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.

Public key encryption with equality test (PKEET) has been widely adopted in applications such as private health record management, secure outsourced data processing, and email filtering, owing to its ability to test equality on ciphertexts encrypted under different public keys. However, existing PKEET schemes often fall short in specialized settings, such as case-insensitive matching. Moreover, their security guarantees remain inadequate. In particular, many existing schemes are vulnerable to offline message recovery attacks (OMRA), which present a significant security challenge to PKEET. Furthermore, the existing IND-CCA security model is incomplete, as it fails to model all potential attacks that an adversary could exploit to execute the OMRA. To address these challenges, we propose a new variant of PKEET, termed public key encryption with case-insensitive fuzzy equality test (PKE-CIFET). To analyze security, we propose a unified security model that more closely aligns with IND-CCA security than previous works. This model includes more comprehensive oracles and considers adversaries launching the OMRA through multi-hop testing. Based on this model, we provide a comprehensive and rigorous security proof. Furthermore, experimental results demonstrate that the proposed PKE-CIFET scheme is efficient in terms of computational cost.

It is widely believed that increasing the amount of training data enhances the intelligence of deep learning models, which in turn heightens dependence on external datasets. However, these datasets are susceptible to adversarial poisoning, allowing attackers to insert backdoors that trigger misclassifications. Although various defense strategies have been suggested, training-phase defenses (TPDs) appear most promising, as they can significantly lower the attack success rate (ASR) without greatly impacting model performance. Nevertheless, creating TPDs that achieve both high accuracy and low ASR is challenging due to two main issues: 1) Many solutions require additional clean samples that match the distribution of the poisoned dataset, which is not always practical in real-world scenarios; 2) most existing solutions have high computational costs, sometimes requiring five to ten times the expense of standard training, which severely limits their practical use. To tackle these challenges, we introduce Confidence Consistency Detection (CCD), an efficient and lightweight training-phase backdoor detection method. CCD is particularly advantageous in situations where clean data is scarce or unavailable, as it completely eliminates the need for external clean samples. Moreover, CCD significantly reduces computational costs to just 25% to 50% of existing solutions (1.7 times the standard training time), providing a notable improvement over current TPD methods. The core innovation of CCD lies in its ability to utilize the high confidence shown by backdoor samples during the early stages of model training for precise detection. Specifically, we initially train a model on a poisoned dataset for a few epochs, followed by intra-class loss fine-tuning to increase sensitivity to poisoned samples. We then create preliminary sets of poisoned and clean samples by assessing the consistency of confidence variations before and after fine-tuning. These sets guide the model training, enabling the detection of high-confidence poisoned samples. Extensive experiments demonstrate that CCD effectively reduces the attack success rate (ASR) to 1.43%, while having a negligible impact on the model’s clean accuracy. In detecting poisoned samples, CCD achieves a 99% true positive rate (TPR) and a 0.033% false positive rate (FPR), setting a new benchmark in the field.

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.

Dynamic Searchable Symmetric Encryption (DSSE) plays a crucial role in secure cloud-based database systems, as it enables efficient keyword search and dynamic updates over encrypted data. However, practical deployment of DSSE schemes faces two significant challenges. First, clients may inadvertently perform faulty updates—such as re-adding an existing keyword-identifier pair or attempting to delete a non-existent one—which can compromise the correctness of subsequent search results. Second, even with correctly issued updates, malicious servers may return incorrect or incomplete search results, undermining data integrity. To address these challenges, we propose FVDSSE, the first fault-tolerant DSSE scheme that tolerates client-side operational faults and provides result verifiability against malicious servers. Moreover, it simultaneously ensures strong privacy by guaranteeing forward and backward privacy—two essential properties for any practical DSSE. To further optimize performance, we present FVDSSE-C, an enhanced variant that leverages caching techniques. Experimental evaluations on a real-world dataset show that FVDSSE-C achieves up to 130× improvement in search efficiency and 3× reduction in communication overhead compared to the state-of-the-art scheme (YCR22-C).

In the cloud era, hosting-based email services have become a common business model. Various entities can participate in the email delivery process. However, the intermediate paths of email delivery have received little attention. In particular, the vulnerabilities and centralization of email intermediate paths have already posed real-world security threats. This paper conducts the first systematic analysis of intermediate paths of email delivery, aiming to understand dependence patterns and characterize the centralization. In collaboration with a large email service provider, we collected Received headers from email reception logs spanning nine months and reconstructed the complete intermediate paths of 105M clean emails. Our results reveal that Microsoft is the dominant provider of intermediate paths, participating in 66.4% of emails. We find that 86.9M (82.7%) emails rely on third-party providers in intermediate paths, and 9.1M (8.7%) paths involve multiple providers. Email signature providers frequently appear in cross-vendor intermediate paths. In addition, we reveal significant differences in the regional dependencies and centralization of email intermediate paths across countries and continents. The centralization observed in email intermediate paths also differs from incoming and outgoing servers. We hope our work prompts more attention to email intermediate paths to enhance the security of the email ecosystem.

Recent developments in the field of Dynamic Searchable Symmetric Encryption (DSSE) with forward and backward privacy have attracted much attention from both research and industrial communities. However, most DSSE schemes with forward and backward privacy schemes only support single keyword queries, which impedes its prevalence in practice. Although some forward and backward private DSSE schemes with expressive queries (e.g., conjunctive queries) have been introduced, their backward privacy either essentially corresponds to single keyword queries or forward privacy is not comprehensive. In addition, the deletion of many DSSE schemes is achieved by addition paired with a deletion mark (i.e., lazy deletion). To address these problems, we present two novel DSSE schemes with conjunctive queries (termed SDSSE-CQ and SDSSE-CQ-S), which achieve both forward and backward privacy. To analyze their security, we present two new levels of backward privacy (named Type-O and Type-O-, more and more secure), which give a more comprehensive understanding of the leakages of conjunctive queries in the OXT framework. Eventually, the security analysis and experimental evaluations show that the proposed schemes achieve better security with reasonable computation and communication increase.

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.

Ring signatures enable a user to sign a message on behalf of a group while preserving both anonymity and unforgeability. Despite these strong privacy guarantees, they present regulatory challenges. To address these issues, we introduce a novel cryptographic primitive:timed anonymous ring signatures(TARS). Unlike group signatures, which rely on a trusted third party, TARS maintains the decentralization and unforgeability of traditional ring signatures while incorporatingtimed anonymity, allowing the signer’s identity to be disclosed by any user after a predetermined time period, denoted asT. To realize this, we propose a new CCA-securetimed public key encryption(TPKE) scheme that ensures correct decryption without the secret key after the timeT. Building upon TPKE, we present two concrete TARS constructions that guarantee anonymity until timeTand unforgeability at all times. To demonstrate its applicability, we apply TARS to a decentralized bidding system that is anticollusion between the auctioneer and the bidders. The system ensures anonymous bidding while disclosing the winner’s identity after the bid announcement, maintaining a transparent, fair, and decentralized bidding process. Finally, experimental evaluations confirm the practicality and efficiency of the proposed schemes. Crucially, the TARS scheme extends conventional ring signature schemes with timed anonymity by introducing only a moderate computational overhead (experimentally measured at ≈1.37 seconds under our configuration) while preserving their cryptographic robustness.

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.

Abnormal email bounces seriously disrupt user lives and company transactions. Proliferating security protocols and protection strategies have made email delivery increasingly complex. A natural question is how and why email delivery fails in the wild. Filling this knowledge gap requires a representative global email delivery dataset, which is rarely disclosed by email service providers (ESPs).

To protect user DNS privacy, four DNS over Encryption (DoE) protocols have been proposed, including DNS over TLS (DoT), DNS over HTTPS (DoH), DNS over QUIC (DoQ), and DNS over HTTP/3 (DoH3). Ensuring reachability stands as a prominent prerequisite for the proper functionality of these DoE protocols, driving considerable efforts in this domain. However, existing studies predominantly concentrate on a limited number of DoT/DoH domains or employ a restricted subset of vantage points (VPs).

