In resource-constrained environments such as IoT sensors and mobile devices, there is a strong demand for efficient conjunctive keyword search over privacy-sensitive data. However, existing schemes struggle to simultaneously suppress sterm equality leakage, the cross-query intersection pattern (IP), and the volume pattern without incurring prohibitive overhead. In this paper, we present XORCMM, a practical volume-hiding encrypted conjunctive multi-map (EMM) designed for robust leakage suppression. First, we shift the index construction from single keywords to global-ordering co-occurrence pairs, which ensures that search tokens are no longer tied to static keyword identities, thereby suppressing sterm equality leakage. Second, we integrate an incremental multiset hash aggregation mechanism directly into a fully padded Xor filter. This allows the server to aggregate multiple conjunctive results into a single, fixed length response, concealing both IP and volume patterns while eliminating the data redundancy of prior schemes. Third, we employ a prefix-constrained PRF to compactly encode keyword pairs, generating succinct query tokens whose size is independent of keyword volumes. Formal security analysis proves that XORCMM is adaptively secure with sterm equality, IP, and volume leakages hidden. Experimental results demonstrate that XORCMM achieves up to a 2.99× speedup in client setup, a 3.3× speedup in server query time, and reductions of 47% in response size and 84.61% in search token size, providing a stronger security guarantee with significantly higher efficiency.

The increase in the amount of data stored in the cloud leads to the need for privacy-preserving multi-keyword search schemes in the cloud. However, most of the existing schemes usually adopt the TF-IDF vector space model, in which the vectors are high-dimensional and sparse. It results in substantial computation time and storage space. To address the issue, we propose an efficient dictionary partition-based multi-keyword ranked search scheme (DPMRS) over encrypted cloud data. First, a dictionary partition-based vector space model (DPVSM) is designed, which can compress vector dimensions and hence accelerate relevance score computation between documents and search keywords. Based on DPVSM, a dictionary partition-based keyword distribution inverted index (DPKD-index) is presented. By using the index, a baseline privacy-preserving ranked search scheme is proposed. To further improve the efficiency of search services, the search tree structure is adopted and a novel double tier search tree-based index (DSTree-index) is designed. By using the optimized index, an enhanced search scheme (DPMRS+) is proposed. The security analysis indicates that the proposed scheme can protect the privacy of search processing, and the experimental results show that the proposed scheme outperforms the existing works in terms of storage size, search precision, and search time cost.

Federated learning (FL) is a key paradigm for deploying AI models across large numbers of Internet of Things (IoT) devices in edge computing. While FL avoids uploading raw data to a central server, client privacy remains vulnerable during new client integration, when previously unseen devices first register their identities and cryptographic keys. A malicious or semi-honest central server (CS) can manipulate training to isolate target gradients, reconstruct local data, and tamper with aggregation. We study the Identity Forgery and Gradient Inversion Attack (IFGIA) against federated edge learning. By fabricating virtual clients and exploiting secure aggregation, a malicious CS can recover target gradients with success rates above 99.5% under realistic edge settings, revealing a critical weakness in existing privacy-preserving and verifiable FL schemes. To defend against IFGIA, we propose Robust Federated Learning (RFL), a framework tailored for edge computing that combines model splitting between edge clients and edge servers, lightweight differential privacy on intermediate representations, and split verification using digital signatures and homomorphic hashes. Experiments show that RFL reduces IFGIA's success rate to 73.6%, shrinks per-client communication from 375 MB to 40 KB, accelerates edge-side training by at least 10×, and maintains competitive accuracy.

The proliferation of data-driven services on cloud platforms, coupled with stringent regulatory frameworks like the General Data Protection Regulation (GDPR), necessitates crypto-graphic solutions that ensure secure and efficient data exchange. However, simultaneously achieving post-quantum security, bilateral access control, data authenticity, simplified key management and efficiency remains a critical challenge. To address these issues, this paper introduces a certificateless lattice-based matchmaking encryption (CLLME) to provide post-quantum security while obviating key escrow and certificate management. The proposed scheme enforces bilateral access control, allowing both data senders and receivers to specify matching access structures; decryption is thus contingent upon mutual authorization. Moreover, to prevent the costly decryption of numerous irrelevant ciphertexts, CLLME embeds a lightweight authenticity operation, which enables retrieval of useful data, effectively creating a high-performance filter for encrypted data streams. We formally prove that CLLME achieves indistinguishability against chosen-plaintext attacks and existential unforgeability against chosen-message attacks under lattice-based assumptions. Experimental evaluations demonstrate that CLLME maintains favorable communicational and computational efficiency, confirming its suitability for practical and scalable deployment in regulation-compliant, large-scale data sharing environments.

