Fully homomorphic encryption (FHE) is a powerful technique that allows unlimited computations on encrypted data without decryption. However, FHE will incur huge computation and storage costs, making it difficult to be applied in real environments. To improve the efficiency of FHE, some optimized schemes have been proposed based on the trusted execution environment (TEE), which offer a lighter and lower overhead solution for FHE optimizations to a certain extent. However, they heavily rely on the confidentiality of the TEE, and their performance is still limited. To solve the above problems, we propose an efficient TEE-assistance CKKS scheme without bootstrapping, named TCKKS, which has the characteristics of security, efficiency, low memory, and scalability. First, to weaken the trust assumption of TEE, we consider TEE to be honest-but-curious, meaning the enclave's algorithm provider will execute the algorithm honestly but might monitor the data in the enclave. Based on this assumption, we design a lightweight secure multiplication protocol (SMP) and a secure rotation protocol (SRP) for TCKKS to efficiently achieve ciphertext multiplication and rotation operations. Then, to further improve the performance of TCKKS, we optimize the arithmetic operations and encryption/decryption operations based on the characteristics of our protocols. Moreover, we prove the security of SMP and SRP under the simulation-based real/ideal worlds model and further demonstrate the security of TCKKS based on the RLWE problem. In addition, extensive experiments indicate that TCKKS has a better performance than mainstream libraries, such as RNS-HEAAN, PALISADE and SEAL.

Promoting data sharing is one of the critical strategies for thriving in the digital age, and enormous demand for location-based services propels data sharing in the Internet of Vehicles (IoV), particularly in the case of integrating mobile crowdsensing (MCS). However, data security and privacy concerns are increasingly posing serious challenges to the development of data sharing. Although a slew of works have been designed to achieve secure data sharing in IoV, they are inadequate for addressing the privacy issues identified in the data sharing model and often suffer from performance limitations. In this work, we propose an efficient and secure data sharing scheme under the MCS-integrated IoV. Specifically, motivated by the distributed point function (DPF), we design a double-output DPF and leverage it to construct a secure updating scheme that protects full privacy while ensuring high efficiency. Then, based on the XOR filter and a series of subtle transformations, we carefully design a secure spatial test protocol to determine whether a point falls within an arbitrary spatial range efficiently. Afterward, we propose a secure retrieving protocol by using the idea of shared shuffling, in which the offline sub-protocol is presented to generate random masks, and the online sub-protocol is designed to quickly retrieve the desired data items. After formally proving the security of our proposed schemes, we experimentally evaluate their efficiency by comparing them with the alternative solutions, and the results indicate that our proposed schemes offer superior performance, particularly in terms of communication overheads.

Searchable encryption (SE) allows users to perform private queries on encrypted databases. Although SE schemes can protect data privacy, some often pursue high performance while allowing certain leakages, such as search and access patterns. Exploiting such leakage together with other knowledge similar to the user’s database, an attacker can recover queries. State-of-the-art attacks (Nie et al., USENIX’ 24) on single-keyword queries achieve accuracies exceeding 90%. More recently, the community has focused on the more challenging attack of recovering multikeyword queries, with the most advanced attacks (Liu et al., TIFS’ 25) achieving over 80% accuracy. Although these attacks can effectively recover queries, they all rely on a large amount of similar document, requiring the attacker to possess documents equivalent in volume to the database. This naturally raises a question: Can we achieve higher-accuracy attacks using less similar data? Less information makes attacks easier to implement. Motivated by this, we present Mirage, an attack that recovers both single-keyword and multi-keyword queries while requiring only partial similar data. Our core idea is to first identify some special queries and design a series of novel algorithms to recover them. Then, partially reconstruct the database index and recover the remaining queries. Extensive experiments conducted across various real-world datasets demonstrate the effectiveness of our attack. The results show that when the attacker observes 51 time intervals and obtains only 0.5% of similar documents in each interval, Mirage achieves 91.6% and 95.4% accuracy on the Enron and Lucene datasets for single-keyword queries, respectively. For multi-keyword queries, Mirage achieves up to 90.7% and 93.5% recovery accuracy, respectively.

In the context of the Internet of Things (IoT), the large-scale generation and collection of data can greatly improve the quality of service provided, but they also raise significant concerns about privacy breaches. However, existing privacy-preserving data collection solutions based on local differential privacy (LDP) often struggle to balance security and accuracy when handling composite data types. To address this challenge, in this paper, we propose CSKV, a high-precision and privacy-preserving key-value data collection scheme. Specifically, we first design a padding and sampling protocol to improve data utility. Then, we propose two randomized response mechanisms to safely perturb keys and values in a cohesive and segmented manner. After that, by leveraging the sampling protocol and key-value correlation perturbation, we demonstrate that CSKV can provide secondary privacy amplification. Detailed theoretical analysis verifies the security and effectiveness of CSKV. In addition, extensive performance evaluations are conducted on synthetic and real-world datasets, and the results indicate that our proposed scheme outperforms existing schemes in terms of hit rate and estimation variance.

In the advancing field of information technology, the Industrial Internet of Things (IIoT) plays a pivotal role across various domains. To fully leverage the massive data generated by IIoT, the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm is well-suited for clustering large-scale time series data due to its incremental and order-sensitive design. Although aggregating multisource data on cloud servers becomes a practical and effective approach, it raises privacy concerns. Existing privacy-preserving BIRCH schemes designed to mitigate these concerns, however, rely on homomorphic encryption for atomic operations and reveal the insertion path of sample points, resulting in significant limitations in performance and security. To address these issues, we propose SBIRCH, a novel secure BIRCH scheme. Specifically, we first introduce oblivious hierarchical updates to preserve insertion paths. Then, we present a shuffling-based obfuscation technique to protect intermediate clustering results during node splitting. In addition, we optimize division operations on encrypted data, delivering high precision and significantly improving efficiency. Security analysis confirms that our scheme effectively protects multisource data, intermediate clustering results, and clustering relationships. While maintaining consistent security, experimental evaluations demonstrate that our scheme achieves substantial improvements in computational efficiency and markedly reduces communication overheads compared to existing schemes.

Merging multi-source time series data in cloud servers significantly enhances the effectiveness of analyses. However, privacy concerns are hindering time series analytics in the cloud. Responsively, numerous secure time series analytics schemes have been designed to address privacy concerns. Unfortunately, existing schemes suffer from severe performance issues, making them impractical for real-world applications. In this work, we propose novel secure time series analytics schemes that break through the performance bottleneck by substantially improving both communication and computational efficiency without compromising security. To attain this, we open up a new technique roadmap that leverages the idea of mixed model. Specifically, we design a non-interactive secure Euclidean distance protocol by tailoring homomorphic secret sharing to suit subtractive secret sharing. Additionally, we devise a different approach to securely compute the minimum of three elements, simultaneously reducing computational and communication costs. Moreover, we delicately introduce a rotation concept, design a rotation-based hybrid comparison mode, and finally propose our fast secure top-$k$protocol that can dramatically reduce comparison complexity. With the above secure protocols, we propose a practical secure time series analytics scheme with exceptional performance and a security-enhanced scheme that considers stronger adversaries. Formal security analyses demonstrate that our proposed schemes can achieve the desired security requirements, while the comprehensive experimental evaluations illustrate that our schemes outperform the state-of-the-art scheme in both computation and communication.

With the rapid advancement and widespread ap plication of the graph neural network (GNN), the collaborative graph learning, in which multiple parties collaboratively construct a GNN model using their respective graph data, has attracted increasing attention. However, this paradigm also raises significant privacy concerns, as both nodes and edges may contain sensitive personal information, while existing privacy preserving schemes often come at the cost of degraded model performance or substantial system overhead. Therefore, this paper proposes an efficient and privacy-preserving collaborative, and Hui Li, Member, IEEE, Xiaoyu Kou Social Platform learning framework on vertically partitioned graph data, dubbed Plog. Specifically, we first design a decomposition algorithm to split the sparse adjacency matrix into the summation of multiple independent permutations, which are lightweight, parallelizable, and well-suited for secure multi-party computation. Building on this, a weighted oblivious batch permutation protocol is carefully customized based on correlated randomness to securely and efficiently compute adjacency matrix multiplications, addressing the core efficiency bottleneck in GNN inference and training. The selective security of Plog is formally verified under the ideal-real paradigm. Extensive experimental results on three real world datasets demonstrate that compared to the state-of-the art scheme, Plog can reduce online communication rounds by 46% and achieve a 1.73× speedup in the overall inference and training time.

Fully Homomorphic Encryption (FHE) allows computations on encrypted data. However, FHE faces significant performance overhead due to the time-consuming homomorphic multiplications and bootstrapping operations, which limits its practical applications. Meanwhile, many FHE optimization schemes have been proposed based on a Trusted Execution Environment (TEE), but they heavily rely on the trustworthiness of the TEE. In outsourcing scenarios, the TEE owner might be a third-party service provider, posing a risk of data leakage for users. To address these issues, we propose an efficient BGV scheme with semi-honest TEE assistance. We consider the TEE to be honest-but-curious, meaning the TEE owner might eavesdrop on the user's data during computation. Based on this assumption, we design a new BGV homomorphic multiplication protocol that operates in a smaller ciphertext space without compromising the original security level and substantially improves computational efficiency. With the assistance of a semi-honest TEE, our multiplication protocol eliminates the need for the switch key, reducing initialization time and supporting an unlimited number of homomorphic multiplication operations without bootstrapping. Furthermore, we provide a security proof using a simulationbased real/ideal world model. Extensive experiments show that our scheme outperforms mainstream libraries such as SEAL, OpenFHE, and Helib.

Weighted set sampling has been proven essential for generating discrete numbers based on their weights and found broad applications in recommendation systems. The extension of this method, known as weighted range set sampling (WRSS), specifies a query range and applies weighted set sampling to the data within that range. With the proliferation of cloud computing, outsourcing encrypted data and data processing tasks to cloud servers has become a common practice to overcome data storage and processing challenges while protecting data privacy. Existing studies have proposed many privacy-preserving solutions for various customized query and data processing tasks, none have specifically addressed privacy-preserving WRSS. In response to this gap, our paper introduces an efficient and privacy-preserving WRSS scheme. We begin by leveraging the three-party secret sharing (TPSS) scheme as a foundation to design an enhanced three-party secret sharing (eTPSS) scheme with superior storage and computational efficiency. Building upon the eTPSS scheme, we introduce a series of private algorithms to safeguard WRSS privacy. Our scheme integrates the use of a binary search tree and the alias method for WRSS, ensuring privacy through eTPSS-based private algorithms. A thorough security analysis under the simulation-based real/ideal worlds model showcases the effectiveness of our proposed scheme. The proposed scheme's efficiency has been substantiated through extensive experiments, demonstrating that our scheme marks a significant advancement in addressing the challenges posed by privacy-preserving WRSS.