The low-Earth orbit (LEO) satellite constellation holds immense potential for offshore wind farm surveillance since it can provide all-day and all-weather monitoring capabilities facilitated by satellite collaboration. However, it faces significant challenges. First, limited downlink transmission bandwidth constrained by ground stations and constraint on-orbit resources necessitate selective data downloads, focusing only on differences between consecutive data sets. Second, a passively injected satellite in open space poses a risk of unauthorized data extraction from neighboring satellites. Third, onboard energy constraints limit the feasibility of computationally intensive cryptographic operations. To tackle these challenges for the first time, we propose a novel secure and efficient on-orbit comparison (SEOC) scheme. Our solution begins with introducing a lightweight matrix encryption-based secure inner product (MSIP) technique tailored for secure on-orbit comparison. We further enhance communication efficiency by integrating a Cuckoo filter to reduce costs, complementing a novel difference comparison tree (DCTree) structure to manage false positives. Through comprehensive security analysis, the$\textsf {MSIP}$technique achieves selective security, and the$\textsf {SEOC}$scheme is secure under the universally composable (UC) framework. At last, performance evaluations demonstrate the high efficiency of our approach in terms of computational costs and communication overheads, which adapts to the limited on-orbit resources.

Graph data structures’ ability of representing vertex relationships has made them increasingly popular in recent years. Amid this trend, many property graph datasets have been collected and made public to facilitate a variant of queries such as the aggregate queries that will be extensively exploited in this paper. While cloud deployment of both the datasets and query services is intriguing, it could raise privacy concerns related to user queries and results. In past years, many works on graph privacy have been put forth, however they either do not consider query privacy or cannot be adapted for aggregate queries. Some others consider queries over encrypted graphs but cannot protect access pattern privacy. In particular, when deploying them to handle queries over public graph datasets, the cloud server can infer additional information related to user queries. Aiming at this challenge, we propose a privacy-preserving property graph aggregate query scheme in this paper. Specifically, we first design new privacy-preserving vertex matching and matching update techniques, which securely initialize and update the mapping between vertices in the dataset and the user-specified patterns, respectively. Based on them, we construct our proposed scheme to achieve aggregate queries over public property graphs. Rigid security analysis shows that our proposed scheme can protect the privacy of user queries and results as well as achieve access pattern privacy. In addition, extensive experiments also demonstrate the efficiency of our scheme in terms of computational overheads.

Community search over graphs, which is believed as a powerful tool for locating subgraphs of closely related vertices, has received considerable attention in recent years, and$k$-truss is such a popular community search metric to obtain subgraphs in which every edge forms$(k-2)$triangles. In this paper, we particularly consider$k$-truss community query services, which will return all$k$-truss communities containing a given query vertex. As is known, when the size of graph grows, for achieving better performance, it is natural for a service provider to outsource the services to a powerful cloud. However, this stresses the need for privacy-preserving$k$-truss community query services, as the cloud server is not fully trustable. Over the past years, many schemes focusing on privacy-preserving graph computation have been put forth, but none of them can well support privacy-preserving$k$-truss community queries. Aiming at this challenge, we first propose a privacy-preserving$k$-truss community query scheme ($k$TCQ) by constructing boolean circuits with homomorphic encryption technique and a table-based index. After that, we also design an efficiency-enhanced version ($k$TCQ+) based on a stream cipher scheme to reduce the encrypted index's size and improve the query efficiency. Detailed security analysis shows that both$k$TCQ and$k$TCQ+ can well preserve data privacy and access pattern privacy, and extensive experimental results also demonstrate that$k$TCQ+ can observably reduce the size of encrypted index and the query time by$12\times$and$5.9\times$, respectively.

Webmail, protected by the HTTPS protocol, only works correctly if both the server and client implement HTTPS-related features without vulnerability. Nevertheless, the deployment situation of these features in the webmail world is still unclear. To this end, we perform the first end-to-end and large-scale measurement of webmail service. For the server side, we first build an email address set with a size of 2.2 billion. Then we construct two webmail domain datasets: one contains 21 k domains filtered from the email address set; the other only includes 34 domains but supports more than 75% of the 2.2 billion email addresses. After performing a comprehensive measurement on these two webmail domain datasets, we find that some features are poorly deployed. Furthermore, we also rank servers by analyzing the properties of HTTPS-related features. For the client side, we investigate implement of HTTPS-related features in 50 different combinations of web browsers and operating systems (OSes). We find that even the latest browsers have poor support for some features. For example, Firefox in all OSes does not support CT. Our findings highlight that the full deployment of the security features for the HTTPS ecosystem is still a challenge, even in the webmail service.

Payment Channel Networks (PCNs) have flourished as one of the most promising solutions to the blockchain scalability problem. Unfortunately, the existing PCN solutions either fail to provide path privacy guarantees or require the not-always-true All-Anonymous-Connected assumption (i.e., an anonymous communication channel always exists for any two participants). To alleviate these problems, we first present a new cryptographic primitive named anonymous multi-hop payment (AMHP), which is an improvement of anonymous multi-hop lock (AMHL). Using AMHP and payment channels, we can have a new PCN solution with path privacy but removing the All-Anonymous-Connected assumption. After that, we present the first AMHP scheme, called AMHL+, by adapting the generic construction of AMHL, but at the cost of high communication overhead. To reduce the communication cost, we further present a new AMHP scheme (named EAMHL+) using bilinear pairing. The communication cost of the EAMHL+ is reduced by 92.3% compared to the AMHL+. The rigorous security analysis demonstrates that the EAMHL+ holds consistency, balance security, and path privacy. Finally, we implement the proposed AMHP schemes using Java. The extensive experimental results show that, though the EAMHL+ requires more computational cost than the AMHL+, it is more efficient than the latter in terms of communication overhead.

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 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.

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.

Bipartite graphs have been widely adopted in applications such as e-healthcare thanks to their ability to model various real-world relationships. Meanwhile, (,)-core query services over bipartite graphs are recognized as a promising approach for finding communities, i.e., closely related sets of vertices in a bipartite graph. As the bipartite graph grows, service providers tend to outsource the services to the cloud. However, there are privacy concerns related to the dataset, queries, and results. Although many schemes have been proposed for privacy-preserving graph analysis, they cannot be directly adopted to handle accurate (,)-core queries. Aiming at the challenges, under the two-server setting, this paper constructs two privacy-preserving schemes with different security levels to handle (,)-core queries. In the proposed schemes, a graph is represented as an index containing two tables and further encrypted by a symmetric homomorphic encryption scheme, and then the servers securely traverse the index. Detailed security analysis shows that both schemes can achieve access pattern privacy, while the security-enhanced one can further protect the structure of the query requests and results. In addition, extensive performance evaluations are conducted to indicate the efficiency of our proposed schemes.

The advances in Internet of Things, Big Data, and machine learning technologies have greatly transformed our daily lives into much more intelligent ones by offering various promising services. Among those services, the discrete Fréchet distance (DFD) range query, which aims to obtain a set of trajectories whose distances to a given query trajectory do not exceed a given threshold, has been widely applied to support applications such as vehicle trajectory clustering and other data processing tasks. Meanwhile, due to the huge data volume issue in the Big Data era, there is a trend towards outsourcing various query services to the cloud for achieving a better performance. However, since the cloud is not fully trustable, designing privacy-preserving query services becomes a research focus. Over the past years, many schemes focusing on privacy-preserving trajectory analysis have been proposed, but none of them can well support privacy-preserving DFD range queries. Aiming at addressing this challenge, this paper proposes a novel privacy-preserving DFD range query scheme, in which queries are conducted in a filtration-and-verification manner and the privacy of the dataset and queries can be preserved. Specifically, by indexing the dataset with two R-trees, a query can be conducted by i) querying the two R-trees to obtain a candidate set and ii) verifying each trajectory in the set, which involve two basic operations, namely, rectangle intersection detection and proximity detection. To preserve the privacy of the dataset and queries, we build the two basic operations upon a novel Inner-Product Preserving Encryption (IPPE) scheme, which is proved to be selectively secure with trivial leakages. Besides, extensive experiments are conducted, and the results demonstrate that our proposed scheme can significantly reduce the computational cost by effectively reducing the candidate set’s size.