We present GC-DP, a novel federated learning framework that enables personalized differential privacy by adaptively adjusting gradient clipping thresholds. Unlike traditional DP methods that apply a fixed clipping bound, GC-DP uses a proxy dataset to learn client-specific mappings from privacy budgets (ε) to optimal clipping thresholds (C^*), while also allowing each client to adjust its local clipping bound in real time based on the l2 norm of its local gradient. This dual adaptivity significantly improves the balance between privacy protection and model utility in heterogeneous FL settings.

In the era of big data, the demand for spatial dataset search has become increasingly urgent. Leveraging the powerful storage and computing capabilities of cloud platforms, the cloud has become a common choice for deploying dataset search services. However, under risks of untrusted cloud environment and malicious attacks, protecting the privacy of sensitive location information during spatial dataset search becomes particularly critical. This paper focuses on the problem of privacy-preserving spatial datasets joinable search in cloud, which has not been addressed in existing research. We first propose a grid-based joinable coverage distinction model to measure the joinability of spatial datasets, and further present a baseline scheme (PDJDS). To further enhance efficiency and reduce storage cost, we propose an optimized scheme (PDJDS+), which constructs a coarse-grained grid-based inverted index to filter candidate datasets and integrates a joinable coverage distinction check table to expedite the evaluation of spatial dataset coverage distinction. Experiments conducted on three real-world spatial data repositories demonstrate that our scheme achieves superior performance in terms of search accuracy, efficiency, and storage cost.

The outbreak of viruses and continuous advancements in positioning systems have prompted researchers to propose a novel spatiotemporal query method known as the contact tracing query. Existing algorithms have already addressed both single-source and multi-source problems, but their judgment conditions are overly strict and thus not well adapted to realworld scenarios. This paper investigates the multi-source loose contact event query problem, a more flexible and practical problem. We first define the concept of multi-source loose contact events and propose a baseline query processing framework based on the idea of sliding window. To improve the query efficiency, we design an InGrid-MBR-based inverted index employed in the optimized processing(MLCEQ+). Comprehensive experiments on real datasets show that proposed query processing can effectively identify more potential contact events than existing algorithm, and MLCEQ+ can improve efficiency while guaranteeing accuracy.

Multi-view clustering aims to enhance clustering performance by leveraging information from diverse sources. However, its practical application is often hindered by a barrier: the lack of correspondences across views. This paper focuses on the understudied problem of fully incomplete multi-view clustering (FIMC), a scenario where existing methods fail due to their reliance on partial alignment. To address this problem, we introduce the Contrastive Prototype Matching Network (CPMN), a novel framework that establishes a new paradigm for cross-view alignment based on matching high-level categorical structures. Instead of aligning individual instances, CPMN performs a more robust cluster prototype alignment. CPMN first employs a correspondence-free graph contrastive learning approach, leveraging mutual $k$-nearest neighbors (MNN) to uncover intrinsic data structures and establish initial prototypes from entirely unpaired views. Building on the prototypes, we introduce a cross-view prototype graph matching stage to resolve category misalignment and forge a unified clustering structure. Finally, guided by this alignment, we devise a prototype-aware contrastive learning mechanism to promote semantic consistency, replacing the reliance on the initial MNN-based structural similarity. Extensive experiments on benchmark datasets demonstrate that our method significantly outperforms various baselines and ablation variants, validating its effectiveness.

As the number of open spatial datasets continues to grow, there is a corresponding increase in demand for the ability to efficiently identify spatial datasets that align with users’ specific requirements. It has become a significant issue, resulting in the need for a variety of spatial dataset search requirements, including the need for range-based spatial dataset search. In this paper, we propose an efficient range-based top-k spatial dataset search processing for spatial information retrieval based on the quadtree-based region-dataset inverted index (QRDI-index). A relevance measurement between a spatial dataset and a search range is presented first, which is used to rank candidate results. To support efficient search processing, the QRDI-index is designed, which combines the inverted index, quadtree, and spatial datasets. Using the index, we propose an efficient search processing algorithm that filters the minimum tree nodes in the QRDI-index, and the search space is narrowed to these nodes. Experimental results on three real-world spatial data repositories validate the accuracy and efficiency of the proposed search scheme.

