With the rapid development of the low-altitude economy, skyline queries play a crucial role in identifying relevant data based on specific query requirements. To reduce storage overhead and improve query efficiency, data owners increasingly outsource their data to cloud servers. However, cloud servers may be untrusted and can potentially return incorrect or incomplete query results. Furthermore, most existing skyline query schemes do not support dynamic data updates and fail to satisfy the privacy and verifiability requirements essential for real-world low-altitude economic scenarios. In this paper, we propose a Secure Dynamic and Verifiable Skyline Query (SDVSQ) scheme, which supports dynamic and verifiable searchable encryption for skyline queries. We first devise a novel index structure, SDVR-tree, designed for efficient skyline query processing, where each data object is represented as a linked list in the leaf nodes. Each node in the linked list corresponds to a raw data object encrypted using a modified Paillier cryptosystem, ensuring that users accessing a list node cannot infer its sub-nodes. To support secure skyline computation, we design privacy-preserving protocols for squared Euclidean distance, comparison, and minimum operations. Additionally, SDVSQ ensures public verifiability of query result correctness and completeness by leveraging blockchain to store verification objects, while avoiding heavy on-chain computation. Formal security analysis shows that SDVSQ ensures forward privacy, data privacy, and query privacy while supporting result verification. Extensive experimental results demonstrate that SDVSQ significantly reduces computational overhead for both updates and skyline queries.

Crowdsourcing has emerged as a vital paradigm for task execution and data collection, with task matching as a core application. crowdsourcing platforms can leverage the spatial keyword similarity to identify whether a worker’s interests and location align with a task requester’s requirements. However, untrusted crowdsourcing platforms pose significant privacy risks to both task requesters and workers. To mitigate these risks, participants typically encrypt their data before outsourcing. In this paper, we propose a privacy-preserving Spatial Keyword Similarity-based Task Matching (SKSTM) scheme that enables secure task matching. In SKSTM, we encode both locations and keywords using Geohash and bitmap representations, respectively, transforming secure task matching into inner product operations in the ciphertext domain via Enhanced Asymmetric Scalar Product-Preserving Encryption (EASPE). Security analysis and experimental results demonstrate that SKSTM preserves participants’ privacy while outperforming state-of-the-art schemes in task matching efficiency.

Dynamic searchable symmetric encryption (DSSE) schemes support keyword search queries on encrypted dynamic datasets with add-or-delete operations stored on an untrusted remote server. Multi-user DSSE (MUDSSE) further considers multiple users to access the encrypted dataset. Most works of MUDSSE focus on how to promise forward and backward privacy of queries. However, the problem in which the data owner can not revocate the deleted encrypted data on the encrypted dataset adaptively and efficiently is not sufficiently considered. To solve this problem, we propose a new multi-user DSSE scheme named X-MUSE, extending cryptographic primitives Symmetric Revocable Encryption (SRE) to design a new searchable encryption with optimal search time for the deletion operation. Furthermore, to minimize communication size and counter new integrity threats raised by malicious clients, we extend X-MUSE to design a new MUDSSE named B-MUSE by integrating blockchain-based technology. Our evaluation confirms that our schemes have practical search performance and lower storage costs on both the server and the client side compared to the state-of-the-art.

The local deployment of federated large language models (FLLM) has further advanced the development of edge intelligence. However, the resource constraints of end devices, device heterogeneity, and the non-independent and identically distributed (Non-IID) nature of data pose significant challenges to the application of FLLM. To address this issue, we propose an Adaptive Asynchronous Accelerated FLLM (Tri-AFLLM) algorithm to achieve the efficient utilization of limited resources and improve model accuracy in the edge computing (EC) scenarios. Specifically, Tri-AFLLM first ships an off-the-shelf LLM, i.e., CLIP, to each end device, keeping the backbone parameters frozen and updating only the parameters of the adapter containing two linear transformation layers by using momentum gradient descent (MGD). Next, a toy example is provided to illustrate the necessity of using different numbers of local iterations for heterogeneous devices in resource-constrained environments. Subsequently, the convergence bound of the Tri-AFLLM under a given resource budget is discussed. Then, we formulated the bound into a resource consumption minimization problem with the number of local iterations as the optimization variable under a given model accuracy to mitigate the contribution disparity of local models to the global aggregation. Finally, extensive experiments are conducted to validate the superiority of Tri-AFLLM in terms of resource consumption, model accuracy, and addressing the Non-IID problem.