Blockchain systems face two emergent problems, namely scalability and privacy, each of which has been addressed independently. However, how to achieve privacy and scalability simultaneously remains a challenging problem for blockchains. In this article, we propose a privacy-preserving and scalable permissioned blockchain system called HyperMaze employing the zero knowledge proof technique and a hierarchical system architecture. It gains scalability by adopting a hierarchy of multiple blockchains that processes transactions in parallel. We design anID-based dual-balance account modelwhere an identity-based account is associated with two types of balances–a plaintext balance and a private (zero-knowledge) balance. Furthermore, we design a two-phase cross-chain transaction mechanism (2PXT) to achieve transaction privacy for both intra-chain and cross-chain transactions. We rigorously formulate a security model for HyperMaze under the universal composability framework, and then provide a simulation-based security proof. A prototype of HyperMaze is implemented and a series of experiments are conducted over up to 2,600 nodes to evaluate its performance. The experimental results show that a 4-level, (7,10)-threshold, 6-ary HyperMaze system can reach 19,440 TPS and the transaction confirmation latency is only 9.5 seconds. To our best knowledge, HyperMaze is the first high-throughput privacy-preserving blockchain whose throughput is over 19,000 TPS.

As a popular and practical query type in location-based services, the spatial keyword query has been extensively studied in both academia and industry. Meanwhile, with the growing demand for data privacy, many privacy-preserving spatial keyword query schemes have been proposed to deal with queries over encrypted data. However, none of the existing schemes preserve access pattern privacy, and the recent research illustrates that leaking such privacy may incur inference attacks and thus disclose sensitive information. In addition, most existing schemes only consider the boolean keyword search, which is not quite practical and flexible in real-world applications. To address the above issues, in this paper, we propose two privacy-preserving spatial keyword similarity query schemes that can preserve full and partial access pattern privacy, respectively. First, we present a basic privacy-preserving spatial keyword similarity query scheme (PPSKS) by integrating a secure set membership test (SSMT) technique with secure circuits. After that, to improve performance, we propose a tree-based scheme (PPSKS+) by employing a new index called FR-tree together with a predicate encryption technique that can encrypt FR-tree. Formal security analysis shows that: i) our proposed schemes can protect outsourced data, query requests, and query results; ii) our PPSKS scheme can hide full access patterns, while the PPSKS+ scheme preserves$m$-access pattern privacy. Extensive experiments are also conducted, and the results indicate that our tree-based PPSKS+ scheme is much more efficient, almost two orders of magnitude better than our linear search PPSKS scheme in performing queries.

Skyline queries, especially those variants that allow users to define their own query criteria, are very promising and practical techniques in multi-criteria decision making applications. Meanwhile, the growing data volume drives the service providers to outsource their data to the cloud for reaping economic benefits. However, privacy concerns compel the outsourced data to be encrypted and require to perform the skyline queries over encrypted data. To achieve the privacy-preserving skyline queries, many schemes were proposed in the literature. However, those existing solutions cannot fully support the user-defined query criteria in skyline queries, and most of them employ a two-server model to support skyline queries over ciphertexts, which needs multi-round communications between the deployed two servers. In this article, we propose a privacy-preserving user-defined skyline query scheme in a single-server model, which eliminates extra communications. Specifically, we first formally define the user-defined skyline query. Then, based on the idea of converting order relations into computing the inner products of two multi-dimensional points, we design three predicate encryption schemes. Finally, we adopt these predicate encryption schemes to construct our proposed scheme. Detailed security analysis shows that these predicate encryption schemes are selectively secure, and the proposed user-defined skyline query scheme is privacy-preserving. In addition, extensive experiments are conducted, and the results show that our proposed scheme outperforms the alternative scheme by up to an order of magnitude in terms of computational costs when performing user-defined skyline queries.

Outsourcing encrypted time series data and query services to a cloud has been widely adopted by data owners for economic considerations. However, it inevitably lowers data utility and query efficiency. Existing secure skyline query schemes either leak critical information or are inefficient. In this paper, we propose an efficient and privacy-preserving interval skyline query scheme by employing symmetric homomorphic encryption (SHE). Specifically, we first devise a secure sort protocol to sort the encrypted dataset and a secure high-dimensional dominance check protocol to securely determine dominance relations of time series data, in which a dominance check tree is presented. With these secure protocols, we propose our secure skyline computation protocol that can ensure both security and efficiency. Furthermore, to deal with the characteristics of time series data, we design a look-up table to index time series for quick query response. The security analysis shows that our proposed scheme can protect outsourced data, query results, and single-dimensional privacy and hide access patterns. In addition, we evaluate our proposed scheme and compare the core component of our scheme with the state-of-the-art solution, and the results indicate that our protocol outperforms the compared solution by two orders of magnitude in the computational cost and at least 23× in the communication cost.

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.

With the emergence of countless independent blockchain systems in recent years, cross-chain transactions have attracted considerable attention, and lots of solutions have been put forth by both industry and academia. However, most of the existing solutions suffer from either centralization or scalability issues. To mitigate these issues, in this paper, we propose the concept of cross-chain virtual payment channels, which allows two users in different blockchain systems to conduct limitless off-chain transactions with the help of an intermediate node, hence solving the centralization and scalability issues. Furthermore, as the intermediate node is only involved in the channel open and close operations, it further improves the efficiency of cross-chain transactions, and to a certain extent, it even enhances the privacy of the cross-chain transaction. Meanwhile, we also present the first concrete cross-chain virtual payment channel scheme, which only requires one of the blockchain systems supporting the Turing-complete scripting language. The corresponding detailed security analysis in the Universal Composability framework demonstrates that our proposal holds the Consensus on Open, Update, and Close. Finally, we implement and deploy our cross-chain virtual payment channel scheme on the Ethereum and Bitcoin test networks. The extensive experimental results show that our proposal dramatically improves the efficiency of cross-chain transactions, and the advantage becomes more pronounced as the number of transactions increases.

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.

The DNS privacy protection mechanisms, DNS over TLS (DoT) and DNS over HTTPS (DoH), only work correctly if both the server and client support the Strict Privacy profile and no vulnerability exists in the implemented TLS/HTTPS. A natural question then arises: what is the landscape of DNS Strict Privacy? To this end, we provide the first longitudinal and comprehensive measurement of DoT/DoH deployments in recursive resolvers, authoritative servers, and browsers. With the collected data, we find the number of DoT/DoH servers increased substantially during our ten-month-long scan. However, around 60% of DoT and 44% of DoH recursive resolver certificates are invalid. Worryingly, our measurements confirm the centralization problem of DoT/DoH. Furthermore, we classify DNS Strict Privacy servers into four levels according to daily scanning results on TLS/HTTPS-related security features. Unfortunately, around 25% of DoH Strict Privacy recursive resolvers fail to meet the minimum level requirements. To help the Internet community better perceive the landscape of DNS Strict Privacy, we implement a DoT/DoH server search engine and recommender system. Additionally, we investigate five popular browsers across four operating systems and find some inconsistent behavior with their DNS privacy implementations. For example, Firefox in Windows, Linux, and Android allows DoH communication with the server without the SAN certificate. At last, we advocate that all participants head together for a bright DNS Strict Privacy landscape by discussing current hindrances and controversies in DNS privacy.