Vertical federated learning for boosting trees has gained significant attention due to its ability to enable participants to collaboratively train high-quality models while preserving data privacy. However, existing privacy-preserving vertical boosting tree schemes suffer from high computation and communication costs or potential security vulnerabilities. Recently, SGBoost, a federated outsourced training and inference scheme, was proposed to address these challenges. However, its performance and security still require significant improvements. Therefore, we propose SGBoost+, an efficient and privacy-preserving vertical boosting tree framework for federated outsourced training and inference. Building upon the strengths of SGBoost, we introduce an RLWE-based lossless and secure internal node construction and an efficient oblivious inference algorithm to finish the model training and inference, significantly enhancing both security and efficiency. To reduce communication cost, we design a ciphertext compression algorithm for model training, which drastically minimizes data transmission costs. Additionally, we analyze the security of a symmetric encryption scheme, specify the required security conditions and parameters, and optimize our model inference based on its improved and secure version. Detailed security analysis confirms that SGBoost+offers strong privacy guarantees. Extensive experiments demonstrate that SGBoost+achieves efficient model training and inference with significantly lower computation and communication costs compared to state-of-the-art schemes.

Local differential privacy (LDP) provides lightweight and provable privacy protection and has wide applications in private data collection. Key-value data, as a popular NoSQL structure, requires simultaneous frequency and mean estimations of each key, which poses a challenge to traditional LDP-based collection methods. Despite many schemes proposed for the privacy protection of key-value data, they inadequately solve the condensed perturbation for keys and the advanced combination of privacy budgets, leading to suboptimal estimation accuracy. To address this issue, we propose an efficient key-value collection scheme (COKV) with tight privacy budget composition. In our scheme, we first design a padding and sampling protocol for key-value data to avoid privacy budget splitting. Second, to enhance the utility of key perturbation, we design a key perturbation primitive and optimize the perturbation range to improve computational efficiency. After that, we propose a key-value association perturbation algorithm whose value perturbation strategy guarantees the output expectation equals the original value. Finally, we demonstrate that through a tight privacy budget composition, COKV can provide higher data utility under the same privacy level. Theoretical analysis shows that COKV possesses lower frequency and mean estimations variance. Extensive experiments on both synthetic and real-world datasets also indicate that COKV outperforms the current state-of-the-art methods for secure key-value data collection.

The rapid development of big data and Internet of Things has promoted the formation of data silos, and cloud computing has facilitated the outsourcing of vertically distributed data to cloud servers. In outsourced query scenarios, building query indexes is crucial for balancing data utility and data privacy protection. The hypercube tree is a widely used index for multi-dimensional data, supporting various query types. Although secure hypercube tree-based queries have been extensively studied in existing works, they are not applicable for building a hypercube tree over vertically distributed ciphertext data. To address this issue, we propose the first efficient and secure hypercube tree building scheme for vertically distributed data, named SCTBuild. We first design a flexible three-party secret sharing (fTPSS) scheme, allowing data owners to flexibly configure secret sharing forms based on real-world computational, communication, and storage constraints. Then, we design a communication-efficient data outsourcing algorithm, a secure data permutation algorithm, and a secure data comparison algorithm based on the fTPSS scheme. After that, we propose our SCTBuild scheme based on the aforementioned algorithms, in which data owners first perform pre-computation on their data to improve tree-building efficiency. We prove that our fTPSS scheme, private algorithms, and the SCTBuild scheme are semantically secure in the simulation-based real and ideal worlds security model; and conduct experiments to validate their high efficiency.

Logistic regression is a widely used and efficient machine learning algorithm for data analysis. However, data is often distributed among different participants, and building high-quality models often involves data interaction, which might easily lead to sensitive information leakage. While some schemes have been proposed to address the data security issue through privacy-preserving technologies such as multi-party computation, homomorphic encryption, and trusted execution environment (TEE), they still suffer from some limitations in terms of security, efficiency, and model integrity and are not suitable for the secure outsourcing training. Therefore, in this paper, we propose an efficient and lossless integrity-preserving training for high-dimensional logistic regression over vertically partitioned data. First, we utilize a block strategy to encrypt the data in batches and outsource them to a cloud with TEE to achieve the non-interactive training. Considering TEE’s limited memory, we employ random sampling technology to achieve mini-batch training within TEE, which ensures the data security. We find that the cloud might launch the active attacks to drop the model accuracy, such as lazy attack and replacement attack. To prevent the attacks, we introduce an integrity protection mechanism to verify data integrity and reduce verification frequency using a mixed-probability checking method, including fixed and random probability checking. Detailed analysis has confirmed that our scheme is secure and integrity-preserving. Extensive experiments have demonstrated that our scheme can achieve lossless, non-interactive, and efficient model training.

Big data have witnessed a growing trend towards vertically distributed storage, with various queries on vertically organized data recognized as effective means for unlocking data's inherent value. Several solutions have emerged for enabling privacy-preserving queries on vertically distributed data using secure multi-party computation techniques. However, these approaches often involve substantial communication overheads among data owners and place significant computational burdens on them, rendering them impractical for resource-constrained data owners. Outsourcing vertically distributed queries to the cloud can substantially alleviate the computational burdens on data owners, and efficient index construction is crucial for outsourced queries on vertical data. In light of this, we present the pioneering “Privacy-Preserving k-d Tree Building” (PTreeB) scheme for vertically distributed outsourced data in this study. Our scheme begins with the development of a private random dimension choosing algorithm (PCDim) and a private equality test (PET) algorithm, leveraging additive Paillier homomorphic encryption. Subsequently, these algorithms, along with various efficiency-enhancing strategies, including pre-sorting each data owner's data and adopting a dual-key system for data privacy protection, form the foundation of our PTreeB scheme. We rigorously demonstrate the security of our scheme, and its efficiency is validated through extensive experimentation.

Machine learning-based trajectory-user linking (TUL) task, which explores human mobility patterns and identifies known users from anonymous trajectories, is widely applied in location-based personalized recommendation systems. However, TUL models implicitly and inadvertently retain users historical trajectories in training data, which could be revealed under malicious inference and analysis. To address this issue, we propose UNIRE, a knowledge transfer-style TUL model aggregation framework to protect sensitive training data. Specifically, teacher TUL models are trained with private users trajectories and then utilized to collaboratively annotate a sensitive-trajectories-isolated dataset. Leveraging this teacher-labeled dataset, student TUL model can be trained and released as the secure aggregation of teacher models, preventing attackers from arbitrarily accessing or exposing the private trajectories. Meanwhile, differential privacy is applied for safeguarding the knowledge transfer process, ensuring that aggregation information from individual teacher models is indistinguishable. Furthermore, we introduce trajectory Equalization and Pseudo Classification as data alignment and accuracy optimization mechanisms for training and aggregating, respectively. Finally, security analysis and extensive experiments indicate that UNIRE framework achieves effective sensitive training data preservation and performance-improved TUL models aggregation, compared to state-of-the-art baselines.

XGBoost, a widely-used machine learning algorithm, has been applied across various fields. To develop a high-quality model, federated learning is employed, allowing participants to keep their data localized while sharing gradients for global model training. However, recent studies have shown that gradients can leak sensitive information, and even when protected, they might still be vulnerable to certain attacks. While several privacy-preserving and verifiable schemes have been proposed to address this issue, they still face limitations in terms of computational and communication costs in certain scenarios. To solve the problem, we propose a privacy-preserving and verifiable XGBoost training scheme for horizontal federated learning. First, we propose a masking with one-time padding protocol (MOTP) to securely aggregate gradients, supporting both online and offline modes for generating random masks. Both approaches reduce the communication costs and improve the training efficiency. Next, we present a linear aggregation verification protocol (LAVP) to ensure the gradient integrity, which avoids complex computations, thereby improving verification efficiency. Building on MOTP and LAVP, we propose a privacy-preserving and verifiable internal node construction algorithm to train a XGBoost model for horizontal data. Detailed security analysis and extensive experiments demonstrate that our scheme can achieve the privacy-preserving, verifiable, lossless, and efficient XGBoost training for horizontally partitioned data.

As one of the most popular queries in big data era, the$k$nearest neighbors ($k$NN) query plays a significant role in various applications, such as medical diagnosis, signal processing, and recommendation systems. Meanwhile, driven by the advancement of the cloud service, an emerging trend among applications is to outsource the dataset and the corresponding$k$NN query services to the cloud. However, as the cloud is not fully trusted, those applications will face vital privacy concerns, and thus they usually encrypt data before outsourcing them to the cloud. Because encrypted data are outsourced to cloud, the$k$NN query over encrypted data has become increasingly attractive, and many solutions have been put forth in recent years. However, existing solutions cannot fully satisfy the objects of returning exact query results, protecting database privacy and query privacy, achieving high query efficiency, and imposing low computational costs at the user side. To address these issues, in this paper, we propose a new practical and privacy-preserving$k$NN query scheme. Specifically, we first refine the general security requirements for the matrix encryption by systematically analyzing existing algorithms. Then, we design a novel asymmetric matrix encryption (AME) to securely achieve Euclidean distance computation and two distances comparison in a single-party and non-interactive way. Then, based on the AME scheme, we propose a privacy-preserving$k$NN query scheme, in which a max-heap of size$k$is used to accelerate query efficiency. Detailed security analysis shows that our proposed scheme is really privacy-preserving. In addition, extensive performance evaluations are conducted, and the results demonstrate that our proposed scheme is also highly efficient.

Big data and bursting cloud computing technologies have facilitated an increasing trend of outsourcing data-driven services to the cloud, where the reverse kNN (RkNN) query is a popularly outsourced query service. The RkNN query aims to retrieve objects having the query object as kNN and widely applied in the product recommendation. Considering privacy concerns, the outsourced query services are demanded to protect data privacy, and consequently a series of privacy-preserving query solutions have been put forth. Nevertheless, RkNN query over high-dimensional data has not been studied to date. In this work, we design the first efficient and privacy-preserving RkNN query scheme over encrypted high-dimensional data, named PHRkNN. Specifically, we first introduce a pivot filter condition for the RkNN query and utilize it to deliberately design a pivot filter R-tree (PFR-tree) to organize the high-dimensional dataset such that the RkNN query has sublinear query efficiency. Then, we propose our PHRkNN scheme by designing some homomorphic encryption based private algorithms and applying them to privately achieve PFR-tree based RkNN query. After that, we propose an oblivious PHRkNN scheme on the basis of the PHRkNN scheme by designing a private random tree permutation (PRTP) algorithm to protect the access pattern privacy. The security of our PHRkNN scheme and oblivious PHRkNN scheme is proved by the simulation-based security analysis. The performance is verified through computational costs and communication overheads evaluation.

The emergence of cloud computing enables various healthcare institutions to outsource pre-diagnostic models and provide timely and convenient services for patients. However, healthcare institutions and patients have serious concerns about potential privacy leakage as cloud servers cannot be fully trusted. In this paper, a privacy-preserving cloud-assisted medical pre-diagnosis scheme, named NAIAD, is proposed, where patients can securely query the outsourced model and obtain their pre-diagnostic results. Specifically, the pre-diagnostic model is constructed on$k$-Nearest Neighbor ($k$NN), and Mahalanobis Distance (MD) is chosen as the similarity metric to achieve high accuracy. Accordingly, a secure MD-based comparison method (SMDC) is designed based on a matrix encryption technique. The method is a basic module of NAIAD that enables cloud servers to compare encrypted medical records and achieve privacy-preserving$k$NN-based pre-diagnosis with linear complexity. To further improve the computational efficiency, medical records are first clustered and encrypted to construct a hierarchical index tree, then patients can query the tree to speed up the query process. Detailed security analysis indicates NAIAD can resist closeness-same-pattern chosen-plaintext attack, and extensive experiments on real-world and synthetic databases demonstrate NAIAD has high query efficiency and pre-diagnosis accuracy.