This research is dedicated to addressing user recommendation matching and multi-objective optimization problems in ride-sharing services. For addressing the challenge of node classification in social networks, the Graph Attention Network with Opinion Dynamics (OD-GAT) is proposed. This model combines opinion dynamics and attention mechanism, which can make full use of multi-dimensional information for social relationship reasoning, and at the same time simulate the influence of individuals by other objects in the group, realize more accurate prediction and reasoning of social relationships, improved service quality and ride-sharing safety. To address the intricate task of balancing multiple objectives, including average detour cost, average response rate, and average user similarity rate, we introduce a novel evolutionary computation method for optimizing ride-sharing scenarios. This approach tackles the dynamic ride-sharing matching problem by emphasizing human factors in the optimization goals, successfully overcoming challenges related to local optima and convergence. Experimental validation confirms the effectiveness of OD-GAT in feature extraction and classification, showcasing the method’s fastest convergence speed and global optimum achievement across three key metrics.

With the expansion of mobile devices and cloud computing, massive spatial trajectory data is generated and outsourced to the cloud for storage and analysis, enabling location-based mobile computing services. However, due to the sensitivity of the trajectory data, sharing it in plaintext could lead to privacy risks, especially in operations like contact queries. Thus, achieving secure and efficient contact queries based on the trajectory data in the cloud is a significant challenge. In this paper, we propose a privacy-preserving contact query processing over trajectory data in mobile cloud computing. The projection-based secure trajectory encoding is designed to convert trajectories into secure codes such that the comparison between the distance of two moving objects and the contact distance threshold is transformed into a problem of secure code matching. Adopting the secure code matching method, a baseline privacy-preserving contact query processing is proposed. To improve the query accuracy and efficiency, an amplification factor, an HTG-index and a filter table are designed for query processing optimization, based on which an enhanced privacy-preserving contact query processing is proposed. The game stimulation-based security analysis and experimental results show that the proposed query scheme is secure and performs well in query accuracy and efficiency.

Federated Learning (FL) has emerged as a key enabler of privacy-preserving distributed model training in edge computing environments, crucial for service-oriented applications such as personalized healthcare, smart cities, and intelligent assistants. However, existing privacy-preserving FL methods are susceptible to multiple privacy leakage attacks (MPLA), where adversaries infer sensitive information through repeated gradient updates. This paper proposes a Robust and Communication-Efficient Federated Learning (RCFL) framework designed to enhance privacy protection and communication efficiency in edge-based service environments. RCFL integrates a global privacy-preserving mechanism with an innovative privacy encoding strategy that minimizes privacy risks over multiple data releases while significantly reducing communication overhead. The proposed framework's theoretical analysis demonstrates its ability to maintain differential privacy across numerous interactions, ensuring robust model convergence and efficiency. Experimental results using MNIST and CIFAR-10 datasets reveal that RCFL can lower the MPLA success rate from 88.56% to 42.57% compared to state-of-the-art methods, while reducing communication costs by over 90%. These findings underscore RCFL's potential to enhance security, efficiency, and scalability in service-oriented edge computing applications.

The development of cloud computing has met the growing demand for dataset search in the era of massive data. In the field of spatial dataset search, the high prevalence of sensitive information in spatial datasets underscores the necessity of privacy-preserving search processing in the cloud. However, existing spatial dataset search schemes are designed on plaintext datasets and do not consider privacy protection in search processing. In this paper, we first propose a privacy-preserving spatial dataset search scheme. The density distribution-based similarity model is proposed to measure the similarity between spatial datasets, and then the order-preserving encrypted similarity is designed to achieve secure similarity calculation. With the above idea, the baseline search scheme (PriDAS) is proposed. To improve the search efficiency, a two-layer index is designed to filter candidate datasets and accelerate the similarity calculation between datasets. By using the index, the optimized search scheme (PriDAS+) is proposed. To analyze the security of the proposed schemes, the game simulation-based proof is presented. Experimental results on three real-world spatial data repositories with 100,000 spatial datasets show that PriDAS+ only needs less than 0.4 seconds to accomplish the search processing.

During infectious disease outbreaks, tracking contacted objects is important for suppressing the spread of the virus and using trajectories of moving objects to discover contacted objects is one of effective approaches. Existing algorithms focus on individual contact event discovery but lack the ability to obtain the k-degree contact events. In this paper, we propose efficient k-degree contact pattern mining algorithms that are capable of mining k-degree contact events. The definition of k-degree contact event is first formulated. Based on the definition, a sliding window-based baseline k-degree contact pattern mining algorithm (KCPMA) is presented. To improve the mining efficiency, the sample-point checking strategy and R-tree index are adopted and the optimized mining algorithm (KCPMA+) is proposed. Comprehensive experiments on real datasets demonstrate that the proposed algorithms are effective and efficient in mining k-degree contact events.