Secure queries are fundamental to data security, particularly in cloud databases. In data analytics, one of the common and practical queries is the iceberg query that can find aggregate values above a specified threshold. However, existing secure aggregate query schemes: 1) are unable to support secure iceberg queries equipped with the HAVING clause; 2) only consider additive aggregate functions; and 3) suffer from performance issues due to the use of homomorphic encryption to encrypt databases. In this article, we present a secure iceberg query scheme, SecBerg, to support both addition-based and comparison-based aggregate functions and ensure high efficiency and security simultaneously. To make it possible, we propose a secure bitmap index system to encode database values and pioneer the use of the arithmetic secret sharing technique to protect databases in the cloud environment. Furthermore, we carefully design efficient and secure protocols over arithmetic secret sharing to construct our SecBerg. Extensive evaluations are conducted, and the results indicate that SecBerg is significantly more efficient than the state-of-the-art relevant scheme in computational overhead and can attain orders of magnitude performance improvement at best.

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.

Although federated learning improves privacy of training data by exchanging local gradients or parameters rather than raw data, the adversary still can leverage local gradients and parameters to obtain local training data by launching reconstruction and membership inference attacks. To defend against such privacy attacks, many noises perturbed methods (like differential privacy or CountSketch matrix) have been widely designed. However, the strong defence ability and high learning accuracy of these schemes cannot be ensured at the same time, which will impede the wide application of FL in practice (especially for medical or financial institutions that require both high accuracy and strong privacy guarantee). To overcome this issue, we propose an efficient model perturbation method for federated learning to defend against reconstruction and membership inference attacks launched by curious clients. On the one hand, similar to the differential privacy, our method also selects random numbers as perturbed noises added to the global model parameters, and thus it is very efficient and easy to be integrated in practice. Meanwhile, the random selected noises are positive real numbers and the corresponding value can be arbitrarily large, and thus the strong defence ability can be ensured. On the other hand, unlike differential privacy or other perturbation methods that cannot eliminate added noises, our method allows the server to recover the true aggregated gradients by eliminating the added noises. Therefore, our method does not hinder learning accuracy at all. Extensive experiments demonstrate that for both regression and classification tasks, our method achieves the same accuracy as non-private approaches and outperforms the state-of-the-art defence schemes. Besides, the defence ability of our method against reconstruction and membership inference attack is significantly better than the state-of-the-art related defence schemes.

The rapidly growing location-based services enable service providers to accumulate plentiful descriptions on points of interest (POIs), which can be used to support expressive POI queries. In this article, we study a type of POI query, named feature-based group$k$nearest neighbor query over road networks, in which a user has a feature set and several locations and wishes to find$k$closest POIs that have similar sets of features to the query. As the POI data sets grow, service providers tend to outsource their data sets to a powerful yet not-fully trusted cloud, which calls for privacy preservation on data sets and user queries. Although many schemes have been proposed for privacy-preserving POI queries, none of them can simultaneously support privacy-preserving set similarity and road network distance comparison. To address this challenge, we propose an efficient and private feature-based group nearest neighbor query scheme. In our scheme, we achieve privacy-preserving distance comparison by employing the road network hypercube embedding technique, and design an encrypted index based on B+-tree for privacy-preserving set similarity range queries. Security analysis shows our proposed scheme can preserve the privacy of the data set and queries, and performance evaluation also demonstrates it is computationally 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.

Wireless body area network (WBAN) and big data techniques indubitably enable the online medical diagnosis system to be more practical. In the system, to make a more accurate diagnosis, doctors wish to obtain some archived medical data records, which are similar to the sensed patient data, to learn from the prior diagnoses. As a practically useful similarity search, the dynamic skyline query can provide doctors with similar data records having all possible relative weights of attributes. Driven by the powerful cloud, the data owner often outsources encrypted data and the corresponding services, e.g., dynamic skyline query services here, to a third-party cloud. As a result, it is required to perform the dynamic skyline query over encrypted data. However, existing schemes are either insecure or inefficient. To address the issue, in this article, we propose an efficient and privacy-preserving dynamic skyline query scheme and use it in an online medical diagnosis system. Specifically, based on symmetric homomorphic encryption (SHE), we present a set of efficient and secure protocols to achieve various operations, such as less than comparison, equality test, and dominance determination, without leaking any sensitive information to the cloud. With these secure protocols, we carefully design our dynamic skyline query scheme to attain full security and high efficiency at the same time. Detailed security analysis shows that our proposed scheme is indeed privacy-preserving. With extensive experimental evaluations, we show that our proposed scheme outperforms the alternative scheme by two orders of magnitude in the computational cost and at least$8.1\times $in the communication cost.

Aggregate nearest neighbor (ANN) query, which can find an optimal location with the smallest aggregate distance to a group of query users’ locations, has received considerable attention and been practically useful in many real-world location-based applications. Nevertheless, query users still hesitate to use these applications due to privacy concerns, as there is a worrisome that the location-based service (LBS) providers may abuse their locations after collecting them. In this article, to tackle this issue, we propose a novel privacy-preserving aggregate query (PPAQ) scheme to select an optimal location for query users in road networks. Specifically, we first analyze the problem of the ANN query in road networks and identify two basic operations, i.e., addition and comparison, in the query. Then, we carefully design efficient addition and comparison circuits to securely add and compare two bit-based inputs, respectively. With these two secure circuits, we propose our PPAQ scheme, which can simultaneously protect the users’ locations, query results, and access patterns from leaking. Detailed security analysis shows that our proposed scheme is indeed privacy-preserving. In addition, extensive performance evaluations are conducted, and the results indicate that our proposed scheme has an acceptable efficiency for non-real-time applications.

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.

Cloud storage as one of the most important services of cloud computing which significantly facilitates cloud users to outsource their data to the cloud for storage and share them with authorized users. In cloud storage, secure deduplication has been widely investigated as it can eliminate the redundancy over the encrypted data to reduce storage space and communication overhead. Regarding the security and privacy, many existing secure deduplication schemes generally focus on achieving the following properties: data confidentiality, tag consistency, access control, and resistance to brute-force attacks. However, as far as we know, none of them can achieve these four requirements at the same time. To overcome this shortcoming, in this article, we propose an efficient secure deduplication scheme that supports user-defined access control. Specifically, by allowing only the cloud service provider to authorize data access on behalf of data owners, our scheme can maximally eliminate duplicates without violating the security and privacy of cloud users. Detailed security analysis shows that our authorized secure deduplication scheme achieves data confidentiality and tag consistency while resisting brute-force attacks. Furthermore, extensive simulations demonstrate that our scheme outperforms the existing competing schemes, in terms of computational, communication and storage overheads as well as the effectiveness of deduplication.

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.

Due to its capabilities of searches and updates over the encrypted database, the dynamic searchable symmetric encryption (DSSE) has received considerable attention recently. To resist leakage abuse attacks, a secure DSSE scheme usually requires forward and backward privacy. However, the existing forward and backward private DSSE schemes either only support single keyword queries or require more interactions between the client and the server. In this article, we first give a new leakage function for range queries, which is more complicated than the one for single keyword queries. Furthermore, we propose a concrete forward and backward private DSSE scheme by using a refined binary tree data structure. Finally, the detailed security analysis and extensive experiments demonstrate that our proposal is secure and efficient, respectively.