With the rapid advancements in machine learning and the widespread adoption of Model-as-a-Service (MaaS) platforms, there has been significant attention on convolutional neural network (CNN) inference services. However, traditional inference services over plaintext data and models are susceptible to the risks of data and model leakage. Although several privacy-preserving CNN inference schemes utilizing trusted execution environment (TEE) and cryptography have been proposed, their security models and performance still have limitations in some scenarios. Aiming at the above challenges, we present an oblivious neural network prediction scheme with semi-honest TEE, namely ToNN, which ensures the security of users’ inputs, outputs, and the model itself. Specifically, based on the limited memory of the TEE, we design secure protocols to perform CNN calculations securely and efficiently, which are friendly to support the single instruction multiple data technique. Additionally, we propose a look-up-table method to optimize the convolution and pooling layers calculations. A detailed security analysis under the simulation-based real/ideal worlds model shows that ToNN can achieve the desired security. Extensive simulation results further demonstrate that ToNN can improve the performance of linear calculations by$\textbf {4.86}\times $and non-linear calculation by$\textbf {37.68}\times $, and can be implemented effectively with low computation and communication costs.

The increasing prevalence of cloud computing drives the exploration of various secure query schemes over encrypted data, among which secure spatial keyword query has drawn a great deal of attention due to its broad application in location-based services. However, most existing schemes are either limited to the boolean keyword test or incapable of protecting access pattern privacy. Although the state-of-the-art secure spatial keyword query scheme can support keyword similarity while preserving access pattern privacy, it is unable to cope with the arbitrary spatial range, which is more general, and has limitations in efficiency and security. In this paper, we propose a new secure spatial keyword similarity query scheme that can support arbitrary spatial ranges and enhance the efficiency and security of the state-of-the-art scheme at the same time. Specifically, we first present a new homomorphic encryption technique by improving the popular symmetric homomorphic encryption (SHE). After that, we propose a novel approach to make supporting arbitrary spatial ranges over encrypted data possible, in which a spatial encoding technique is designed to improve performance. Finally, by designing a pack-based solution to protect access pattern privacy, our proposed scheme can hide the number of query results while optimizing performance. We formally prove the security of our proposed scheme and conduct experiments to evaluate its performance. The results indicate that our proposed scheme outperforms the state-of-the-art scheme in both the computational costs and communication overhead.

Outsourcing big data to cloud servers has gained prominence, and growing concerns about privacy, alongside privacy-related regulations, underscore the need to encrypt data before sending them to the cloud. Nevertheless, encryption significantly hampers the query capabilities of data, particularly in the case of vertically distributed data. This paper focuses on developing secure and efficient similarity query schemes for vertically distributed data in cloud environments. As is known, current solutions are constrained by limitations in query efficiency, approximate query results, and their ability to support vertical data. To address these issues, we introduce two novel schemes: a Fast Similarity Query Scheme (FSQ) and a Non-interactive Similarity Query Scheme (NoSQ) for outsourced distributed data. In the FSQ scheme, we enhance query efficiency by designing a trusted execution environment (TEE) assisted fast secret sharing (FSS) scheme and a series of FSS-based private algorithms, enabling secure data index construction and fast similarity query processing. For the NoSQ scheme, we eliminate communication overheads by designing a TEE assisted non-interactive secret sharing (NoSS) scheme and a series of NoSS-based private algorithms. Both schemes have undergone rigorous security validation using a simulation-based real/ideal worlds model, and their efficiency has been confirmed through comprehensive experiments.

With the rapid growth of Internet data volumes, Big Data analysis technologies have gradually permeated all aspects of life. However, the existence of data silos and the promulgation of relevant regulations make it challenging to apply these technologies. In this context, federated learning provides a feasible solution. Especially, XGBoost schemes for vertical federated learning have attracted much attention due to the widespread use of XGBoost. However, these schemes have limitations in terms of security or efficiency. To address these issues, we propose an efficient and privacy-preserving vertical federated learning framework based on the XGBoost algorithm, namely ELXGB, which achieves secure data alignment, XGboost training, and inference services. First, we design two node split algorithms based on homomorphic encryption and differential privacy, which securely and efficiently achieve tree node generation to construct the global model. Then, we utilize attribute obfuscation and direction obfuscation to achieve a secure inference algorithm, which avoids sensitive information leakage and protects the global model. Additionally, the global model of ELXGB is designed to be centralized, which does not require all participants to stay online for inference. Detailed security analysis demonstrates that ELXGB is privacy-preserving. Moreover, extensive experiments on real-world datasets indicate that ELXGB achieves high efficiency without sacrificing model accuracy.

The ever-growing data scale and increasingly strict privacy restraint have recently drawn extensive attention to federated learning (FL) as a multi-party machine learning paradigm for achieving high-quality model construction without data collection. Nevertheless, uploading local models in FL can still be exploited by adversaries to infer participants' sensitive data. Furthermore, it is possible for malicious participants to manipulate the global model by submitting poisonous local models. To tackle these challenges, this paper proposes an efficient and privacy-preserving federated learning framework against poisoning adversaries, namely ELFL, which can ensure the confidentiality of local models while effectively resisting data poisoning attacks. Specifically, we first design a grouped secure aggregation algorithm, through which the aggregation server can compute the summations of local models inside logic groups but cannot see individual ones. Then, based on grouped aggregations, our poisoning defense mechanism could detect and quickly phase out malicious participants from training candidates. Moreover, the computational complexity of participants is independent of their total number, so it is suitable for large-scale scenes. Detailed security analysis demonstrates the security of ELFL. Experimental results show that ELFL could maintain a high accuracy against representative data poisoning attacks, and its computational and communication overhead is indeed low.

Set similarity query is a fundamental query type in various applications, such as clinical diagnosis, online shopping, and mobile crowdsensing. Meanwhile, as the prevalence of outsourced query services, privacy-preserving set similarity query has been considerablely studied. However, to the best of our knowledge, most previously reported solutions suffer from applicability, efficiency, or security issues. Aiming at addressing these issues, we propose an efficient and privacy-preserving set similarity range query scheme (EPSet), where Jaccard similarity is employed as the similarity metric. Specifically, the set similarity range query is first transformed into multi-dimensional range queries by leveraging the triangle inequality of Jaccard distance. Then, a pivot-based k-d tree is designed for indexing the dataset and processing the set similarity query. After that, we design homomorphic encryption based privacy-preserving filter/refinement protocols, respectively named as PPF and PPR, to protect set similarity query privacy, and propose our EPSet scheme. The security of our scheme is proved under the simulation-based real/ideal model, and the performance is validated thorugh the extensive experiment evaluation.

Similarity range query over string sequences plays a significant role in bioinformatics, entity extraction, data mining and information retrieval. Meanwhile, with the popularity of cloud computing, a new paradigm is to outsource the similarity range query service to the cloud. However, existing solutions still have suffer from security and query efficiency drawbacks. To address these issues, we propose an efficient and private similarity range query scheme over encrypted string sequences. Specifically, we first organize string sequences into inverted index and design an inverted index based efficient similarity range query algorithm by applying several filter strategies. Second, based on public-key homomorphic encryption, we design a set of privacy-preserving protocols to protect the privacy of inverted index based range queries. Based on this, we propose a private and computationally efficient similarity range query scheme. Finally, we analyze the security of our scheme and conduct experiments to evaluate its performance, and the results indicate that our proposed scheme is computationally effective.

The explosive growth of data scales has recently drawn extensive attention to machine learning (ML) prediction services for achieving effective and efficient decision-making. However, the potential risk of model or data leakage, as well as vertically partitioned query data in real-world scenes, bring many challenges to ML prediction. Therefore, this paper proposes an efficient and privacy-preserving logistic regression prediction scheme, through which query institutions with vertically partitioned data can enjoy secure, fast, and high-accuracy prediction services from one cloud service provider. Specifically, we take the efficiency advantage of the trusted execution environments (TEE) technique under a semi-honest model, and design a series of masking and recovery methods based on homomorphic encryption to guarantee privacy inside and outside the enclave. Moreover, the ciphertext packing technique is integrated into our scheme for processing predictions parallelly, which further reduces communication and computational overhead. As a result, our scheme will not introduce additional strong security assumptions to TEE; meanwhile, it achieves a trade-off between security and efficiency. Based on the simulation-based paradigm, we formally prove the selective security of our scheme. Extensive experiments on real-world and synthetic datasets show that our prediction accuracy is comparable with plaintext prediction, and our scheme has at least a 30× running speedup compared to the existing representative schemes.

The hidden Markov model (HMM) has played a pivotal role in various IoT applications due to its ability to model time-varying sequences. Since the datasets usually live in isolated islands and their privacy naturally demands to be seriously considered, the HMM should be trained in a privacy-preserving manner. A typical HMM training framework is federated learning, in which a federated server and many data owners collaboratively train an HMM without revealing data owners' data to the federated server and the trained model to data owners. Since existing HMM training schemes are computationally intensive, we propose an efficient and privacy-preserving federated learning scheme for HMM training to address the efficiency issue in this paper. First, we transform all HMM training computations into matrices- and vectors-based computations over real domains. Then, we introduce our federated HMM training scheme by applying matrix encryption to protect the HMM training privacy. After that, we show that our scheme is privacy-preserving through a rigorous analysis on the security of our scheme. We illustrate that our scheme is efficient through extensive experimental evaluation on the performance of our scheme.

With the rapid development of machine learning, MLaaS has infiltrated into many fields such as image recognition, natural language processing, medical diagnosis, and so on. However, in MLaaS, data interaction between users and service providers is inevitable, and both users' private data and servers' model parameters are at risk of privacy disclosure. In order to solve this problem, homomorphic encryption is an extensively used technique to process private information over ciphertexts. However, since homomorphic encryption only supports linear operations, approximation techniques are required to calculate nonlinear functions, which leads to the loss of prediction accuracy and heavy computation overhead. Therefore, in this paper, we propose a secure neural network prediction scheme combining the trusted execution environment and homomorphic encryption with different security assumptions. Specifically, we first define the security model of TEE-assisted neural network prediction. Then, by combining a lightweight homomorphic encryption technique with TEE, we design secure neural network prediction protocols under different security levels, with which neural network prediction can be securely processed with high performance and accuracy. Finally, we evaluate the performance of our scheme on the MNIST, Fashion-MNIST, and KMNIST datasets, and the results demonstrate that our scheme indeed improves the prediction efficiency and accuracy compared to traditional homomorphic encryption-based schemes with polynomial approximation.

With the popularity of location-based social networks (LBSNs), locations and social relationships have been considered to be important factors in point-of-interest (POI) recommendation services. The boom of cloud computing has driven data owners to outsource the LBSN data and the POI recommendation services to the cloud with powerful computing and storage capabilities. However, as the data usually contains sensitive information, it should be encrypted before being out-sourced, and consequently, the POI recommendation has to be processed over encrypted data. Although several privacy-preserving LBSNs-based POI recommendation schemes have been proposed, they are either inapplicable to the outsourcing scenario or have issues with the recommendation accuracy. Aiming at addressing these issues, in this paper, we propose an efficient and privacy-preserving LBSNs-based POI recommendation scheme (PLPR). Specifically, we first index the users' social relationships with Bloom filters and then organize the user resident location and social relationship dataset into a Vantage Point (VP) tree. Then, we design an efficient LSBNs-based POI recommendation algorithm based on the VP tree. After that, we design a privacy-preserving range determination protocol (PRD) and a privacy-preserving neighbor determination protocol (PND) to respectively protect the privacy of locations and social relationships in the designed algorithm and propose our PLPR scheme. Security analysis shows that our scheme is privacy-preserving, and performance evaluation demonstrates that our scheme is also efficient.