With the significant increase in open spatial datasets, there is a growing need to search for datasets that meet users’ requirements for decision-making and machine learning. This has become a prominent issue, leading to various spatial dataset search requirements, including the need for spatial dataset range search. In this paper, we propose spatial dataset range search schemes, which is the first systematic study of spatial dataset range search processing according to the best of our knowledge. A baseline spatial dataset range search scheme is first proposed to process spatial dataset range searches. To improve the search efficiency, we proposed two optimized search schemes, the accuracy-first optimized search scheme and the efficiency-first optimized search scheme. In the former optimized scheme, the spatial dataset-MBR-based R-tree (SDMR-tree) is designed to filter candidate datasets without compromising search accuracy. In the latter optimized scheme, the dataset-grid inverted index (DGI-index) storing the spatial dataset grid distributions is designed and used to determine the search result approximately. The search efficiency is further improved but with a bit loss of accuracy. Comprehensive experiments on real-world data validate the accuracy and efficiency of the proposed search schemes.

The era of data explosion has brought a significant increase in demand for dataset search. However, deployment of spatial dataset search services in the cloud suffers from privacy leakage and search effectiveness issues. To resolve this problem, we first propose an exemplar privacy-preserving spatial dataset search scheme (EDSS) in this paper. EDSS extracts and encrypts the grid distribution of spatial datasets as metadata to enable the search processing on encrypted spatial datasets. Experimental results using three real-world spatial data repositories demonstrate that the proposed scheme can effectively implement exemplar spatial dataset search.

Urban electric load forecasting is an important content of urban electric system planning and dispatching. However, the problem of data imbalance in urban electric load forecasting leads to poor performance of single-model based methods. Existing multi-models based methods not only increase the cost of constructing models but also separate common time series features among different electric load profiles samples. In this paper, we propose an urban electric load forecasting model (DCSC-LSTM), which introduces the discrepancy compensation module and the short-term sampling contrastive loss to the Long Short-Term Memory. The DCSC-LSTM model uses the discrepancy compensation module to learn the discrepancies between samples with different electric load profiles, and the short-term sampling contrastive loss to regularize the training of the model. A series of experiments were conducted to validate the design of the DCSC-LSTM model. The experiments results show that the DCSC-LSTM model achieves competitive performance in the task of forecasting electric load.

Human action recognition is one of the basic problems in the field of computer vision, which has a wide range of applications in video surveillance, sports analysis, medical care. Since the skeleton data can not be easily affected by background and views and has small computational cost, skeleton-based action recognition has attracted a lot of researchers’ interest. In recent years, some researchers have proposed to use the joints connection methods to mine the intrinsic correlation between skeleton joints as the feature representation of motion skeleton, and good results are achieved. However, there is still the problem of confusion when distinguishing actions with similar motion segments. To solve this problem, a human skeleton feature vector model (HSFV) is proposed in this paper. By constructing the feature extraction reference frame, the model uses OKS indicator to calculate and generate the feature vector describing the human posture. The feature vectors are input into the recurrent neural network, the experimental results on public and self-built datasets show that the human skeleton feature vector model based action recognition method proposed in this paper can distinguish actions with similar motion segments, and has the advantages of simple training and small calculation cost. It has broad application prospects in the fields of process detection, motion evaluation and so on.

Image-based calculation of human pose similarity is one of the computer vision research fields. Most existing research uses the human skeleton joint to calculate the human pose similarity, but usually does not consider the influence of inaccurate recognition of skeleton joint on similarity calculation caused by the complex environment (such as the occlusion of body parts, etc.). We propose a human pose similarity calculation method based on partition weighted OKS model. Due to the influence of external factors such as occlusion, the skeleton joint extracted by the human pose estimation algorithm is inaccurate, which leads to the decrease of the accuracy of the human pose similarity calculation. We propose the partition rule of human skeleton joints and the dynamic strategy adjustment of partition weight. The partition weighted OKS model and a human pose similarity calculation method based on the partition weighted OKS model are given. The experimental results on datasets show that the proposed method for human pose similarity calculation is superior to the traditional one.

Using trajectories of moving objects and performing contact event query during disease transmission is an effective method of prevention and control. Existing contact query processing algorithms only consider single-source (one-to-one) contact event and thus can not discover multi-source (n-to-one) contact events. In this paper, we propose efficient multi-source contact event query processing methods that are capable of querying multi-source contact events. The definition of multi-source contact events is first formulated. Then, a baseline multi-source contact event query processing algorithm is presented, which adopts the idea of sliding window-based sequential scanning. To improve the query efficiency, the 2-dimensional bitmap filter and the anchor time point scanning are designed and used in the optimized query processing algorithm. Comprehensive experiments on real-world data demonstrate that the proposed algorithms can find more potential contact events and have good performance in the manner of query time cost.