It has been witnessed that Aggregate Reverse Skyline (ARS) query has recently received a wide range of practical applications due to its marvelous property of identifying the influence of query requests. Nevertheless, the query users may hesitate to participate in such query services as the query requests and query results may leak sensitive personal data or valuable business data assets to the service providers. To tackle the concerns, a promising solution is to encrypt the query requests, conduct the ARS queries over encrypted query requests without decrypting, and return the encrypted query results. Unfortunately, many existing solutions are either deployed over a two-server model or unable to fully preserve query privacy. In this paper, we propose a novel privacy-preserving aggregate reverse skyline query (PPARS) scheme on a single server model while ensuring full query privacy. Specifically, we first transform the problem of ARS query into a combination of set membership test and logical expressions. Then, by employing the prefix encoding technique, bloom filter technique, and fully homomorphic encryption, we run the transformed logical expressions to obtain the encrypted aggregate values without leaking query requests, query results, and access patterns. Furthermore, we propose an interpolation-based packing technique to improve the communication efficiency of PPARS. Detailed and formal security analysis demonstrates that our proposed schemes can guarantee strong security. In addition, extensive experiments are conducted, and the results validate the efficiency of our proposed schemes.

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.

Due to the big data blowout from the Internet of Things and the rapid development of cloud computing, outsourcing computation has received considerable attention in recent years. Particularly, many outsourcing computation schemes have been proposed to dedicate the outsourcing polynomial computation due to its use in numerous fields, such as data analysis and machine learning. However, none of those schemes are practical enough, as they either require some time-consuming cryptographic operations to achieve fairness between the user and the worker, or cannot allow the user to outsource arbitrary polynomial to the worker, or need two non-collusive workers. To tackle these challenges, in this article, we propose a new outsourcing polynomial computation scheme by employing a variant of Horner’s method and the blockchain technology. Specifically, the former makes the computational cost on the worker side as low as possible, and the latter guarantees the fairness between the user and the worker if the result from the worker can be publicly verified. To achieve the public verifiability property, we apply the sampling technique, which is effective in our proposal according to a game-theoretic analysis. Furthermore, we also implement a prototype of our proposal and run it on an Ethereum test net. The extensive experimental results demonstrate that our proposal is efficient in terms of computational cost.

Due to the high demands of searchability over encrypted data, searchable encryption (SE) has recently received considerable attention and been widely suggested in encrypted cloud storage. Typically, the cloud server is assumed to be honest-but-curious in most SE-based cloud storage systems, i.e., the cloud server should follow the protocol to return valid and complete search results to users. However, this trust assumption is not always true due to some unanticipated situations, such as misconfigurations and malfunctions. Therefore, the function of verifiability of search results becomes crucial for the success of SE-based cloud storage systems. For this reason, many verifiable SE schemes have been proposed; however, they either fail to support query operators “OR”, “AND”, “$\ast$” and “?” simultaneously, or require many time-consuming operations. Aiming at addressing this problem, in this paper, we propose a new verifiable SE scheme for encrypted cloud storage. The proposed scheme is characterized by integrating various techniques, i.e., bitmap index, radix tree, format preserving encryption, keyed-hash message authentication code and symmetric key encryption, for achieving efficient and verifiable conjunctive and fuzzy queries over encrypted data in the cloud. Detailed security analysis shows that our proposed scheme holds the confidentiality of data and verifiability of search results at the same time. In addition, extensive experiments are conducted, and the results demonstrate our proposed scheme is efficient and suitable for users to retrieve their data from the cloud to their mobile devices.

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.

As one of the most fundamental problems in mobile crowdsensing (MCS), worker selection has drawn significant attention in recent years. However, very few studies consider the workers' spatio- and temporal-coverage for the sensing task. In this paper, we propose a novel top-k worker selection scheme such that the MCS platform can select qualified workers in terms of spatio-temporal similarity. Besides, we design a novel privacy-preserving approach for protecting participants' spatio-temporal information based on the modified Paillier encryption technique. Detailed security analysis showed that the task re-questor's temporal information and the workers' spatio-temporal information are preserved and will not be revealed to any other parties. Extensive experiments are conducted, and the results demonstrate that our scheme outperforms the baseline methods regarding the selection of reliable workers.

As the growing proportion of aging population, the demand for sustainable, high quality, and timely healthcare services has become increasingly pressing, especially since the outbreak of COVID-19 pandemic in the early of 2020. To meet this demand, a promising strategy is to introduce cloud computing and digital twin techniques into the healthcare systems, where the cloud server is employed for storing healthcare data and offering efficient query services, and the digital twin is used for building digital representation for patients and leverages the query services of the cloud server to monitor healthcare states of patients. Although several cloud computing and digital twin based healthcare monitoring frameworks have been proposed, none of them has considered the data privacy issue, yet the leakage of the private healthcare information may cause catastrophic losses to patients. Aiming at the challenge, in this paper, we propose an efficient and privacy-preserving similarity query based healthcare monitoring scheme over digital twin cloud platform, named PSim-DTH. Specifically, we first formalize a similarity query based healthcare monitoring model over digital twin cloud platform. Then, we deploy a partition-based tree (PB-tree) to index the healthcare data and introduce matrix encryption to propose a privacy-preserving PB-tree based similarity range query (PSRQ) algorithm. Based on PSRQ algorithm, we propose our PSim-DTH scheme. Both security analysis and performance evaluation are extensively conducted, and the results demonstrate that our proposed PSim-DTH scheme is really privacy-preserving and efficient.

Enabled by the flourishing Internet-of-Things technology, smart cities can offer a variety of smart services to our daily lives and have received considerable attention in recent years. As a pivotal component of smart cities, location-based services (LBSs) have been deeply exploited by both academia and industry. Meanwhile, since cloud computing can provide reliable and flexible IT resources, many LBS services have been outsourced to the cloud for offering better services. Nevertheless, as the cloud is not fully trusted, privacy preservation becomes an essential requirement for these services. Over the past years, many privacy-preserving location-based k-nearest neighbor ( kNN) query schemes over the cloud have been proposed. However, most of them are subjected to an inevitable design defect, i.e., whenever a user queries twice at the same location, the cloud can identify and return the same query result to the query user, and such information together with third-party data breaches could be exploited by the cloud for some location disclosures. Although some existing schemes can cope with the issue, they are not quite practical, as they will bring heavy overheads on the query user side. In this article, aiming to address the above challenge, we propose a novel oblivious location-based kNN query scheme, in which the cloud cannot link two queries even if they are initiated by query users at the same location. Specifically, based on the modified Paillier cryptosystem, we first present three privacy-preserving protocols, namely, oblivious absolute value calculation, sorting, and top- k extraction. Then, by integrating these three protocols, we propose our novel oblivious location-based kNN query scheme. The detailed security analysis shows that our proposed scheme really enhances the privacy preservation in LBS queries. In addition, extensive performance analysis and experiments are conducted, and the results indicate that our proposed scheme is also efficient for the query user.

The sixth-generation (6G) communication technology has been attracting great interests from both industry and academia, as it is regarded as a promising approach to achieve more stable and low-latency communication. These promising features of 6G make it an enabler for cybertwin, a technique to create digital representations for physical objects to implement various functionalities. In this article, we consider a cybertwin-based spatiotemporal keyword query service over a dynamic message data set in intelligent transportation system (ITS) scenarios. Particularly, in the considered service, publishers upload messages to the cloud, and each cybertwin predictively launches queries to retrieve messages on behalf of the corresponding vehicle, such that each vehicle can timely receive messages that are of its interest whenever it arrives at a location. Nevertheless, as the cloud is not fully trustable, there exist privacy concerns related to the messages and queries. Up to now, although many schemes have been proposed to handle privacy-preserving spatial, temporal, or keyword queries, none of them can simultaneously support queries containing both spatial, temporal, and keyword criteria on dynamic data sets. Aiming at the issue, we design a layered index based on segment trees to dynamically organize messages containing both spatial, temporal, and keyword information. Moreover, based on a symmetric homomorphic encryption scheme, we encrypt the messages and queries and present a two-server privacy-preserving spatiotemporal keyword query scheme. We analyze the security of the proposed scheme and also conduct extensive experiments to evaluate its performance. The results show that our proposed scheme is indeed privacy preserving and computationally efficient.