Spatial keyword query has emerged as a critical service in mobile cloud, enabling cloud servers to retrieve spatiotextual objects within a mobile user's query range that contain specified query keywords. Numerous secure spatial keyword query schemes have been developed to enable geometric range queries and keyword searches on encrypted spatial data. However, spatial keyword queries are typically designed for searching high-dimensional spatial data across arbitrary geographic ranges. Most of them fail to handle arbitrary geometric range queries and efficient spatial keyword query over high-dimensional encrypted data. To address these issues, we propose a high-dimEnsional and Privacy-preserving Spatial Keyword Query (EPSKQ) scheme with arbitrary geometric ranges over encrypted spatial data, leveraging Hilbert curve encoding and Enhanced Matrix-based Inner Product Encryption (EMIPE). In EPSKQ, spatial locations and multi-keywords are encoded into compact vectors, and arbitrary geometric range queries are transformed into range intersection tests. To reduce computational overhead, we employ vector bucketing technique to partition large-size vectors into several small-size sub-vectors. Furthermore, we design a novel index structure called Hilbert Binary tree (HB-tree) to optimize range intersection tests. Based on HB-tree, we propose an enhanced spatial keyword query scheme, named EPSKQ+, which further improves query performance. Security analysis demonstrates that both EPSKQ and EPSKQ+ achieve semantic security against indistinguishability under chosen-plaintext attack (INDCPA). Extensive experimental evaluations show that the proposed EPSKQ and EPSKQ+ schemes significantly outperform state-ofthe-art schemes in terms of computational and communication costs, with EPSKQ+ being 9× and 3× faster than the state-ofthe-art schemes in the index build and query phase, respectively

In spatial crowdsourcing, location-based task recommendation schemes are widely used to match appropriate workers in desired geographic areas with relevant tasks from data requesters. To ensure data confidentiality, various privacy-preserving location-based task recommendation schemes have been proposed, as cloud servers behave semi-honestly. However, existing schemes reveal access patterns, and the dimension of the geographic query increases significantly when additional information beyond locations is used to filter appropriate workers. To address the above challenges, this paper proposes two efficient and privacy-preserving location-based task recommendation (EPTR) schemes that support high-dimensional queries and access pattern privacy protection. First, we propose a basic EPTR scheme (EPTR-I) that utilizes randomizable matrix multiplication and public position intersection test (PPIT) to achieve linear search complexity and full access pattern privacy protection. Then, we explore the trade-off between efficiency and security and develop a tree-based EPTR scheme (EPTR-II) to achieve sub-linear search complexity. Security analysis demonstrates that both schemes protect the confidentiality of worker locations, requester queries, and query results and achieve different security properties on access pattern assurance. Extensive performance evaluation shows that both EPTR schemes are efficient in terms of computational cost, with EPTR-II being$10^{3}\times$faster than the state-of-the-art scheme in task recommendation.

With the proliferation of cloud computing, data owners can outsource the spatial data from the Internet of Things devices to a cloud server to enjoy the pay-as-you-go storage resources and location-based services. However, the outsourced services may raise privacy concerns, since the cloud server may not be fully trusted for both data owners and search users. If the data owners and search users conventionally encrypt the spatial data and query requests, the efficiency and functionality of query processing are weakened. Most of the existing works only focus on spatial data search or keyword search and do not consider spatial keyword search over encrypted data. In this article, we first design a geometric range query (GRQ) scheme, which can generate an arbitrary geometric range to fit the search user’s desired spatial data while protecting location privacy. Furthermore, based on GRQ, we propose a multidimensional spatial keyword similarity search scheme with access control (MSSAC) by integrating the polynomial function and matrix transformation. Specifically, an access control strategy is defined by a role-based polynomial function, which is embedded in the vectors of indices and trapdoors to achieve efficient and lightweight access control. Moreover, MSSAC enables the cloud server to execute compute-then-compare operations for spatial keyword search in a privacy-preserving manner by leveraging techniques of randomizable permutation and matrix multiplication. The formal security analyses and extensive experiments demonstrate that GRQ and MSSAC preserve the privacy of data owners and search users while achieving efficient spatial keyword search.