With the development of location based service and online social networking, geo-social-based points of interest (POIs) recommendation has received wide attention, which comprehensively considers the geographic and social factors. The popularity of cloud computing techniques have driven the emerging trend of outsourcing the geo-social-based POI recommendation service to the cloud. However, the cloud server is not fully trusted, leading to the raising concerns of data privacy. Although many privacy-preserving schemes have been proposed for the geo-social-based POI recommendation, they can only return approximate query results. Aiming at addressing this issue, in this paper, we propose an efficient and privacy-preserving geo-social-based POI recommendation scheme, called TRIPE, with accurate query results. Specifically, we first leverage the Quadtree to organize the geographic data and the MinHash method to index the social data. Then, we design a Quadtree-based POI filtering algorithm and a MinHash-based POI filtering algorithm to filter out some POIs that do not meet geo-social POI recommendation threshold. Meanwhile, we employ the BGV homomorphic encryption to protect the privacy of Quadtree-based/MinHash-based POI filtering algorithms and propose our TRIPE scheme based on these algorithms. Security analysis shows that TRIPE is privacy-preserving, and experimental results show that TRIPE is efficient.

Cloud computing's ease of management and on-demand availability features have triggered the boom of outsourcing query services to the cloud. Considering the not-fully trustfulness of the cloud server, the outsourced query services should take the data privacy into consideration, especially when sensitive data are involved. Although many query types have been studied in the context of cloud computing, the range-based reverse k nearest neighbors (RkNN) query is still an unexplored area, which searches data records having any record in the query range as kNN and has wide applications in the taxi dispatching and advertisement placement. As a steppingstone, we propose the first efficient and privacy-preserving range-based RkNN query (PRRkNN) scheme in the cloud. We first design a modified R-tree (MR-tree) to organize the dataset and introduce an efficient MR-tree based RkNN query algorithm to handle RkNN queries with sublinear query efficiency. Then, we propose a sign-preserving matrix encryption (SPME) scheme to privately determine the sign of the scalar product between two records and deploy Paillier homomorphic encryption to introduce a two-party multiplication protocol for secure multiplication between a matrix and a vector. After that, we leverage the SPME scheme and the multiplication protocol to design our PRRkNN scheme. In addition, security analysis shows that our PRRkNN scheme achieves the desired security; and the performance evaluation verifies its efficiency.

With the ubiquity of Internet of Things, location-based service (LBS) providers have collected huge volumes of individuals’ trajectories, which are valuable for some applications, e.g., store location choosing for merchants. However, directly publishing raw trajectories to applications may violate individuals’ data privacy and lead to unexpected loss. Although many trajectory synthesis methods under differential privacy have been proposed to privately publish trajectories data, they cannot sufficiently preserve the semantic information of trajectories. Aiming at this issue, in this article, we introduce a semantic-preserving scheme to synthesize trajectories for publishing under differential privacy. Specifically, we first design a hierarchical graphical model (HGM) to capture the semantic feature of trajectories. Then, we propose a metric, named the correlation score, to measure the relationship between two locations, which can well capture the geographic feature of trajectories. After that, we propose a private trajectory synthesis algorithm by first adding Laplace noises to the extracted features and then synthesizing trajectories based on the noisy features and the Markov chain theory. Privacy analysis demonstrates that our scheme can protect the privacy of trajectories. In addition, performance evaluation illustrates that our synthetic trajectories maintain good utility semantically and geographically.

With the explosive growth of data volume and computing capability, federated learning, which involves constructing global models over multiple data islands, has demonstrated its advantages and vast prospects in the field of machine learning. However, due to commonly vertically partitioned data, coupled with privacy concerns about data leakage, there are still some challenging issues in traditional federated learning. To tackle these challenges, in this article, we propose an efficient and privacy-preserving vertical federated learning framework for logistic regression, named VFLR, where multiple participants can collaboratively perform global model training and query over their vertically partitioned data. Specifically, we first design a data aggregation matrix construction algorithm, with which the vertically partitioned data can be aggregated for high-accuracy global model training. Then, by utilizing a novel symmetric homomorphic encryption, our framework can ensure that the whole training and query processes do not leak any private information. Moreover, based on the data aggregation matrix, multi-round interactions are not required in VFLR, improving training efficiency significantly. Detailed security analysis shows that VFLR can well protect data and model information from inference attacks. In addition, extensive experiments demonstrate that VFLR has high training and query accuracy and low computation and communication overhead.

Over the past decade, genomic data has grown exponentially and is widely used in promising medical and health-related applications, which opens up new opportunities for the field of medicine. Similar patients query (SPQ), which can help physicians formulate an optimal therapy, is one of such popular applications. Despite its popularity, since human genomes are usually highly sensitive, a series of policies have been launched by the government to strictly control its acquisitions and utilization. Thus, how to prevent privacy disclosure becomes of great importance to the flourish of SPQ services. In this article, aiming at the above challenge, we first design a novel genetic BK-tree (GBK-tree) for a genomic database. Then, combined with a random sorting mechanism and some existing encryption techniques, we propose an efficient and privacy-preserving similar patients query scheme over encrypted cloud data, named CASPER. With CASPER, a medical institution can securely outsource its private genomic database to a cloud server, and physicians can request SPQ services from the cloud server while keeping her/his query secret. Detailed security analysis shows that CASPER can preserve privacy in the presence of different threats. Furthermore, extensive performance evaluations demonstrate the high accuracy and efficiency of our proposed scheme.

The advance of cloud computing has driven an emerging trend of outsourcing the rapidly growing data and query services to a powerful cloud for easing the local storage and computing pressure. Meanwhile, when taking data privacy into account, data are usually outsourced to the cloud in an encrypted form. As a result, query services have to be performed over the encrypted data. Among all kinds of query services, the reverse kNN query is highly popular in various applications, such as taxi dispatching and targeted push of multimedia information, but its privacy has not received sufficient attention. To our best knowledge, many existing privacy-preserving reverse kNN query schemes still have some limitations on the query result accuracy, dataset privacy, and flexible support for the choice of the query object and the parameter k. Aiming at addressing these limitations, in this paper, we propose an efficient and privacy-preserving reverse kNN query scheme over encrypted data, named PRkNN. Specifically, we first design a modified M-tree (MM-tree) to index the dataset and further present an MM-Tree based reverse kNN query algorithm in the filter and refinement framework. Then, we leverage the lightweight matrix encryption to carefully design a filter predicate encryption scheme (FPE) and a refinement predicate encryption scheme (RPE); and propose our PRkNN scheme by applying them to protect the privacy of the MM-Tree based reverse kNN query algorithm. Detailed security analysis shows that FPE and RPE schemes are selectively secure, and our PRkNN scheme can preserve both query privacy and dataset privacy. In addition, we conduct extensive experiments to evaluate the performance of our scheme, and the results demonstrate that our scheme is efficient.

Location-based services (LBSs) provide enhanced functionality of mobile applications and convenience for mobile users, which plays a more and more remarkable role in people’s daily life. In LBSs, spatial range query is an essential tool for users to find interesting points in a specific region. However, during spatial range query, it is necessary for data owners and query users to exchange their location data, and the leakage of private location information has drawn significant attention in both governmental and social aspects. Meanwhile, most existing location privacy protection schemes only focus on achieving regular geometry range query over static location datasets. In this paper, we present an efficient and privacy-preserving arbitrary polygon range query scheme, named EPAPRQ. With EPAPRQ, the arbitrary and fine-grained polygon range query can be executed over a dynamic and time-series location dataset with privacy protection. Specifically, in EPAPRQ, an arbitrary polygon range query algorithm is first introduced with sub-range query technique. Then, to protect the private location information of the data owner and query users, a series privacy-preserving data computation protocols are constructed with a symmetric homomorphic encryption algorithm, and a ciphertext-based location dataset updating strategy is also designed. Finally, we propose a double filtration method through combining the quadtree index structure and minimum bounded rectangle, which is able to greatly improve the query efficiency over ciphertexts. Detailed security analysis shows that the sensitive location information in EPAPRQ can be well protected. Furthermore, we evaluate the performance of EPAPRQ in the real map, and the results demonstrate that EPAPRQ is indeed efficient.

Aiming at balancing data privacy and availability, Google introduces the concept of federated learning, which can construct global machine learning models over multiple participants while keeping their raw data localized. However, the exchanged parameters in traditional federated learning may still reveal the data information. Meanwhile, the training data are usually partitioned vertically in real-world scenes, which causes difficulties in model construction. To tackle these problems, in this paper, we propose an efficient and privacy-preserving vertical federated tree boosting framework, namely SGBoost, where multiple participants can collaboratively perform model training and query without staying online all the time. Specifically, we first design secure bucket sharing and best split finding algorithms, with which the global tree model can be constructed over vertically partitioned data; meanwhile, the privacy of training data can be well guaranteed. Then, we design an oblivious query algorithm to utilize the trained model without leaking any query data or results. Moreover, SGBoost does not require multi-round interactions between participants, significantly improving the system efficiency. Detailed security analysis shows that SGBoost can well guarantee the privacy of raw data, weights, buckets, and split information. Extensive experiments demonstrate that SGBoost can achieve high accuracy comparable to centralized training and efficient performance.

The advance of cloud computing has driven a new paradigm of outsourcing large-scale data and data-driven services to public clouds. Due to the increased awareness of privacy protection, many studies have focused on addressing security and privacy issues in outsourced query services. Although many privacy-preserving schemes have been proposed for various query types, the set reverse k nearest neighbors (RkNN) query is still an unexplored area. Even if some existing schemes can be adapted to achieve privacy-preserving set RkNN queries, they will suffer from linear search efficiency. As a steppingstone, in this paper, we propose an efficient and privacy-preserving set RkNN query scheme over encrypted data with sublinear query efficiency. Specifically, we first design an inverted prefix index to organize the set dataset and propose an algorithm to traverse the index with sublinear search efficiency. Then, we propose two oblivious data comparison protocols based on a symmetric homomorphic encryption (SHE) scheme and design the private filter/refinement protocols to preserve the privacy of index searching. After that, we propose an access pattern privacy-preserving set RkNN query scheme by using private filter/refinement protocols. Rigorous security analysis demonstrates that our scheme can protect data privacy and access pattern privacy. Experimental results indicate that our scheme is more efficient than the available naive solution in terms of computational costs and communication overheads.