The fog computing architectural model has recently seen advances with respect to bandwidth and latency issues. However, since fog devices are deployed at the network edge and are not fully trustable, there are still security and privacy challenges. In this article, aiming at improving both communication efficiency and privacy protection, we propose a new efficient and privacy-preserving range query scheme in fog-based Internet of Things (IoT). We, first, introduce a new decomposition technique to efficiently interpret a given range query [L, U], where 0 ≤ L ≤ U ≤ n - 1, as a form of inverted reduced path strings. Then, the symmetric homomorphic encryption (SHE) scheme is employed to encrypt the reduced paths and hand them over securely through a fog node to the IoT devices. This technique enables a query user to launch a privacy-preserving continuous or noncontinuous range query and receive a homomorphically aggregated encrypted response with an improved O(log2n) communication efficiency. The detailed security analysis shows that our proposed scheme is privacy preserving. In addition, extensive performance evaluations are also conducted, and the results demonstrate that our proposed scheme is by far more efficient than those previously reported schemes in terms of computational overhead and communication complexity.

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.

The emergence of mobile crowdsensing (MCS) has provided us with unprecedented opportunities for both sensing coverage and data transmission. However, in many MCS applications, the MCS workers are usually required to report the location information of the assigned tasks, which inevitably reveals the workers' location information, even trajectories, and severely impedes the popularization of the MCS system. It is believed that the query on the most-frequent location, e.g., querying the most congested location over a period in a city, is one of the most popular statistics queries in the MCS system, but it may disclose workers' location information. To address the issue, in this article, we propose a location privacy-preserving scheme for outsourced most-frequent item query in the MCS system, where two noncollusive semi-trusted cloud servers cooperatively handle the most-frequent item query. Specifically, by employing our pseudonymization mechanism, transposition cipher, ciphertext packing technique, and order-preserving merge function, our proposed scheme can efficiently answer the most-frequent item query while ensuring the privacy of both workers' personal information and query results. Detailed security analysis shows that our proposed scheme is privacy-preserving. In addition, extensive experiments are conducted, and the results show that our proposed scheme outperforms alternative schemes in terms of computational costs and communication overhead.

Proxy re-encryption (PRE) allows a proxy to transform one ciphertext to another under different encryption keys while keeping the underlying plaintext secret. Because of the ciphertext transformability of PRE, there are many potential private communicating applications of this feature. However, existing PRE schemes are not as full-fledged as expected. The lack of necessary features makes them hard to apply in real-world scenarios. So far, there does not exist a unidirectional multihop PRE scheme with constant decryption efficiency and constant ciphertext size without extensions. Impractical performance and weak scalability also hinder PRE from most real-world applications. In this work, we present a new PRE scheme with secure hardware enclave namedhPRESS(hardware-enhanced PRE scheme using secure enclave). To the best of our knowledge,hPRESSis the first unidirectional multihop PRE scheme which achieves both constant decryption efficiency and constant ciphertext size without extensions. A detailed security analysis demonstrates that our proposal is CCA secure based on the security of the underlying encryption schemes and the secure enclave. We also implement a prototype based on Intel SGX, one of the most popular secure enclave techniques in recent years, and evaluate its performance. The experimental results show that, compared with previous PRE schemes, ourhPRESSis almost one order of magnitude faster in terms of the decryption and transformation.

Secure data deduplication, as it can eliminate redundancies over encrypted data, has been widely developed in cloud storage to reduce storage space and communication overheads. Among them, the convergent encryption has been extensively adopted. However, it is vulnerable to brute-force attacks that can determine which plaintext in a message space corresponds to a given ciphertext. Many existing schemes have to sacrifice efficiency to resist brute-force attacks, especially for cross-domain deduplication, which is inevitably contrary to practical applications. Moreover, few existing schemes consider protecting the message equality information (i.e., whether two different ciphertexts correspond to an identical plaintext). To address the above challenges, in this paper, we propose an efficient and privacy-preserving big data deduplication scheme for a two-level multi-domain architecture. Specifically, by generating a random tag and a constant number of random ciphertexts for each data, our scheme not only ensures data confidentiality under multi-domain deduplication but also resists brute-force attacks. By allowing only the agent and cloud service provider to perform intra-deduplication and inter-deduplication, respectively, our scheme can protect the message equality information from disclosure as much as possible. Detailed security analysis shows that our scheme achieves privacy-preservation for both data content and the message equality information and data integrity while resisting brute-force attacks. Furthermore, extensive simulations demonstrate that our scheme significantly outperforms the existing competing schemes, especially the computational cost and the time complexity of the duplicate search.

By integrating various connected devices, it is possible for smart cities to optimize the efficiency of various aspects of city operations. In particular, connected vehicles in smart cities, which are coordinated by Intelligent Transportation Systems (ITS), can not only enjoy enhanced safety and efficiency, but also offer content dissemination services through smart cities. In order to achieve effective content dissemination, a vehicle selection approach usually needs to be involved to select a limited number of vehicles while disseminating content to a city as wide as possible. However, such an approach inevitably requires the trajectories of vehicles, which are private to the vehicles. In this paper, to preserve the trajectory privacy of the vehicles during the vehicle selection, we propose a privacy-preserving vehicle selection scheme for effective content dissemination. Specifically, in the proposed scheme, given encrypted trajectories of n vehicles, a cloud with two non-collusive servers can select k vehicles that jointly cover an approximately optimal area of the city. Detailed security analysis and performance evaluation show that our proposed scheme can not only preserve the privacy of vehicles' trajectories, but also achieve efficient vehicle selection with an approximately optimal coverage.

The vision of future intelligent information society will be globally data driven, enabled by Internet of Things (IoT) techniques. In any IoT-enabled applications, huge volumes of time-series data are continuously generated by IoT devices, which will be fed for high-level functions. Among these functions, the max aggregation query over a specific time interval is one of the frequently used operations. However, due to the limited resources in IoT devices, a common way to deal with the max aggregation query is to involve powerful cloud servers. Nevertheless, as the sensed data from IoT devices and its pattern (e.g., local ranking sequences) are usually private and the cloud servers are not fully trusted, the data should be encrypted before being outsourced to cloud servers. Obviously, the data encryption will incur some efficiency issues. In this paper, to mitigate the privacy and efficiency issues, we propose an efficient and privacy-preserving max aggregation query scheme for time-series data in IoT scenarios. Specifically, we first employ a segment tree based data structure to represent the data collected by IoT devices. Then, to protect the privacy, we leverage two encryption techniques to encrypt the data structure. With the encrypted data structure, our proposed scheme can handle a ranged max aggregation query with O(log L) time complexity, where L is the range length of the query. Detailed security analysis and performance evaluation show that our scheme can not only preserve the privacy of data and its local ranking sequences, but also achieve efficient ranged max aggregation query.

The advance of Internet-of-Things (IoT) techniques has promoted an increasing number of organizations to explore more mission-critical solutions. However, the response latency, bandwidth usage, and reliability are still challenging issues in the traditional IoT. To tackle these challenges, the fog-based IoT has become popular and the range query is one of the most frequently used operations in fog-based IoT, where given a range query, a fog node will return the aggregated data from IoT devices to the query user. Because the fog nodes are not fully trusted, there is a desire to design a privacy-preserving range query scheme in the fog-based IoT. However, most of existing privacy-preserving range query schemes are not efficient in terms of communication overhead, especially for a large-size range. Therefore, it is still a challenging issue to design a communication-efficient range query in fog-based IoT. Aiming at this challenge, in this article, we propose a new privacy-preserving range query scheme in the fog-based IoT. Specifically, we first devise an efficient homomorphic encryption scheme for maintaining data privacy and security in a range query. Then, we present a novel range decomposition technique to compile the range query, which can transform a given range query [L, U], where 0 ≤ L ≤ U ≤ n - 1, into a semi-triangular structure, and enable our proposed scheme to achieve O(log3n) communication efficiency. The detailed security analysis shows that our proposed scheme is really privacy preserving, and the extensive performance evaluation demonstrates that our proposed scheme is efficient in terms of low communication overhead and the computational cost.