Traditional searchable encryption schemes for clouds are generally based on TF-IDF vector space model, but they ignore the high-dimensional sparse characteristic of encrypted vectors. It will lead to substantial computational cost of inner product, and thus slows the search speed. In this paper, we propose a two-layer fast search index-based multi-keyword ranked search scheme (TFSRS) to address this problem. In the proposed TFSRS scheme, a keyword clustering-based equal-length dictionary partition (KCEDP) strategy is adopted to compress the document and search vectors, which benefits the process of inner product. Based on the strategy, a novel KCEDP-based vector space model (KCEDP-VSM) is proposed, and based on which, a two-layer fast search index (TFS-index) is presented. By using the TFS-index, secure inner product and symmetric encryption, the efficient multi-keyword ranked search scheme over encrypted cloud data is proposed. Experimental results show the better performance of the proposed schemes in search efficiency.

In recent years, vaccine safety incidents have occurred frequently. To protect vaccine safety, researchers have proposed to use blockchain to secure the vaccine circulation process. Technically, blockchain has some limitations in solving vaccine and other supply chain problems, such as large on-chain storage consumption and low throughput. To better alleviate these restrictions, we propose an improved, blockchain-based, storage-efficient vaccine safety protection scheme in this work. Specifically, we first model the vaccine circulation process. We then design a system to protect vaccine circulation using blockchain, cloud, and cryptographic mechanisms. The proposed system leverages the cloud to implement the vaccine circulation model. Correspondingly, it uses the blockchain to store circulating data certificates and signatures. We evaluated the proposed conceptual model using a consortium blockchain. The experimental results show that the proposed system is efficient.

The growing concerns about data privacy in society lead to restrictions on the computer vision research gradually. Several collaboration-based vision learning methods have recently emerged, e.g., federated learning and split learning. These methods protect user data from leaving local devices, and make training performed only by uploading gradients, parameters, or activations, etc. However, there is little research on collaborative learning based on state-of-the-art and large-scaled models, mainly due to the high computation or communication overheads of the latest models. Training these models may be still unrealized for users’ terminals. In this paper, we make a first attempt at the sensitive image pre-training with large-scaled models in the collaborative learning scenario, and propose a new lightweight framework for split learning based on mask, Masked Split Learning (MaskSL). We further ensure its security by differential privacy. Besides, we model the computation and communication overheads of several collaborative learning approaches by deduction to illustrate advantages of our scheme. Finally, we design and conduct a series of experiments on real-world datasets, e.g., in face recognition and medical image classification tasks, to demonstrate the performance of MaskSL.

In federated learning (FL), each client collaboratively trains the global model through the cloud server (CS) without sharing its original dataset in edge computing. However, CS can analyze and forge the uploaded parameters and infer the privacy of clients, which calls for the necessity of verifying the integrity and protecting the privacy for aggregation. Although there are some works to ensure the verifiability of aggregation results, there is still a lack of work on analyzing the relationship between verification and dropout rate for edge computing. In this work, we propose privacy-preserving and verifiable federated learning (PVFL) with low communication and computation overhead for verification. We theoretically demonstrate that PVFL has three properties: 1) the communication overhead for verification is independent of the dropouts and the dimension of the parameter vector; 2) the computation overhead for verification is independent of the dropouts; 3) the value of the loss function is negatively correlated with the number of dropouts. Experimental results demonstrate the correctness of our theoretical results and practical performance with a high dropout rate, thereby facilitating the design of privacy-preserving and verifiable FL algorithms for edge computing with a high dimension of parameter vectors and a high dropout rate.

Using the trajectory data of moving objects to analyze and study the infection mode of viruses or germs has practical application value. The definition of infection pattern in existing works only considers one-to-one infection mode rather than many-to-one mode, and thus some infection events could be ignored. This paper presents multi-source infection pattern mining algorithms oriented to moving objects for the first time. The multi-source infection event (MSIE) is defined. On the basis of MSIE, the multi-source infection pattern mining algorithm (MIPM) is proposed, which uses sliding window mechanism. The sliding window is used to record the set of candidate infection source that may infect each normal object at each time point, and then the determined infection events are mined. To improve performance, an optimized mining algorithm (MIPM+) based on the R-tree index is proposed, which can reduce the number of objects to be detected at each time point. The experimental results show that our proposed multi-source infection pattern mining algorithms can mine more potential infection events.

The document vectors constructed by the traditional searchable encryption scheme based on the term frequency-inverse document frequency model not only have high dimensionality and sparsity, but also ignore the semantic information of documents and keywords. In this paper, we introduce the sentence bidirectional encoder representations from transformers model (SBERT) to obtain semantic information-embedded vectors for documents and keywords. By adopting the SBERT model, we pro-pose a CBG-index based semantic-aware multi-keyword ranked search scheme (CSMRS). In the scheme, a topic-term frequency-inverse topic frequency (TTF-ITF) model and a clustering-based group index (CBG-index) are proposed. The TTF-ITF model is used to generate semantic vectors for keywords, and the CBG-index is used to improve the search efficiency. The experimental results demonstrate the better performance than the existing works in terms of search efficiency and search result semantic precision.