The boom of cloud computing has stimulated the prevalence of outsourced query services, and privacy concerns further motivate extensive studies on privacy-preserving queries in the cloud. Graph similarity query is one critical query type, in which the similarity between two graphs is usually measured by graph edit distance (GED). Although many schemes have been proposed for GED computation/graph similarity query, they do not consider data privacy and are not applicable to the cloud computing scenario. To address this issue, in this paper, we propose the first efficient and privacy-preserving graph similarity query (PGSim) scheme in the filter and verification framework. Specifically, we first identify the pivot filter property of GED and use the property to design a pivot R-tree based filter algorithm, which can efficiently retrieve candidate graphs for graph similarity query. Then, we design a vertex mapping (VM) tree to index all vertex mappings between two graphs and develop a GED query verification algorithm to verify candidate graphs. After that, we design a suite of private algorithms based on a symmetric homomorphic encryption scheme and apply them to propose a pivot R-tree based filter predicate encryption (PRFilter) scheme and a private GED query verification (PGQVerify) algorithm. Based on the PRFilter scheme and the PGQVerify algorithm, we propose our PGSim scheme. Rigorous security analysis shows that our scheme is selectively secure. Performance evaluation also demonstrates the high efficiency of our scheme.

The aggregated reverse kNN (ARkNN) query aims to identify one query record with the maximum influence set and has become a powerful tool to support optimal decision-making in crowdsensing. Considering data privacy and query privacy, ARkNN queries should be performed in a private manner. Unfortunately, existing schemes cannot support privacy-preserving ARkNN queries over crowd-sensed data. To address this issue, we propose two efficient and privacy-preserving ARkNN query schemes with different security levels, named the BARQ scheme and the EARQ scheme, where the former can only protect data privacy while the latter can protect both data privacy and query privacy. Specifically, we first formalize the models of privacy-preserving ARkNN queries and propose our BARQ scheme based on a random response (RR) frequency oracle. Then, we design a privacy-preserving hardware-assisted reverse kNN query determination (PRkD) scheme for privately determining whether a query record is among the RkNN of a data record. After that, we present our EARQ scheme by leveraging the PRkD scheme to protect query privacy and integrating the RR frequency oracle to protect data privacy. In addition, our rigorous security analysis demonstrates that the BARQ scheme can well protect data privacy, and the EARQ scheme can protect both data privacy and query privacy. Extensive experimental results illustrate that they have high accuracy in query results and are efficient in computational costs and communication overheads.

Reverse k nearest neighbor (RkNN) query has been widely applied in the targeted push of information. Many schemes for the RkNN query on encrypted data have been proposed for coordinating the emerging trend of outsourcing data to the cloud. However, none of them supports the spatial data with many features, a prevalent data type in location-based services, e.g., each user in online dating apps usually has a spatial location and many personality trait features. Meanwhile, incorporating features with the spatial data endows the spatial-feature-based RkNN query to provide more precise services than the spatial-based RkNN query. Therefore, as a steppingstone, we propose an efficient and privacy-preserving spatial-feature-based RkNN scheme in this work for the first time. Specifically, we first design a modified intersection and union R tree (MIUR-tree) to index the spatial and feature data. Then, we introduce an MIUR-tree based RkNN query algorithm in the filter and refinement framework to efficiently process RkNN queries. After that, based on a symmetric homomorphic encryption (SHE) scheme, we design a private filter protocol and a private refinement protocol, and leverage them to propose our RkNN query scheme. Rigorous security analysis demonstrates that our scheme is privacy-preserving, and extensive experiments indicate that our scheme is computationally efficient.

With the rapid advancement in medical imaging techniques, Content-Based (medical) Image Retrieval (CBIR), which can assist in disease diagnosis, has gained much attention in both academia and industry. However, due to patients’ sensitive information involved in medical images, privacy-preserving CBIR is a challenge worth exploiting. Though several privacy-preserving CBIR schemes have been put forth, they can only resist known-background attack (KBA), and do not suffice for protecting the image privacy in outsourced settings. In this article, aiming at the above challenge, we first design a novel Privacy-preserving Mahalanobis Distance Comparison (PMDC) method to improve the accuracy of medical images retrieval. Then, combined with the Mahalanobis distance based Fuzzy C-Means (FCM-M) algorithm, a scheme named TAMMIE is proposed to achieve accurate and privacy-preserving medical image retrieval over encrypted data. With TAMMIE, an image owner can securely outsource the images and indexes to a cloud server, and query users can request retrieval services from the cloud server while keeping their queries private. Detailed security analysis shows that our proposed schemes are secure under the attack stronger than KBA. Furthermore, thorough empirical experiments conducted on two real-world and one synthetic datasets also demonstrate the efficiency of TAMMIE.

The advance of artificial intelligence has stimulated the rapid development of multi-biometric recognition that recognizes users based on their multiple biometric features. Since biometric features are private information of users, multi-biometric recognition has to be processed in a privacy-preserving way. Although many approaches have been proposed for privacy-preserving multi-biometric recognition, they have some limitations in security and performance. Aiming at this challenge, we propose a privacy-preserving multi-biometric recognition scheme. In this scheme, we first utilize a neural network to train a face and voiceprint fusion model, which obtains better fusion layer parameters and improves the accuracy of fusion recognition. Then, we design an efficient and privacy-preserving multi-biometric recognition scheme based on the MK-CKKS cryptosystem in the decentralized model, which does not rely on a trusted third party to increase its universality. The scheme can guarante recognition accuracy by utilizing a feature-level fusion mechanism, reaching EERs as low as 0.66%. Security analysis shows that the privacy of biometric information and recognition results can be guaranteed. Experimental results validates the efficiency of our scheme.

Weighted similarity query has been an essential primitive to enable personalized disease diagnosis in eHealthcare. With the prevalence of cloud computing, a new paradigm is to outsource weighted similarity range query services to the cloud. Meanwhile, the query services are usually processed over encrypted data considering data privacy. Although many existing schemes are available to achieve privacy-preserving weighted similarity query over encrypted data, they have some security and query efficiency drawbacks. This paper addresses this problem by proposing an efficient and privacy-preserving weighted similarity range query scheme. First, we employ a k-d tree to index the outsourced dataset and present a k-d tree based weighted similarity range query algorithm. Then, we propose our scheme by applying the MASPE scheme to protect the privacy of the k-d tree based weighted similarity queries. Privacy preservation of our scheme is proved through security analysis. Efficiency improvement is confirmed by the extensive experiments that indicate that our scheme improves 7 x query efficiency than the state-of-the-art scheme.

With AI-derived knowledge graph (KG), medical centers can recommend appropriate treatment options to physicians as references based on their patients' personal healthcare information (PHI). However, the treatment recommendation services may also cause serious privacy concerns. In this paper, we propose an efficient and privacy-preserving personalized treatment recommendation scheme with the aid of a third-party server. Specifically, a medical center denotes the KG of each disease by a directed graph with conditional edges and vertices that describe the treatment options and costs in different states. To prevent privacy leakage while reducing computational and management cost, the graphs are encrypted and then delegated to an honest-but-curious server. With the assistance of the server, physicians can set proper illness states and cost requirements according to patients' PHI, and correspondingly generate personalized ciphertexts to retrieve appropriate treatment options. The key component of the proposed scheme is a novel designed secure and flexible path comparison protocol, by tailoring a symmetric homomorphic encryption algorithm and combining it with a secure hash function. The protocol can enable the server to compare the uploaded ciphertexts with encrypted graphs in a secure and efficient way. Comprehensive security analysis indicates that the proposed scheme can meet desirable privacy requirements, and extensive experimental results demonstrate its practicality.

As a straightforward consequence of advances in the Internet of Things (IoT), location-based service (LBS) applications have been pervasive in our daily lives. Nevertheless, since those LBS applications will continuously collect and disclose users’ location data, major concerns on privacy leakage are raised. Aiming at the challenge, in this article, we first build up a detect module (DM) and employ it to investigate more than 80% of LBS applications are keen on tracking users. Then, to thwart the threats from those LBS applications, we exploit the deceptive dummy techniques and design a dummy-based location privacy preserving scheme, named dummy location provider (DLP), which comprises three algorithms, namely, Spread, Shift, and Switch. Specifically, Spread and Shift are in charge of generating deceptive dummies and trajectories. And with Switch, users’ real locations are replaced with dummy trajectories before being submitted to LBS applications. As a result, users can not only prevent applications from accessing location data arbitrarily, but also avoid being questioned by applications in terms of honesty. Furthermore, to guarantee necessary functions of LBS, DLP offers customizable privacy-preserving strategies for users, which can achieve flexible location data usage control. Finally, our DLP can also attain achievable and effortless deployment over smart devices. Detailed security analysis indicates that DLP resists inference attacks even facing skeptical applications. In addition, for performance evaluation, a DLP application (DLPA) is developed on the Android platform and tested in the real environment, and the extensive experimental results demonstrate that the DLPA is indeed effective and high efficiency in practice.

With the popularity of intelligent terminals and the advances of mobile Internet, carpooling service, which reduces the travel cost of each user by allowing multiple users to share one car, has received considerable attention and makes our life more convenient. However, the vigorous development of carpooling services still faces severe challenges in users’ location or route privacy. In this article, we propose an efficient and privacy-preserving route matching scheme called TAROT for carpooling services. With TAROT, users can enjoy high-quality carpooling services while without revealing sensitive location and route information. Specifically, based on a Goldwasser–Micali-based equality determination algorithm (GMEDA), we design an accurate similarity computation algorithm (ASCA), which allows users to get accurate carpooling results over ciphertexts. Meanwhile, the reverse Minhash (RM) method is also designed to construct a dissimilar route filter algorithm (DRFA), which can filter out dissimilar routes in advance and reduce computational costs and communication overheads. Security analysis shows that TAROT can protect users’ location privacy. In addition, TAROT is also evaluated with many random maps, and the simulation results demonstrate that TAROT is highly efficient.

Mobile crowdsensing (MCS) is a newly emerged sensing paradigm, where a large group of mobile workers collectively sense and share data for real-time services. However, one major problem that hinders the further development of MCS is the potential leakage of workers’ data privacy. In this article, we integrate federated learning (FL) with MCS and introduce a novel sensing system, called federated MCS (F-MCS). In F-MCS, the workers can optimize the global model while keeping all the sensitive training data locally, thus ensuring their data privacy. Nevertheless, there are still two major issues in F-MCS. The first issue is that in F-MCS services, the workers are heterogeneous in terms of computational capacities and data resources. Hence, qualified workers should be appropriately selected to improve the efficiency of the training process. The second issue is that F-MCS is across-deviceFL system, where the platform will finally get the global model after multiple training rounds. However, most privacy-preserving techniques are designed forcross-siloFL platforms, which cannot be applied to real-world F-MCS scenarios. To tackle the above problems, in this article, we propose a privacy-preserving scheme for F-MCS, namely, FedSky. Mainly, by extending the classic FedAvg algorithm, FedSky selects qualified workers based on the constrained group skyline (CG-skyline) and securely aggregates model updates based on the homomorphic encryption technique. Comprehensive security analysis demonstrates the privacy preservation of FedSky. Extensive experiments are conducted on an image classification task, where the comparison results validate the proposed scheme’s efficiency and effectiveness.