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.

Data collected from various data sources are destined to be logically interrelated but geographically distributed. Top-k query is an efficient way to find the most important objects from high volumes of data. A common way to process the top-k query over distributed data is to bring them to a centralized entity (e.g. cloud). However, there are privacy considerations during the top-k query when dealing with sensitive data (e.g. eHealthcare data) in such method. Apart from data privacy, efficiency also needs to be taken into consideration. Existing focuses on top-k query do not (fully) consider the data privacy or efficiency. In order to deal with the mentioned disadvantages, in this paper, we propose an efficient and privacy-preserving top-k query scheme over vertically distributed data. Specifically, we first design a data filtering technique to reduce the number of transmitted data from each data source to the centralized entity, which can greatly reduce the communication overhead and computational cost. Then, we propose a privacy-preserving top-k query scheme over encrypted data by deploying the homomorphic encryption technique, which can well preserve the private information and achieve the functionality at the same time. Besides, security analysis shows that the proposed scheme is privacy-preserving and performance evaluation validates the efficiency of the proposed scheme.

Abstract With the development of Lie theory, Lie groups have attained profound significance in several branches of Mathematics and Physics. In Lie theory, the matrix exponential plays a crucial role between Lie groups and Lie algebras. Meanwhile, as the finite analogue of Lie groups, finite groups of Lie type have potential applications in cryptography due to their unique mathematical structures. In this paper, we first put forward a novel idea of designing cryptosystems based on Lie theory. First of all, combing with discrete logarithm problem and group factorization problem, we proposed several new intractable assumptions based on the matrix exponential in finite groups of Lie type. Subsequently, in analog with Boyen’s scheme (Asiacrypt 2007), we designed a public-key encryption scheme based on the non-abelian factorization problem in finite groups of Lie type. Finally, our proposal was proved to be indistinguishable against adaptively chosen-ciphertext attack in the random oracle model. It is encouraging that our scheme also has the potential to resist against Shor’s quantum algorithm attack.

Big data mining-driven disease risk prediction has become one of the important topics in the field of e-healthcare. However, without the security and privacy assurances, disease risk prediction cannot continue to flourish. To address this challenge, in this paper, an efficient and privacy-preserving disease risk prediction scheme for e-healthcare is proposed, hereafter referred to as EPDP. Compared with the up-to-date works, the proposed EPDP comprehensively achieves two phases of disease risk prediction, i.e., disease model training and disease prediction, while ensuring the privacy preservation. Specifically, a super-increasing sequence is combined with a homomorphic cryptographic algorithm to efficiently extract the symptom set of each disease in the phase of disease model training. Bloom filter technique is introduced to compute the prediction result in the phase of disease risk prediction. Besides, extensive performance evaluations demonstrate that our proposed EPDP attains outstanding efficiency advantage over the state-of-the-art in terms of both computational and communication overheads, and hence our EPDP is more suitable for real-time e-healthcare, especially medical emergency.

Blockchain, as one of the crypto-intensive creatures, has become a very hot topic recently. Although many surveys have recently been dedicated to the security and privacy issues of blockchains, there still lacks a systematic examination on the cryptographic primitives in blockchains. To this end, we in this paper conduct a systematic study on the cryptographic primitives in blockchains by comprehensive analysis on top-30 mainstream cryptocurrencies, in terms of the usages, functionalities, and evolutions of these primitives. We hope that it would be helpful for cryptographers who are going to devote themselves to the blockchain research, and the financial engineers/managers who want to evaluate cryptographic solutions for blockchain-based projects.

Data sharing in cloud storage is receiving substantial attention in information communications technology because it can provide users with efficient and effective storage services. To protect the confidentiality of the shared sensitive data, cryptographic techniques are usually applied. However, the data protection is still posing significant challenges in cloud storage for data sharing. Among them, how to protect and revoke the cryptographic key is the fundamental challenge. To tackle this, we propose a new data protection mechanism for cloud storage, which holds the following properties. First, the cryptographic key is protected by the two factors. Only if one of the two factors works, the secrecy of the cryptographic key is held. Second, the cryptographic key can be revoked efficiently by integrating the proxy re-encryption and key separation techniques. Finally, the data is protected in a fine-grained way by adopting the attribute-based encryption technique. Furthermore, the security analysis and performance evaluation show that our proposal is secure and efficient, respectively.

Distributed energy resources (ERs), featured with small-scale power generation technologies and renewable energy sources, are considered as necessary supplements for smart grid. To ensure that merged resources contribute effectively to the grid, data generated by consumer side should be shared among the ERs. However, it also introduces challenges of the protection of consumer privacy. To address these difficulties, we propose a new framework to share data in smart grid by leveraging new advances in homomorphic encryption and proxy re-encryption. Our proposed framework allows ERs to analyze consumer data while ensuring consumer privacy. An additional benefit of our proposed framework is that consumer data is transmitted over the smart grid only once. Furthermore, we present a concrete scheme falling into the proposed framework. Extensive analysis shows that the concrete scheme is secure and efficient.

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 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.

Association rule mining and frequent itemset mining are two popular and widely studied data analysis techniques for a range of applications. In this paper, we focus on privacy-preserving mining on vertically partitioned databases. In such a scenario, data owners wish to learn the association rules or frequent itemsets from a collective data set and disclose as little information about their (sensitive) raw data as possible to other data owners and third parties. To ensure data privacy, we design an efficient homomorphic encryption scheme and a secure comparison scheme. We then propose a cloud-aided frequent itemset mining solution, which is used to build an association rule mining solution. Our solutions are designed for outsourced databases that allow multiple data owners to efficiently share their data securely without compromising on data privacy. Our solutions leak less information about the raw data than most existing solutions. In comparison to the only known solution achieving a similar privacy level as our proposed solutions, the performance of our proposed solutions is three to five orders of magnitude higher. Based on our experiment findings using different parameters and data sets, we demonstrate that the run time in each of our solutions is only one order higher than that in the best non-privacy-preserving data mining algorithms. Since both data and computing work are outsourced to the cloud servers, the resource consumption at the data owner end is very low.

Due to the convenience, the data sharing in cloud computing via mobile devices has become more and more popular. However, data confidentiality and online computational cost still present practical concerns to the deployment of data sharing in cloud computing for mobile devices. Existing data sharing protocols in cloud computing either cannot support the flexible sharing style for the encrypted data, or suffer from massive online computational cost that scales with the complexity of the access policy. In this paper, to cope with these challenging concerns, we propose a new data sharing protocol for cloud computing by using a new cryptographic primitive named online/offline attribute-based proxy re-encryption and the transform key technique. To the best of our knowledge, the proposed data sharing protocol is the first one featuring with fine-grained access control, flexible sharing, data confidentiality, and minimum online computational cost on the user side at the same time. Furthermore, the proposed online/offline attribute-based proxy re-encryption scheme may be of independent interest. At last, extensive analysis shows that our proposed data sharing protocol is secure in terms of data confidentiality, and suitable for mobile devices in terms of online computational cost.

Summary Network coding is a new routing technique that can improve a network's throughput. The key idea is to allow network routers to code the received packets before transmission. However, network coding is vulnerable to pollution attacks where malicious node(s) can flood the network with invalid packets and prevent the receiver from the right decoding. Network coding signature offers a good solution to this problem. Nevertheless, existing network coding signature schemes cannot either be proven secure in the standard model, support multiple sources, or in‐time signing. In this paper, we propose a new network coding signature scheme to solve the aforementioned problems. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, we propose a privacy-preserving framework, called PLAM, for local-area mobile social networks. The proposed PLAM framework employs a privacy-preserving request aggregation protocol with k-Anonymity and l-Diversity properties while without involving a trusted anonymizer server to keep user preference privacy when querying location-based service (LBS), and integrates unlinkable pseudo-ID technique to achieve user identity privacy, location privacy. Moreover, the proposed PLAM framework also introduces the privacy-preserving and verifiable polynomial computation to keep LBS provider's functions private while preventing the provider from cheating in computation. Detailed security analysis shows that the proposed PLAM framework can not only achieve desirable privacy requirements but also resist outside attacks on source authentication, data integrity and availability. In addition, extensive simulations are also conducted, and simulation results guide us on how to set proper thresholds for k-anonymity, l-diversity to make a tradeoff between the desirable user preference privacy level and the request delay in different scenarios.