Traditional term frequency-inverse document frequency model-based privacy-preserving ranked search schemes rarely consider the latent semantic meanings of documents and keywords. It is a challenge to design efficient semantic-aware ranked search (SRSE) schemes with privacy preservation. In this paper, two privacy-preserving SRSE schemes are developed for the cloud environments. The first scheme is the accuracy-first search scheme. In this scheme, the Latent Dirichlet Allocation topic model is adopted to generate the topic-based semantic information-embedded vectors for documents and queried keywords, which supports semantic-aware relevance measurement. The bisecting k-means clustering algorithm is used to build an accuracy-first filtering tree index (AFF-tree), and the AFF-tree-based search algorithm is proposed to achieve the accuracy-first ranked search. The second scheme is the efficiency-first search scheme. It performs a structure optimization on the AFF-tree, and a newly efficiency-first filtering tree index (EFF-tree) is designed. By using the EFF-tree, an anchor node-based search algorithm is designed to achieve the efficiency-first ranked search at the expense of a little decrease in search result precision. The secure inner product is used to perform privacy-preserving semantic-aware relevance measurement between documents and queried keywords in both schemes. To analyze the security of the proposed schemes, the game stimulation-based proof is presented. Experimental results show the better performance of the proposed schemes in search time cost.

Traditional searchable encryption schemes based on the Term Frequency-Inverse Document Frequency (TF-IDF) model adopt the presence of keywords to measure the relevance of documents to queries, which ignores the latent semantic meanings that are concealed in the context. Latent Dirichlet Allocation (LDA) topic model can be utilized for modeling the semantics among texts to achieve semantic-aware multi-keyword search. However, the LDA topic model treats queries and documents from the perspective of topics, and the keywords information is ignored. In this article, we propose a privacy-preserving searchable encryption scheme based on the LDA topic model and the query likelihood model. We extract the feature keywords from the document using the LDA-based Information Gain (IG) and Topic Frequency-Inverse Topic Frequency (TF-ITF) model. With feature keyword extraction and the query likelihood model, our scheme can achieve a more accurate semantic-aware keyword search. A special index tree is used to enhance search efficiency. The secure inner product operation is utilized to implement the privacy-preserving ranked search. The experiments on real-world datasets demonstrate the effectiveness of our scheme.

Federated learning (FL) can protect clients’ privacy from leakage in distributed machine learning. Applying federated learning to edge computing can protect the privacy of edge clients and benefit edge computing. Nevertheless, eavesdroppers can analyze the parameter information to specify clients’ private information and model features. And it is difficult to achieve a high privacy level, convergence, and low communication overhead during the entire process in the FL framework. In this paper, we propose a novel privacy-preserving federated learning framework for edge computing (PFLF). In PFLF, each client and the application server add noise before sending the data. To protect the privacy of clients, we design a flexible arrangement mechanism to count the optimal training times for clients. We prove that PFLF guarantees the privacy of clients and servers during the entire training process. Then, we theoretically prove that PFLF has three main properties: 1) For a given privacy level and model aggregation times, there is an optimal number of participating times for clients; 2) There is an upper and lower bound of convergence; 3) PFLF achieves low communication overhead by designing a flexible participation training mechanism. Simulation experiments confirm the correctness of our theoretical analysis. Therefore, PFLF helps design a framework to balance privacy levels and convergence and achieve low communication overhead when there is a part of clients dropping out of training.

In federated learning, multiple parties may use their data to cooperatively train a model without exchanging raw data. Federated learning protects the privacy of users to a certain extent. However, model parameters may still expose private information. Moreover, existing encrypted federated learning systems need a trusted third party to generate and distribute key pairs to connected participants, making them unsuitable for federated learning and vulnerable to security risks. To mitigate these issues, we propose a privacy-preserving oriented no trusted third party federated learning system based on blockchain (NttpFL). The initiator of the federated learning task and the partners negotiate keys through the conference key agreement and do not need to distribute keys through a trusted third party. We design a double-layer encryption mechanism to ensure privacy. Partners cannot obtain any private information other than their information. The decentralized nature of blockchain suits our system. In addition, blockchain makes the entire process transparent and traceable and avoids the single node failure problem. Experimental results confirm that the proposed method significantly reduces the communication costs and computational complexity compared to existing encrypted federated learning without compromising the performance and security.