Healthcare data explosion and cloud computing booming have motivated healthcare centers to outsource their healthcare data and data-driven services to a powerful cloud. Nevertheless, due to privacy concerns, the data are usually encrypted before being outsourced, which will degrade the data utility and make it challenging to implement data-driven services. Although the multidimensional range query over encrypted data, as one of the most popular outsourced services in eHealthcare, has been extensively studied, existing solutions still have some limitations in efficiency, privacy, and practicality. Aiming at this challenge, in this article, we design an efficient and privacy-preserving multidimensional range query (PMRQ) scheme. We first build an R-tree to index the data set and reduce the R-tree-based range queries to the multidimensional range intersection problem. Then, by delicately designing a data comparison algorithm and a homomorphic encoding technique, we present an encoding-based range intersection algorithm. After that, by employing matrix encryption to protect the privacy of the encoding-based range intersection algorithm, we design a multidimensional range intersection predicate encryption (MRIPE) scheme. Based on the MRIPE scheme, we then propose our PMRQ scheme. A detailed security analysis illustrates that our PMRQ scheme is privacy preserving, and experimental results demonstrate that it is computationally efficient.

The thriving of the Internet of Things (IoT) has become the enabler of smart eHealthcare, which greatly benefits patients by providing various data-driven healthcare monitoring services. Among those promising services, the time-series activities-based healthcare monitoring service is highly regarded due to its popularity. Meanwhile, with the rapidly growing volume of healthcare data, an emerging trend is to outsource the time-series activities-based healthcare monitoring models and the corresponding services to a cloud, which, however, inevitably entails privacy concerns. Although many existing works have put forth some solutions for privacy-preserving time-series activities-based healthcare monitoring, they are not applicable to the outsourced scenario with a single-server setting. To address the challenge, in this article, we propose an efficient and privacy-preserving forward algorithm (PPFA) and further apply PPFA to construct a remote healthcare monitoring scheme over the cloud. To the best of our knowledge, our PPFA is the first privacy-preserving forward algorithm over cloud while without any accuracy loss. In addition, our remote healthcare monitoring scheme is also the first privacy-preserving hidden Markov model-based healthcare monitoring scheme in the single-server setting. Detailed security analysis shows that our PPFA and healthcare monitoring scheme are indeed privacy preserving. In addition, extensive simulations are conducted, and the results also demonstrate their efficiencies.

It is believed that online disease risk assessment system has great potential to alleviate the medical treatment problems for the future smart city and communities, as it can excavate disease risk factors from a large number of patient features, provide diagnostic references for doctors, and save medical treatment time for patients. However, the flourish of online disease risk assessment service still faces severe challenges including information privacy and security. In this article, based on the naïve Bayesian classification, we propose an efficient and privacy-preserving diseaserisk assessment scheme over multi-outsourced vertical datasets, named CARER. With CARER, the e-healthcare provider can securely train a disease risk predication model over vertically distributed medical data from multiple medical centers (i.e., hospitals), and provide privacy-preserving disease risk predication services for users (i.e., patients and doctors). During the model training and disease risk prediction phases, all sensitive data are operated over ciphertexts without decryption. As a result, the private information of medical centers, e-healthcare provider, and users can be well protected. Detailed security analysis shows that CARER can resist various known security threats. In addition, we evaluate the performance of CARER with real medical datasets, and the results demonstrate that CARER is efficient.

Similarity query over time series data plays a significant role in various applications, such as signal processing, speech recognition, and disease diagnosis. Meanwhile, driven by the reliable and flexible cloud services, encrypted time series data are often outsourced to the cloud, and as a result, the similarity query over encrypted time series data has recently attracted considerable attention. Nevertheless, existing solutions still have issues in supporting similarity queries over time series data with different lengths, query accuracy and query efficiency. To address these issues, in this article, we propose a new efficient and privacy-preserving similarity range query scheme, where the time warp edit distance (TWED) is used as the similarity metric. Specifically, we first organize time series data into a$k$d-tree by leveraging TWED’s triangle inequality, and design an efficient similarity range query algorithm for the$k$d-tree. Second, based on a symmetric homomorphic encryption technique, we carefully devise a suite of privacy-preserving protocols to provide a security guarantee for$k$d-tree based similarity range queries. After that, by using the similarity range query algorithm and these protocols, we propose our privacy-preserving similarity range query scheme, in which we elaborate on two strategies to make our scheme resist against the cloud inference attack. Finally, we analyze the security of our scheme and conduct extensive experiments to evaluate its performance, and the results indicate that our proposed scheme is indeed privacy-preserving and efficient.

Set similarity search, aiming to search the similar sets to a query set, has wide application in today's recommendation services. Meanwhile, the rapid advance in cloud technique has promoted the boom of data outsourcing. However, since the cloud is not fully trustable and the data may be sensitive, data should be encrypted before outsourced to the cloud. Undoubtedly, data encryption will hinder some basic functionalities, e.g., set similarity search. For achieving set similarity search over encrypted data, many solutions were proposed, yet they either only satisfy weak security requirements, or only achieve approximate similarity, or have low efficiency or under the model of two cloud servers. Therefore, in this article, we propose a new efficient and privacy-preserving exact set similarity search scheme under a single cloud server. Specifically, we first design a symmetric-key predicate encryption (SPE-Sim) scheme, which can support similarity search over binary vectors. Then, we represent the set records to be binary vectors and employ the B+ tree to build an index for them. After that, based on SPE-Sim and the B+ tree-based index, we propose our scheme and it can achieve efficient set similarity search while preserving the privacy of set records and query contents. Finally, security analysis and performance evaluation indicate that our scheme is privacy-preserving and efficient.

It is undeniable that Internet of Things (IoT) in big data era can provide us with huge volumes of multi-dimensional data, transforming our society into a much more intelligent one. In order to fit for the multi-dimensional data processing in big data era, multi-dimensional range queries, especially over cloud platform, have received considerable attention in recent years. However, as the cloud server is not fully trustable, designing multi-dimensional range queries over encrypted data becomes a research trend, and many solutions have been proposed in the literature. Nevertheless, most existing solutions suffer from the leakage of the single-dimensional privacy, and such leakage would severely put the data at risk. Although a few existing works have addressed the problem of single-dimensional privacy, they are impractical in some real scenarios due to the issues of inefficiency, inaccuracy, and two-cloud-server requirement. Aiming at solving these issues, in this article, we propose a practical and privacy-preserving multi-dimensional range query (PRQ) scheme. Specifically, in our proposed PRQ scheme, we first index the multi-dimensional dataset with an R-tree and reduce R-tree based range queries to the problem of point intersection and range intersection. Then, by employing the lightweight matrix encryption technique, we design two novel algorithms for PRQ, i.e., multi-dimensional point intersection predicate encryption (PIPE) and multi-dimensional range intersection predicate encryption (RIPE), which can preserve the privacy of the proposed point intersection algorithm and range intersection algorithm, and further preserve the single-dimensional privacy of the proposed PRQ scheme. Detailed security analysis shows that our proposed PRQ scheme is indeed privacy-preserving. In addition, extensive simulations are conducted, and the results also demonstrate its efficiency.

Over the past years, the increasingly severe data island problem has spawned an emerging distributed deep learning framework—federated learning, in which the global model can be constructed over multiple participants without directly sharing their raw data. Despite its promising prospect, there are still many security challenges in federated learning, such as privacy preservation and integrity verification. Furthermore, federated learning is usually performed with the assistance of a center, which is prone to cause trust worries and communicational bottlenecks. To tackle these challenges, in this paper, we propose a privacy-preserving and verifiable decentralized federated learning framework, named PVD-FL, which can achieve secure deep learning model training under a decentralized architecture. Specifically, we first design an efficient and verifiable cipher-based matrix multiplication (EVCM) algorithm to execute the most basic calculation in deep learning. Then, by employing EVCM, we design a suite of decentralized algorithms to construct the PVD-FL framework, which ensures the confidentiality of both global model and local update and the verification of every training step. Detailed security analysis shows that PVD-FL can well protect privacy against various inference attacks and guarantee training integrity. In addition, the extensive experiments on real-world datasets also demonstrate that PVD-FL can achieve lossless accuracy and practical performance.

Similarity queries, giving a way to disease diagnosis based on similar patients, have wide applications in eHealthcare and are essentially demanded to be processed under fine-grained access policies due to the high sensitivity of healthcare data. One efficient and flexible way to implement such queries is to outsource healthcare data and the corresponding query services to a powerful cloud. Nevertheless, considering data privacy, healthcare data are usually outsourced in an encrypted form and required to be accessed in a privacy-preserving way. In the past years, many schemes have been proposed for privacy-preserving similarity queries. However, none of them is applicable to achieve data access control and access pattern privacy preservation. Aiming at this challenge, we propose an efficient and access pattern privacy-preserving similarity range query scheme with access control (named EPSim-AC). In our proposed scheme, we first design a novel tree structure, called$k$-d-PB tree, to index healthcare data and introduce an efficient$k$-d-PB tree based similarity query algorithm with access control. Second, to balance the search efficiency and access pattern privacy of$k$-d-PB tree, we also define a weakened access pattern privacy, called$k$-d-PB tree’s$\beta $-access pattern unlinkability. After that, we preserve the privacy of$k$-d-PB tree based similarity queries with access control through a symmetric homomorphic encryption scheme and present our detailed EPSim-AC scheme. Finally, we analyze the security of our scheme and also conduct extensive experiments to evaluate its performance. The results demonstrate that our scheme can guarantee$k$-d-PB tree’s$\beta $-access pattern unlinkability and has high efficiency.

Set containment search, which aims to retrieve all set records containing a specific query set, has received considerable attention. Meanwhile, due to the dramatic growth of data, data owners tend to outsource their data to the cloud and deploy the cloud server to offer the set containment search services. However, as the cloud server is not fully trustable and the data may be sensitive, a straightforward strategy for the data owners is to encrypt the data before outsourcing them. Although the encryption technique can preserve data privacy, it inevitably hinders the functionality of set containment search. Many existing studies on the set containment search over outsourced data still suffer from the search efficiency and security issues. In this article, aiming at the above issues, we propose an efficient and privacy-preserving set containment search scheme. Specifically, we first deploy an asymmetric scalar-product-preserving encryption technique to design a set containment/intersection encryption (SCIE-Enc) scheme. Then, we build a radix tree to represent the set records. Based on the radix tree and SCIE-Enc construction, we present our scheme that can achieve efficient set containment search while preserving the privacy of set records, query sets, and query results, as indicated in our security analysis and performance evaluation.

As a consequence of advance in the Internet of Things (IoT) and big data technology, smart eHealthcare has emerged and greatly enabled patients to enjoy high-quality healthcare services in disease prediction, clinical decision making and healthcare surveillance. Meanwhile, in order to support the dramatic increase of healthcare data, healthcare centers often outsource the on-premises data to a powerful cloud and deploy the cloud server to manage the data. However, since the healthcare data usually contain some sensitive information and also the cloud server is not fully trusted, healthcare centers need to encrypt the data before outsourcing them to the cloud. Unfortunately, data encryption inevitably hinders some advanced applications of the data like the similarity range query in cloud. Although many studies on similarity range query over encrypted data have been reported, most of them still have some limitations in security, efficiency and practicality. Aiming at this challenge, in this article, we propose a new efficient privacy-preserving similarity range query (EPSim) scheme. Specifically, we first present a modified asymmetric scalar-product-preserving encryption (ASPE) scheme and prove it is selectively secure. Then, we introduce a Quadsector tree to represent the data, and employ a filtration condition to design an efficient algorithm for efficient similarity range queries over the Quadsector tree. Finally, we propose our EPSim scheme by integrating the modified ASPE scheme and Quadsector tree. Detailed security analysis indicates that our proposed EPSim scheme is really secure. In addition, extensive performance evaluations are conducted, and the results also demonstrate it is efficient and practical.