In this paper, we propose a fine-grained privacy-preserving location-based service (LBS) framework, called FINE, for mobile devices. It adopts the data-as-a-service (DaaS) model, where the LBS provider publishes its data to a third party (e.g., cloud server) who executes users' LBS queries. The proposed FINE framework employs a ciphertext-policy anonymous attribute-based encryption technique to achieve fine-grained access control, location privacy, confidentiality of the LBS data and its access policy, and accurate LBS query result while without involving any trusted third party. Moreover, the proposed FINE framework also integrates the transformation key and proxy re-encryption to migrate most of computation-intensive tasks from the LBS provider and users to the cloud server. This property keeps mobile devices away from massive resource-consuming operations. Extensive analysis shows that our proposed FINE framework is secure and highly efficient for mobile devices in terms of computation and communication cost.

Cloud-assisted mobile health (mHealth) monitoring, which applies the prevailing mobile communications and cloud computing technologies to provide feedback decision support, has been considered as a revolutionary approach to improving the quality of healthcare service while lowering the healthcare cost. Unfortunately, it also poses a serious risk on both clients' privacy and intellectual property of monitoring service providers, which could deter the wide adoption of mHealth technology. This paper is to address this important problem and design a cloud-assisted privacy preserving mobile health monitoring system to protect the privacy of the involved parties and their data. Moreover, the outsourcing decryption technique and a newly proposed key private proxy reencryption are adapted to shift the computational complexity of the involved parties to the cloud without compromising clients' privacy and service providers' intellectual property. Finally, our security and performance analysis demonstrates the effectiveness of our proposed design.

ABSTRACT Proxy re‐encryption (PRE) is a public key encryption that allows a semi‐trusted proxy with some information (a.k.a., re‐encryption key) to transform a ciphertext under one public key into another ciphertext under another public key. Because of this special property, PRE has many applications, such as the distributed file system. Some of these applications demand that the underlying PRE scheme is anonymous under chosen‐ciphertext attacks (CCAs); that is, the adversary cannot identify the recipient of the original/transformed ciphertext, even if it knows the PRE key and can launch the CCA. However, to the best of our knowledge, none of the existing PRE schemes satisfy this requirement. In this work, we propose the first anonymous PRE with CCA security and collusion resistance. Our proposal is proved in the random oracle model based on the DDH assumption. Copyright © 2011 John Wiley & Sons, Ltd.

We propose a solution for electronic identity cards that makes it possible to create multiple identities for a person creating digital signatures with just one private key stored on his personal electronic identity card. The public keys corresponding to this secret key belong to different sectors, devoted to different applications or activity areas (like private use and signing in behalf of a company or an authority). We provide strong privacy guarantees called unlinkability. It means that given two public keys from different sectors and some signatures corresponding to these keys, it is unfeasible to say if these keys (and signatures) come from the same person. The proof is performed in the random oracle model and reduces linkability to the Decisional Diffie-Hellman Problem. Our proposal extends the idea of Restricted Identification introduced in German personal identity cards from unlinkable sector identification to unlinkable sector signatures.

We consider a scenario in which Alice entitles Bob to serve as her proxy with the right to sign one out of two possible documents, say m1 and m2. The protocol guarantees that the data given to Bob cannot be recognized as signatures of m1 and m2, unless Bob transforms them with his private key. The most important feature is, however, then if Bob finalizes both signatures (of m1 and of m2) - violating the delegated rights, then Bob's private key will be revealed to Alice. So we propose an undeniable proof of misbehavior instead of other means that turn out to be less effective and more difficult to implement.

This paper presents the first multi-use unidirectional proxy re-encryption scheme proven-secure against chosenciphertext attacks and collusion attacks in the standard model. Although our proposal features a linear ciphertext size and decryption time in the number of translations, we emphasize that it is the first multi-use and unidirectional realization of the primitive satisfying the chosen-ciphertext security and collusion resistance. The proposal gives an answer to the problem proposed by Canetti and Hohenberger at ACM CCS 2007.

This paper proposes a new cryptographic primitive, named identity-based conditional proxy re-encryption (IBCPRE). In this primitive, a proxy with some information (a.k.a. re-encryption key) is allowed to transform a subset of ciphertexts under an identity to other ciphertexts under another identity. Due to the specific transformation, IBCPRE is very useful in encrypted email forwarding. Furthermore, we propose a concrete IBCPRE scheme based on Boneh-Franklin identity-based encryption. The proposed IBCPRE scheme is secure against the chosen ciphertext and identity attack in the random oracle.

To construct a suitable and secure proxy re-signature scheme is not an easy job, up to now, there exist only a few schemes. None of these schemes is unidirectional identity-based proxy re-signature, where a semi-trusted proxy can transform a signature under an identity to another signature under another identity on the same message, while the proxy cannot generate any signature on behalf of any of these two identities. In this paper, based on Schnorr's signature and Libert-Vergnaud proxy re-signature, we propose the first unidirectional identity-based proxy re-signature, which is existentially unforgeable in the random oracle model based on the extended computational Diffie-Hellman assumption.

Abstract By allowing a proxy to blindly perform meaningful transformations from one ciphertext to another, proxy re‐encryption (PRE) is an important cryptographic primitive in many applications, such as encrypted email forwarding and distributed file system. Due to its usefulness, various PRE schemes have been proposed; however, only one can simultaneously achieve chosen ciphertext security (CCA security) and collusion‐resistance. When such schemes are deployed, lack of CCA security will cause secret leaking, and lack of collusion‐resistance will cause non‐repudiation damage. In this paper, we propose a novel approach (denoted SCCR) to simultaneously achieve CCA security and collusion‐resistance in PRE, which faces daunting new challenges. We address these challenges by using CCA‐secure (2, 2) threshold cryptosystem to obtain public verifiability, and the method of key management in identity‐based encryption (IBE) to achieve collusion‐resistance. These two novel techniques have not been yet used in any previous PRE schemes. A unique characteristic of SCCR is that it is a generic construction which has more advantages than a concrete PRE scheme does. Copyright © 2009 John Wiley & Sons, Ltd.

Attribute based proxy re-encryption scheme (ABPRE) is a new cryptographic primitive which extends the traditional proxy re-encryption (public key or identity based cryptosystem) to the attribute based counterpart, and thus empower users with delegating capability in the access control environment. Users, identified by attributes, could freely designate a proxy who can re-encrypt a ciphertext related with a certain access policy to another one with a different access policy. The proposed scheme is proved selective-structure chosen plaintext secure and master key secure without random oracles. Besides, we develop another kind of key delegating capability in our scheme and also discuss some related issues including a stronger security model and applications.

Abstract Deniable authentication, which was introduced to realize secure online negotiation, has been an extensive topic of research. Wang et al. proposed an efficient deniable authentication protocol based on ElGamal cryptography. However, in this article, we show that Wang et al.'s protocol (WLT protocol) is vulnerable to the person‐in‐the‐middle (PIM) attack. An adversary who launches a PIM attack can impersonate a valid user to communicate with the verifier, which will eventually lead to the failure of an online negotiation. To mitigate this security breach, we propose an improved deniable authentication protocol based on Wang et al.'s scheme. Further, a detailed security analysis is conducted to verify the proposed scheme. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 48(4), 179–181 2006