With the comprehensive development of cloud computing technology, more and more enterprises and individuals tend to outsource computing, data, and other resources to the cloud service providers to save the data management cost. Since the plaintext data outsourcing in the cloud could leak users’ private information, it is highly recommended to encrypt them before outsourcing. However, it is a challenge to perform searches over encrypted cloud data. In this paper, we adopt the keyword grouping idea into the traditional inverted index and propose a keyword-grouping inverted index (KGI-index). Based on the index, we propose a privacy-preserving KGI-index based multi-keywords ranked search scheme (KMRS). To improve the search efficiency, we adopt two strategies including grouping high relevant keywords and using the complete binary tree structure to optimize the index. The security analysis and experimental result show that the proposed scheme is a privacy-preserving and efficient multi-keyword ranked search scheme over encrypted cloud data.

With the popularity of mobile devices equipped with positioning devices, it is convenient to obtain enormous amounts of trajectory data. The development promotes the study of extracting moving patterns from trajectory data of moving objects. One such pattern is the convoy, which refers to a group of objects moving together for a period of time. The existing convoy mining algorithms have a large time cost because they adopt a density-based clustering algorithm over global objects. In this paper, we propose an efficient convoy mining algorithm (ECMA) that adopts the divide-and-conquer methodology. A block-based partition model (BP-Model) is designed to divide objects into multiple maximized connected nonempty block areas (MOBAs). The convoy mining problem is then solved by processing each MOBA sequentially, which significantly reduces the time cost of convoy mining. In the experiments, we evaluate the performance of our algorithm on real-world datasets. The results show that the ECMA is more efficient than existing convoy mining algorithms.

In recent years, vaccine incidents occurred around the world, which endangers people’s lives. In the technical respect, these incidents are partially due to the fact that existing vaccine management systems are distributively managed by different entities in the vaccine supply chain. This architecture makes it relatively easy to modify or even delete the vaccine circulation data maliciously, which makes tracing problematic vaccine hard and identifying the responsibility for a vaccine accident hard. To solve these issues, this article presents a blockchain-based solution to protect the whole process of vaccine circulation. We first propose a model to supervise the vaccine circulation process by incorporating existing regulatory practices. Then, we propose a blockchain-based tracing system to implement this model. The proposed system takes the blockchain as a global, unique, and verifiable database to store all the circulation data. Through data insertions and queries on the global and unique database, the proposed system achieves the protection of vaccine circulation. We also implement a proof-of-concept prototype of the proposed system. Experimental results confirm that the proposed system is beneficial.

In the wireless sensor networks, it is a challenging issue to protect the data privacy from curious users while providing top-k query services. In this paper, a novel privacy-preserving and collusion-resisting top-k query processing interactive protocol is proposed for WSNs. To the best of our knowledge, it is the first work providing the privacy preservation and collusion resistance simultaneously in top-k query processing in WSNs. Data encryption with different private keys, the bloom filter and HMAC are adopted to achieve data privacy preservation even there are a few sensors colluding with the adversaries. During the interactive procedures of the query processing, two rounds of secure interactions between the sink and sensors are performed to obtain the query results. The protocol analysis indicates that the protocol can preserve data privacy even a few sensors collude with the adversaries, while the experiment result shows that the proposed protocol has good performance on network communication cost.

In the interest of privacy concerns, cloud service users choose to encrypt their personal data before outsourcing them to cloud. However, it is difficult to achieve efficient search over encrypted cloud data. Therefore, how to design an efficient and accurate search scheme over large-scale encrypted cloud data is a challenge. In this paper, we integrate bisecting k-means algorithm and multibranch tree structure and propose the α-filtering tree search scheme based on bisecting k-means clusters. The novel index tree is built from bottom-up, and a greedy depth first algorithm is used for filtering the nonrelevant document cluster by calculating the relevance score between the filtering vector and the query vector. The α-filtering tree can improve the efficiency without the loss of search accuracy. The experiment on a real-world dataset demonstrates the effectiveness of our scheme.

Cloud storage security has gained considerable research efforts with the wide adoption of cloud computing. As a security mechanism, researchers have been investigating cloud storage auditing schemes that enable a user to verify whether the cloud keeps the user's outsourced data undamaged. However, existing schemes have usability issues in compatibility with existing real world cloud storage applications, error-tolerance, and efficiency. To mitigate this usability gap, this article proposes a new general cloud storage auditing scheme that is more usable. The proposed scheme uses the idea of integrating linear error correcting codes and linear homomorphic authentication schemes together. This integration uses only one additional block to achieve error tolerance and authentication simultaneously. To demonstrate the power of the general construction, we also propose one detailed scheme based on the proposed general construction using the Reed Solomon code and the universal hash based MAC authentication scheme, both of which are implemented over the computation-efficient Galois field GF(28). We also show that the proposed scheme is secure under the standard definition. Moreover, we implemented and open-sourced the proposed scheme. Experimental results show that the proposed scheme is orders of magnitude more efficient than the state-of-the-art scheme.