Dynamic symmetric searchable encryption (SSE), which enables a data user to securely search and dynamically update the encrypted documents stored in a semi-trusted cloud server, has received considerable attention in recent years. However, the search and update operations in many previously reported SSE schemes will bring some additional privacy leakages, e.g., search pattern privacy, forward privacy and backward privacy. To the best of our knowledge, none of the existing dynamic SSE schemes preserves the search pattern privacy, and many backward private SSE schemes still leak some critical information, e.g., the identifiers containing a specific keyword currently in the database. Therefore, aiming at the above challenges, in this article, we design a practical SSE scheme, which not only supports the search pattern privacy but also enhances the backward privacy. Specifically, we first leverage the$k$-anonymity and encryption to design an obfuscating technique. Then, based on the obfuscating technique, pseudorandom function and pseudorandom generator, we design a basic dynamic SSE scheme to support single keyword queries and simultaneously achieve search pattern privacy and enhanced backward privacy. Furthermore, we also extend our proposed scheme to support more efficient boolean queries. Security analysis demonstrates that our proposed scheme can achieve the desired privacy properties, and the extensive performance evaluations also show that our proposed scheme is indeed efficient in terms of communication overhead and computational cost.

Worker selection is always one of the most fundamental problems in mobile crowdsensing (MCS), since the reliability of workers' sensing data is hugely significant to the service quality. In the worker selection process, it is inevitable for the workers to share some of their sensitive information. Consequently, numerous studies are conducted on the problem of privacy-preserving worker selection in MCS platforms. However, most of the existing methods focus on static and short-term situations. As a result, they are inapplicable to the highly dynamic environments where the MCS tasks are long term and the workers can continuously arrive at/leave the system. To solve these problems, in this article, we propose a privacy-preserving worker selection scheme based on the probabilistic skyline over sliding windows. Specifically, the proposed scheme can select reliable workers for each current sliding window in terms of working experience, expiry time, and trustability. Besides, we design an ElGamal encryption-based scheme for securely outsourcing and comparing workers' personal information without revealing their privacy. Detailed security analysis shows that the workers' sensitive information, e.g., working experience and trustability, are not revealed to any authorized parties during the process of MCS under our security model. Furthermore, extensive experiments on both real-world and simulated data sets demonstrate that our proposed scheme outperforms the baseline method in two application scenarios, i.e., 1) continuous worker arrival and 2) continuous worker departure.

With the pervasiveness of mobile devices and the development of biometric technology, biometric identification, which can achieve individual authentication relies on personal biological or behavioral characteristics, has attracted widely considerable interest. However, privacy issues of biometric data bring out increasing concerns due to the highly sensitivity of biometric data. Aiming at this challenge, in this paper, we present a novel privacy-preserving online fingerprint authentication scheme, named e-Finga, over encrypted outsourced data. In the proposed e-Finga scheme, the user's fingerprint registered in trust authority can be outsourced to different servers with user's authorization, and secure, accurate and efficient authentication service can be provided without the leakage of fingerprint information. Specifically, an improved homomorphic encryption technology for secure euclidean distance calculation to achieve an efficient online fingerprint matching algorithm over encrypted FingerCode data in the outsourcing scenarios. Through detailed security analysis, we show that e-Finga can resist various security threats. In addition, we implement e-Finga over a workstation with a real fingerprint database, and extensive simulation results demonstrate that the proposed e-Finga scheme can serve efficient and accurate online fingerprint authentication.

The rapid advance of the Internet of Things (IoT) has enabled a new paradigm of the sensing network, i.e., mobile crowdsensing (MCS). Primarily, in MCS systems, a crowd of participating mobile users, namely, workers, are allocated by the MCS platforms to outsource their sensory data for specific tasks. Obviously, the reliability of workers and the trustability of their sensing data play significant roles in the service quality, thus the worker selection becomes crucial for the success of MCS applications. However, due to either a large number of candidates or their dynamic natures, selecting reliable workers poses big challenges to the MCS platform. Evidently, workers' reputation-based characteristics, such as trustability and credibility, are also pivotal for the worker selection in MCS, but they were often neglected in previous literature. In this article, aiming at addressing the above challenges, we propose a new privacy-preserving worker selection scheme based on the probabilistic skyline computation technique. Specifically, our proposed scheme is characterized by: 1) assigning a trustability score to each worker based on his/her past performance without revealing his/her sensitive information and 2) efficiently selecting a subset of reliable workers for a particular task. Detailed security analysis shows that our proposed scheme can preserve workers' privacy. In addition, performance evaluations via extensive simulations are conducted, and the results also demonstrate its effectiveness and efficiency for reliable worker selection in MCS applications.

Speaker verification has played a significant role in authentication with the booming development of smartphones and intelligent terminals in recent years. However, most speaker verification systems directly store the users original voiceprint template data (or called acoustic features). In this paper, we reveal the insecurity and sensitiveness of voiceprint template data by carrying out spoofing attacks on speaker verification systems using genetic algorithm. Meanwhile, multiple generation models based on different genetic algorithms (standard genetic algorithm, multiple population genetic algorithm) are proposed, but also the effects of these generation models are compared. Moreover, experimental results on state-of-the-art text-independent speaker verification techniques (such as i-vector, GMM-UBM) clearly demonstrate that our generated attack voice with leaked voiceprint template data can completely imitate users and pass the speaker verification.

Online medical primary diagnosis system, which can provide convenient medical decision support through applying mobile communication and data analysis technology, has been considered as a promising approach to improve the quality of healthcare service. However, it still faces many severe challenges on the privacy of users' health information and the accuracy of diagnosis result, which deter the wide adoption of online medical primary diagnosis system. In this paper, we propose an efficient and privacy-preserving online medical primary diagnosis (CINEMA) framework. Within CINEMA framework, users can access online medical primary diagnosing service accurately without divulging their medical data. Specifically, based on fast secure permutation and comparison technique, the encrypted user's query is directly operated at the service provider (SP) without decryption, and the diagnosis result can only be decrypted by the user, meanwhile, the diagnosis model in SP can also be protected. Through extensive analysis, we show that CINEMA can ensure that user's health information and healthcare SP's diagnosis model are kept confidential, and has significantly reduce computation and communication overhead. In addition, performance evaluations via implementing CINEMA demonstrate its effectiveness in term of the real environment.

High Efficiency Video Coding (HEVC) encryption, which has been proposed to encrypt intra prediction modes (structural information), transform coefficients (texture information), and motion related codewords (motion information), has received considerable attention recently. However, there is still the issue of efficiency when HEVC encryption is applied in the Internet of Multimedia Things (IoMT). Aiming at this challenge, in this paper, we propose a new low-overhead HEVC encryption scheme for energy-constrained IoMT. Concretely, the proposed scheme adjusts the selection of the aforementioned syntax elements to be encrypted according to the structure, texture, and motion energy present in each frame. It works as follows. The energy levels of quantized coefficients and motion vectors are calculated and compared with adaptive threshold values to classify the energy level in each video frame. When there is a high energy frame in the video, all the syntax elements are encrypted. When there is a low energy frame, alternate syntax elements are encrypted for achieving low encryption overhead. Moreover, in the case of transform coefficients, to withstand the interpolation attack, alternate coefficients are encrypted after correlating the frame with its neighboring coefficients. Extensive experiments were conducted, and the results demonstrate that the proposed scheme efficiently reduces the encryption overhead with low impact on the security level, making it suitable for IoMT.

With the development of virtualization technology and fast expansion of network-scale, SDN has been employed in various cases from campus networks to cloud data center networks. However, SDN networks are also facing some new security issues, relative to the traditional networks. In this work, we demonstrate a novel network isolation attack in SDN networks, called Network Harvesting, that lets an attacker can access to the user's network privileges without the awareness of victim and OpenFlow SDN architecture, which significantly increases persistence. We then present a defense, SpoofDefender, that prevents network isolation attacks or other spoofing attacks by leveraging SDN's data and control plane separation, global network view, and programmatic control of the network, while building upon IEEE 802.1x and encryption. In addition, we also implement SpoofDefender on ONOS 1.10.4 and Mininet with a real network, and extensive simulation results demonstrate that our proposed SpoofDefender is highly effective in terms of computation and communication costs.

Online medical primary diagnosis system, which can provide the pre-diagnosis service anywhere anytime, has attracted considerable interest. However, the flourish of online medical primary diagnosis system still faces many serious challenges since the sensitivity of personal health information and service provider''s diagnosis model. In this paper, we propose an efficient and privacy-preserving medical primary diagnosis scheme based on k-nearest-neighbors classification (kNN), called EPDK. With EPDK, medical users can ensure that their sensitive health information are not compromised during the online medical diagnosis process, and service provider can provide high-accuracy service without revealing its diagnosis model. Specifically, based on lightweight multiparty random masking and polynomial aggregation techniques, a medical user preprocesses her/his query vector before sending out and the preprocessed vector is directly operated in the service provider without obtaining original data, meanwhile, the primary diagnosis result cannot be achieved by anyone except the medical user. Through extensive analysis, we show that EPDK can resist multifarious known security threats, and has significantly lower computation complexity than existing schemes. Moreover, performance evaluations via implementing EPDK in the real environment demonstrate that EPDK is highly efficient in terms of computation overhead.

With the pervasiveness of location-aware mobile terminals and the popularity of social applications, location-based social networking service (LBSNS) has brought great convenience to people's life. Meanwhile, proximity detection, which makes LBSNS more flexible, has aroused widespread concern. However, the prosperity of LBSNS still faces many severe challenges on account of users' location privacy and data security. In this paper, we propose two efficient and privacy-preserving proximity detection schemes, named arbitrary geometric range query for polygons (AGRQ-P) and arbitrary geometric range query for circles (AGRQ-C), for location-based social applications. With proposed schemes, a user can choose any area on the map, and query whether her/his friends are within the region without divulging the query information to both social application servers and other users, meanwhile, the accurate locations of her/his friends are also confidential for the servers and the query user. Specifically, with algorithms based on ciphertext of geometric range query, users' query and location information is blurred into ciphertext in client, thus no one but the user knows her/his own sensitive information. Detailed security analysis shows that various security threats can be defended. In addition, the proposed schemes are implemented in an IM APP with a real LBS dataset, and extensive simulation results over smart phones further demonstrate that AGRQ-P and AGRQ-C are highly efficient and can be implemented effectively.

With the development of cloud computing technology and the proliferation of the Internet of Things (IoT) terminals, more and more scenes need the collaboration of virtual machines and IoT terminals to resolve. However, there are many severe challenges on the security of virtual machines and IoT terminals. Based on Bell‐LaPadula Model (BLP), a task‐oriented multilevel cooperative access control scheme virtualization and reality BLP, named VR‐BLP, is proposed. Specifically, tasks are created for each user of the platform and tasks and users are divided into multiple levels to provide more granularities to limit access between virtual machines and IoT terminals. Moreover, with network isolation cooperating with process isolation and shared memory isolation mechanisms, VR‐BLP is implemented to enhance the security isolations between tasks. Performance evaluations show that VR‐BLP enhanced the security of environment with virtualization and IoT without causing significant performance penalty.