Multi-keyword search, aiming to search the objects by a query request that consists of multiple keywords, has wide applications in personalized recommendation services. Mean-while, the fast development of cloud technology has given rise to a new trend that data are encrypted before being outsourced to a public cloud for users to enjoy pay-as-you-go services. However, most of the existing works primarily focus on the single keyword search, and consider a general scenario with a single owner and a single user. In this paper, we propose an efficient and Privacy-preserving Multi-user Multi-keyword Search (PMMS) scheme, which can support user scalability without key sharing. In particular, based on the matrix decomposition, a key derivation approach is integrated into our PMMS to generate secret keys and re-encryption keys. Furthermore, by employing threshold predicate encryption and leveraging the techniques of matrix transformation and proxy re-encryption, PMMS guarantees that only the comparison result of an inner product of two vectors and a pre-defined threshold is revealed, and enables the cloud server to perform multi-keyword search in an efficient and privacy-preserving manner. Security analysis shows that the confidentiality of owners’ data and users’ queries can be guaranteed. Extensive experiments on a real-world dataset demonstrate that PMMS is efficient in terms of multi-keyword search.

Data sharing is one of the key technologies, which provides the practice of making data collected from a crowd of mobile devices available to others using a cloud infrastructure, known as mobile crowdsensing (MCS). However, the collected data may contain sensitive information, and sharing them in public clouds without proper protection could cause serious security problems, such as privacy leakage, unauthorized access, and secret key abuse. To address the above issues, in this paper, we propose a Traceable and privacy-preserving non-Interactive Data Sharing (TIDS) scheme in mobile crowdsensing. Specifically, to achieve privacy-preserving fine-grained data sharing, an attribute-based access policy is generated by a data owner without interacting with data users in the TIDS. Furthermore, we design a ciphertext conversion mechanism to support flexible data sharing. Also, by utilizing traceable Ciphertext-Policy Attribute-Based Encryption (CP-ABE), TIDS supports a trusted authority to trace malicious users who abuse their secret keys without incurring additional computational overhead. Security analysis demonstrates that TIDS can protect the confidentiality of the outsourced data. Experimental results show that TIDS can achieve efficient data sharing in mobile crowdsensing applications.

With the proliferation of cloud services, cloud-based image retrieval services enable large-scale image outsourcing and ubiquitous image searching. While enjoying the benefits of the cloud-based image retrieval services, critical privacy concerns may arise in such services since they may contain sensitive personal information. In this paper, we propose an efficient and Privacy-Preserving Image Retrieval scheme with Key Switching Technique (PPIRS). PPIRS utilizes the inner product encryption for measuring Euclidean distances between image feature vectors and query vectors in a privacy-preserving manner. Due to the high dimension of the image feature vectors and the large scale of the image databases, traditional secure Euclidean distance comparison methods provide insufficient search efficiency. To prune the search space of image retrieval, PPIRS tailors key switching technique (KST) for reducing the dimension of the encrypted image feature vectors and further achieves low communication overhead. Meanwhile, by introducing locality sensitive hashing (LSH), PPIRS builds efficient searchable indexes for image retrieval by organizing similar images into a bucket. Security analysis shows that the privacy of both outsourced images and queries are guaranteed. Extensive experiments on a real-world dataset demonstrate that PPIRS achieves efficient image retrieval in terms of computational cost.

Cloud computing has become an important and popular infrastructure for data storage and sharing. Typically, data owners outsource their massive data to a public cloud that will provide search services to authorized data users. With privacy concerns, the valuable outsourced data cannot be exposed directly, and should be encrypted before outsourcing to the public cloud. In this paper, we focus on k-Nearest Neighbor (k-NN) search over encrypted data. We propose efficient and secure k-NN search schemes based on matrix similarity to achieve efficient and secure query services in public cloud. In our basic scheme, we construct the traces of two diagonal multiplication matrices to denote the Euclidean distance of two data points, and perform secure k-NN search by comparing traces of corresponding similar matrices. In our enhanced scheme, we strengthen the security property by decomposing matrices based on our basic scheme. Security analysis shows that our schemes protect the data privacy and query privacy under attacking with different levels of background knowledge. Experimental evaluations show that both schemes are efficient in terms of computation complexity as well as computational cost.