WSN-as-a-Service (WaaS) is a novel application model of wireless sensor networks (WSNs). Owners of WSNs provide data queries as services, while users pay for needed services as they use such services. The adoption of WaaS improves the usage of WSNs and reduces the cost of network deployment and maintenance. It is challenging to protect data from curious users while, at the same time, providing MAX/MIN query services. In this paper, we propose a privacy-preserving MAX/MIN query processing method for WaaS. To the best of our knowledge, this work is the first to discuss a privacy-preserving data query method in the WaaS environment. To implement privacy-preserving MAX/MIN queries, we propose a novel query protocol by adopting the idea of secure multiparty computation. The protocol consists of two cooperative query processing algorithms that are deployed in the aggregate sensor and normal sensors. During query processing, multiple rounds of secure interactions between sensors are performed. In each round, one bit of the query result is determined through cooperation of sensors, while the data of sensors participating in query processing remain private. Curious users cannot obtain any private data from the network even if a few compromised sensors collude with them. The analysis and evaluations indicate that the proposed protocol computes query results reliably, avoids the energy hole problem and is efficient in terms of communication cost.

Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.

With the advance of database-as-a-service (DaaS) and cloud computing, increasingly more data owners are motivated to outsource their data to cloud database for great convenience and economic savings. Many encryption schemes have been proposed to process SQL queries over encrypted data in the database. In order to obtain the desired data, the SQL queries contain some statements to describe the requirement, e.g., arithmetic and comparison operators ( + , - , × , < , > , and = ). However, to support different operators ( + , - , × , < , > , and = ) in SQL queries over encrypted data, multiple encryption schemes need to be combined and adjusted to work together. Moreover, repeated encryptions will reduce the efficiency of execution. This paper presents a practical and secure homomorphic order-preserving encryption (FHOPE) scheme, which allows cloud server to perform complex SQL queries that contain different operators (such as addition, multiplication, order comparison, and equality checks) over encrypted data without repeated encryption. These operators are data interoperable, so they can be combined to formulate complex SQL queries. We conduct security analysis and efficiency evaluation of the proposed scheme FHOPE. The experiment results show that, compared with the existing approaches, the FHOPE scheme incurs less overhead on computation and communication. It is suitable for large batch complex SQL queries over encrypted data in cloud environment.

With the development of cloud computing, services outsourcing in clouds has become a popular business model. However, due to the fact that data storage and computing are completely outsourced to the cloud service provider, sensitive data of data owners is exposed, which could bring serious privacy disclosure. In addition, some unexpected events, such as software bugs and hardware failure, could cause incomplete or incorrect results returned from clouds. In this paper, we propose an efficient and accurate verifiable privacy-preserving multikeyword text search over encrypted cloud data based on hierarchical agglomerative clustering, which is named MUSE. In order to improve the efficiency of text searching, we proposed a novel index structure, HAC-tree, which is based on a hierarchical agglomerative clustering method and tends to gather the high-relevance documents in clusters. Based on the HAC-tree, a noncandidate pruning depth-first search algorithm is proposed, which can filter the unqualified subtrees and thus accelerate the search process. The secure inner product algorithm is used to encrypted the HAC-tree index and the query vector. Meanwhile, a completeness verification algorithm is given to verify search results. Experiment results demonstrate that the proposed method outperforms the existing works, DMRS and MRSE-HCI, in efficiency and accuracy, respectively.

We consider a capable and scalable network model named two-tiered sensor network which consists of regular resource-limited sensor nodes and powerful storage nodes with abundant resources. In such architecture, storage nodes are on charge of storing data collected and submitted by sensor nodes as well as processing queries from the base station. Owing to the importance role that storage nodes play, they are more vulnerable and attractive to adversaries in a hostile environment. A compromised storage node may inject fake data into and/or omit qualified data from its returned responses, which make the base station not able to obtain authentic and/or complete results. This paper proposes EVTQ, a novel and efficient verifiable top-k query processing which is capable of verifying the authentication and completeness of query result. To achieve such security features, sensor nodes are settled to submit their collected data items together with corresponding codes which embed ordered and adjacent relationships of the collect data items by a hashed message authentication coding function. Thus any attack that leads to unauthentic and incomplete query result will be detected. According to this basic idea, the data submission and query processing protocols are proposed to describe the details of EVTQ. Moreover, a hash based optimization is presented to save more communication cost. The simulation result shows that EVTQ is more efficient than the existing work in communication cost.