With the pervasiveness of mobile devices and the development of biometric technology, biometric identification, which can achieve individual authentication relies on personal biological or behavioral characteristics, has attracted widely considerable interest. However, privacy issues of biometric data bring out increasing concerns due to the highly sensitivity of biometric data. Aiming at this challenge, in this paper, we present a novel privacy-preserving online fingerprint authentication scheme, named e-Finga, over encrypted outsourced data. In the proposed e-Finga scheme, the user's fingerprint registered in trust authority can be outsourced to different servers with user's authorization, and secure, accurate and efficient authentication service can be provided without the leakage of fingerprint information. Specifically, an improved homomorphic encryption technology is introduced to achieve an efficient online fingerprint matching algorithm over encrypted FingerCode data in the outsourcing scenarios. Through detailed analysis, we show that e-Finga can resist various security threats and serve efficient and accurate online fingerprint authentication.

Internet of Things is expanding the network by integrating huge amount of surrounding objects which requires the secure and reliable transmission of the high volume data generation, and the mobile relay technique is one of the efficient ways to meet the on-board data explosion in LTE-Advanced (LTE-A) networks. However, the practice of the mobile relay will pose potential threats to the information security during the handover process. Therefore, to address this challenge, in this paper, we propose a secure handover session key management scheme via mobile relay in LTE-A networks. Specifically, in the proposed scheme, to achieve forward and backward key separations, the session key shared between the on-board user equipment (UE) and the connected donor evolved node B (DeNB) is first generated by the on-board UE and then securely distributed to the DeNB. Furthermore, to reduce the communication overhead and the computational complexity, a novel proxy re-encryption technique is employed, where the session keys initially encrypted with the public key of the mobility management entity (MME) will be re-encrypted by a mobile relay node (MRN), so that other DeNBs can later decrypt the session keys with their own private keys while without the direct involvement of the MME. Detailed security analysis shows that the proposed scheme can successfully establish session keys between the on-board UEs and their connected DeNB, achieving backward and forward key separations, and resisting against the collusion between the MRN and the DeNB as the same time. In addition, performance evaluations via extensive simulations are carried out to demonstrate the efficiency and effectiveness of the proposed scheme.

With the pervasiveness of mobile devices and the development of wireless communication technique, location-based services (LBSs) have made our life more convenient, and the polygons spatial query, which can provide more flexible LBS, has attracted considerable interest recently. However, the flourish of polygons spatial query still faces many challenges including the query information privacy. In this paper, we present an efficient and privacy-preserving polygons spatial query framework for location-based services, called Polaris. With Polaris, the LBS provider (LP) outsources the encrypted LBS data to cloud server (CS), and the registered user can query any polygon range to get accurate LBS results without divulging his/her query information to the LP and CS. Specifically, an efficient special polygons spatial query (SPSQ) algorithm over ciphertext is constructed, based on an improved homomorphic encryption technology over composite order group. With SPSQ, Polaris can search outsourced encrypted LBS data in CS by the encrypted request, and respond the encrypted polygons spatial query results accurately. Detailed security analysis shows that the proposed Polaris can resist various known security threats. In addition, performance evaluations via implementing Polaris on smartphone and workstation with real LBS dataset demonstrate Polaris' effectiveness in term of real environment.

With the advances of machine learning algorithms and the pervasiveness of network terminals, the online medical prediagnosis system, which can provide the diagnosis of healthcare provider anywhere anytime, has attracted considerable interest recently. However, the flourish of online medical prediagnosis system still faces many challenges including information security and privacy preservation. In this paper, we propose an e fficient and privacy-preserving online medical prediagnosis framework, called eDiag, by using nonlinear kernel support vector machine (SVM). With eDiag, the sensitive personal health information can be processed without privacy disclosure during online prediagnosis service. Specifically, based on an improved expression for the nonlinear SVM, an efficient and privacy-preserving classification scheme is introduced with lightweight multiparty random masking and polynomial aggregation techniques. The encrypted user query is directly operated at the service provider without decryption, and the diagnosis result can only be decrypted by user. Through extensive analysis, we show that eDiag can ensure that users' health information and healthcare provider's prediction model are kept confidential, and has significantly less computation and communication overhead than existing schemes. In addition, performance evaluations via implementing eDiag on smartphone and computer demonstrate eDiag's effectiveness in term of real online environment.

With the evolving of ehealthcare industry, electronic health records (EHRs), as one of the digital health records stored and managed by patients, have been regarded to provide more benefits. With the EHRs, patients can conveniently share health records with doctors and build up a complete picture of their health. However, due to the sensitivity of EHRs, how to guarantee the security and privacy of EHRs becomes one of the most important issues concerned by patients. To tackle these privacy challenges such as how to make a fine-grained access control on the shared EHRs, how to keep the confidentiality of EHRs stored in cloud, how to audit EHRs and how to find the suitable doctors for patients, in this paper, we propose a fine-grained EHRs sharing scheme via similarity-based recommendation accelerated by Locality Sensitive Hashing (LSH) in cloud-assisted ehealthcare system, called FSSR. Specifically, our proposed scheme allows patients to securely share their EHRs with some suitable doctors under fine-grained privacy access control. Detailed security analysis confirms its security prosperities. In addition, extensive simulations by developing a prototype of FSSR are also conducted, and the performance evaluations demonstrate the FSSR's effectiveness in terms of computational cost, storage and communication cost while minimizing the privacy disclosure.

With the development of intelligent transport systems (ITS) and vehicular ad hoc network (VANET), vehicular cloud computing (VCC) has been proposed to bring essential and potential benefits, such as improving traffic safety and offering computational services to road users. To make such computational services reliable and secure, the computation results from the vehicular cloud (VC) should be verifiable and the trustworthy vehicles need to be selected to form the VC with disclosure-minimizing privacy. To address these challenges, a privacy-preserving trust-based verifiable vehicular cloud computing scheme has been proposed in this paper, named PTVC. Specifically, the proposed PTVC scheme integrates the unique features of VCC and the requirements of privacy into traditional reputation system based on beta distribution, which can help differentiate the trust levels of the vehicles and preserve location privacy in the meantime. Moreover, by using the verifiable techniques, the cloud users can verify the correctness of outsourced computation while guaranteeing the privacy of their outsourced data. Detailed security analysis shows that the proposed PTVC scheme is secure and robust against several sophisticated attacks. In addition, performance evaluations via extensive simulations are also conducted, demonstrating its effectiveness.

Vehicular cloud, which is constituted by gathering the under-utilized on-board capabilities on the road, has received considerable attention in recent years. In this paper, we propose a novel secure and privacy-preserving incentive mechanism in vehicular cloud, which employs the Stackelberg Game to model the interaction between the leader and follower vehicles. With the proposed incentive mechanism, the leader vehicle which represents the task announcement server can select competent follower vehicles to collaborate for the announced task, and the selected follower vehicles can earn payments from participating in and completing the announced tasks. By exploiting the group signature technique, the leader and follower vehicles can achieve mutual verification with each other without privacy-related information disclosure. To show the efficiency of the proposed scheme, numerical analysis are conducted, and the derived results demonstrate that the proposed incentive mechanism can bring benefits to both parties, in terms of the utilities of the involved vehicles.

With the ubiquity of mobile devices, location-based social networking applications have been widely used in people's daily life. However, due to the importance and sensitivity of location information, these applications may lead to serious security issues for user's location privacy. To handle these location privacy challenges, in this paper, we propose an efficient and privacy-preserving proximity testing scheme, called EPPD, for location-based services. With EPPD, a group of users can test whether they are within a given distance with minimal privacy disclosure. In specific, EPPD is comprised of two phases: first, users periodically upload their encrypted locations to service provider; and later, users can send requests to service provider for proximity testing and obtain the final testing results. Detailed security analysis shows that EPPD can achieve privacy-preserving proximity testing. In addition, performance evaluations via extensive simulations also demonstrate the efficiency and effectiveness of EPPD in term of low computational cost and communication overhead.

Abstract Support vector machine (SVM) classification, which can handle large data sets in high dimensional spaces, has been widely applied in numerous settings nowadays, such as genetic match, spam detection, and financial prediction. However, because of the data's sensitivity and classifiers' confidentiality, how to provide a privacy‐preserving SVM classification has attracted considerable interest recently. Aiming at these privacy challenges, in this paper, we present an efficient and privacy‐preserving classification service query framework, named EPCS, for the linear kernel SVM classifier. Specifically, based on lightweight multiparty random masking and polynomial aggregation techniques, EPCS preserves the privacy of users' data and SVM classifier efficiently during the process of user query. Through detailed security analysis, we show that EPCS can resist various security threats. In addition, we implement EPCS in Java and evaluate EPCS over a smart phone and a PC, and extensive simulation results demonstrate that the EPCS framework can indeed achieve high efficiency and effectiveness in SVM classification. Copyright © 2016 John Wiley & Sons, Ltd.

Social networking service (SNS) is one of the major technological applications based on Web 2.0, which can help users to express their views and share information with others. How to describe the information diffusion process in online SNS accurately has attracted considerable interest recently. However, almost all existing models focus on a single online SNS tool and face many challenges with multiple online SNS tools. In this paper, we turn to users’ privacy setting policies and propose a general stochastic model with multiple diffusion mechanisms in online SNS, called DMPS. Specifically, we first define a privacy protection mechanism based on information sharing in online SNS and classify nodes according to different privacy setting policies; then, we define the states of the nodes and information dissemination rules with dynamics of infectious disease; finally, we describe the evolution process of different nodes by dynamic evolution equations. Detailed simulations and analysis show that the DMPS can precisely describe the diffusion process with multiple diffusion mechanisms and have the same characteristics as a diffusion process in real online SNS. As a result, DMPS can be used to identify the underlying diffusion mechanism of information and forecast its trend in online SNS.

Abstract With the pervasiveness of smartphones and the richness of mobile apps, many people are storing increasingly sensitive data on them, in greater quantities. In order to protect this sensitive information from misuse due to loss, or other accidental reasons, strong smartphone authentication has become imperative and has received considerable attention in recent years. However, when we directly implement traditional authentication schemes in smartphone devices, the balance between security and user‐friendliness of authentication becomes challenging, mainly because of the input‐in‐motion environments. In this paper, without adding extra hardware devices, we present a user‐friendly, dual‐factor authentication scheme called Duth, for Android smartphone devices. Specifically, the proposed Duth scheme is characterized by utilizing the spatial and time features of the user‐writing process as two factors of authentication; a user can be authenticated only if these two features are fulfilled. We implement Duth in Java as a library, which we make publicly available. With extensive discussions on parameter selection, we choose proper parameters and implement Duth on a smartphone with Android 2.3 for experiments, and the experiment results demonstrate that Duth can indeed achieve efficient and effective dual‐factor authentication. Copyright © 2014 John Wiley & Sons, Ltd.

Attribute‐based encryption (ABE) has been considered as a promising cryptographic primitive for realising information security and flexible access control. However, the characteristic of attributes is treated as the identical level in most proposed schemes. Lattice‐based cryptography has been attracted much attention because of that it can resist to quantum cryptanalysis. In this study, lattice‐based threshold hierarchical ABE (lattice‐based t ‐HABE) scheme without random oracles is constructed and proved to be secure against selective attribute set and chosen plaintext attacks under the standard hardness assumption of the learning with errors problem. The notion of the HABE scheme can be considered as the generalisation of traditional ABE scheme where all attributes have the same level.