due to the extreme weather condition and the interference of sensors, some transmitted values are missing, which renders the sensors unreliable. Unfortunately, the existing prediction methods have a lower accuracy and there still exists a gap between the real value and the predicted value. In this article, we propose a novel data fusion model, named as KNN-XGBoost, to predict the missing values. Specifically, in this model, it mainly consists of two parts: 1) KNN stage: a KNN model is used to find${K}$nearest labels as new attributes and 2) XGBoost: an XGBoost model is applied to predict missing attribute values. The experimental results have demonstrated that our proposed model can improve the accuracy from 1.11% to 4.05%, 0.55% to 1.84%, and 1.10% to 4.04% than the other methods under the three grid health models, respectively.

With the rapid advancement of design and manufacturing technologies of nano-scale CMOS circuits, latches are becoming increasingly sensitive to multiple-node-upsets caused by harsh radiation effects. In this paper, a Parallel Dual-interlocked-storage-cells (DICEs) and Dual-level C-elements (CEs) based 3-node-upset (3NU)-Tolerant Latch, namely PDDCTL, design for highly robust computing, is proposed. The latch comprises five transmission gates, two DICEs and three CEs. Due to the use of two single-node-upset self-recoverable DICEs and three error-interceptive CEs, the latch can provide complete 3NU-tolerance with low cost. Simulation results not only confirm the 3NU-tolerance of the proposed latch but also demonstrate that the delay-power-area product of the PDDCTL latch is reduced by 68.82% on average compared with the state-of-the-art 3NU hardened latch designs.

The authenticity and integrity of sensed data in the data collection stage is a very critical aspect for the smart city industrial environment. They impact the accuracy of data analysis and the objectivity of making decisions. However, how to identify attack behaviors from environmental interference and establish a secure route to transmit data for resource-constrained terminals are challenging problems. To address these problems, in this article, we propose a trust-based security system (TSS). In TSS, we first design a trust model using binomial distribution for calculating the node's trust value and a third-party recommendation scheme for improving the objectivity of trust value. Then, we propose a trust management scheme for preventing theon–offattack. After that, we design a secure routing protocol, which is used to balance the security, transmission performance, and energy efficiency. Finally, the analytical results of the TSS are evaluated with extensive simulation experiment.

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.