Data aggregation, as a data processing technique, facilitates accurate diagnosis in the Healthcare Internet of Things (HIoT) by integrating multi-source heterogeneous health data. However, achieving efficient and secure aggregation of multi-dimensional medical data remains challenging, particularly when simultaneously preserving location privacy and providing fair, quality-driven incentives. To address these issues, this paper proposes a Quality-Aware Secure Multi-Dimensional Data Aggregation scheme with Location Privacy for HIoT (QSDA). First, the scheme employs inner product encryption to support aggregation task matching without revealing users’ actual coordinates, and further integrates symmetric homomorphic encryption with super-increasing sequences to enable one-stop compressed aggregation of multi-dimensional data, thereby effectively supporting common statistical operations such as mean and variance. Second, it introduces a data quality incentive mechanism based on offset metrics, while leveraging blockchain auditing to ensure the traceability of the aggregation process and the verifiability of the aggregation results. Finally, security analysis and performance evaluation demonstrate the scheme’s effectiveness and efficiency.

Federated Learning (FL) has emerged as a promising paradigm to address data silos and privacy concerns in artificial intelligence applications. It shows great potential in the Internet of Vehicles (IoV), where sensitive vehicle data must remain local. However, FL is highly vulnerable to model poisoning attacks, especially under non-IID distributions and high malicious participation. Existing defenses, most relying on gradient similarity across clients, often fail under such heterogeneous settings. To address this, we propose RDFDS, a robust defense framework for FL in IoV. RDFDS consists of two tightly coupled modules: (1) a fast distillation-based synthesis module that accelerates knowledge distillation via a lightweight preprocessing step, suitable for real-time vehicular environments; (2) a Gas-KMeans clustering aggregation module that adaptively assigns aggregation weights by identifying the benign majority through Gap Statistics, preserving reliable contributions without requiring global or cross-client reference data. Extensive experiments on MNIST, CIFAR-10, and GTSRB demonstrate that RDFDS consistently outperforms existing defenses in accuracy and robustness, particularly under extreme non-IID conditions and high malicious participation, highlighting its practicality for real-world IoV scenarios.

With the advent of cloud computing, traditional machine learning (ML) are migrating into cloud-based ML day by day following the concept of machine learning as a cloud service, which enables multiple entities to contribute to and benefit from shared datasets and models. As well as training the linear classification model, training the nonlinear classification model is also an essential task in cloud-based ML. However, this task commonly involves learning knowledge from different datasets provided by various entities, which often contain sensitive information like patients' physiological indices. Therefore, it gives rise a natural question how to allow multiple users collaboratively participating in a nonlinear classification task while preserving these datas' privacy. As a promising cryptographic tool, the concept of unbounded multiinput functional encryption can be developed to answer such a question, such as google search engines are running over this concept-based ML approaches. However, most of existing approaches are derived from this concept with inner product functionality, specifying for a linear classification model and thus fails to cope with a non-linear classification one. In this paper, we introduce an advanced cryptographic concept called unbounded multi-input quadratic functional encryption, and give a concrete construction which allows arbitrary number of users participating in the classifying tasks with a nonlinear classification model but without divulging their private data. Moreover, we provide a strict mathematical security proof under a well-defined security model as well as some security attacks are analyzed, followed by an experimental analysis and comparison on a real dateset as well as a practical use case to demonstrate our scheme's performance.

With the increasing prevalence of outsourcing images to cloud servers, privacy-preserving content-based image retrieval (CBIR) has attracted significant research attention. Existing privacy-preserving CBIR schemes often prioritize retrieval speed by adopting methods that provide weak privacy guarantees and low-dimensional image features, which inevitably compromises security and retrieval accuracy. Additionally, most solutions directly employ CNN models pre-trained on public datasets for feature extraction, neglecting domain adaptation problem. To address these limitations, we propose SecOIR, a secure outsourced image retrieval scheme based on deep hashing networks, which achieves provable security under the semi-honest adversary model while hiding access patterns. Furthermore, domain adaptation is resolved through fine-tuning of feature extraction models. Experimental results demonstrate that SecOIR outperforms state-of-the-art schemes by 11%-12% in accuracy under identical datasets and configurations, while maintaining practical efficiency. To achieve SecOIR, we propose two modified function secret sharing (FSS) schemes that overcome the limited compatibility of the original FSS schemes with replicated secret sharing (RSS). Then, building upon the modified FSS schemes and RSS, we design a series of efficient sub-protocols. Benchmark tests reveal that our sub-protocols surpass existing mainstream solutions in efficiency, which can also serve as independent contributions to secure multi-party computation protocol design.

The rapid growth of sensitive cross-domain data, such as electronic health records and genomic sequences in healthcare, presents significant opportunities for large-scale, multi-institutional collaborative analysis. Meeting stringent privacy regulations while utilizing data has become a critical challenge. Private set operations (PSO) play a crucial role to address this challenge. PSO protocols enable privacy-preserving data alignment across parties (such as interinstitutional data matching based on private set intersection (PSI)) and secure data aggregation (such as federated data aggregation through private set union (PSU)), providing fundamental support for cross-domain data collaboration. This type of technology is not only applicable to multi-center medical research but also has broad value in other scenarios requiring confidential data sharing. However, existing delegated/outsourced PSO schemes face two key limitations: (1) high client-side preprocessing overhead, requiring clients to expensively mask private data before uploading; (2) performance and single-point dependency bottlenecks in client-assisted computation where one client must act as a computational leader. To address these issues, we propose a secret-shared PSO framework for lightweight clients. In our scheme, the clients can go offline while servers perform all computations, significantly reducing clients’ burden. Notably, our protocol can be extended to support multi-party settings, making it well-suited for collaborative research across multiple institutions. In addition, we prove the security of all constructions under the semi-honest model. Experiments show that when the set sizen≥ 216, our protocol has a significant advantage and is well-suited for lightweight client that holds a large set.

With the increasing availability of large-scale spatiotemporal data from location-based services, trajectory publishing has become essential for data-driven analysis and intelligent applications. However, insufficient protection of trajectory location data may result in the disclosure of user privacy and social relationship information. To address this issue, we propose a semantic-aware privacy-preserving trajectory data publishing scheme (SAPP). First, a sliding-window algorithm is employed to extract stay points as key semantic locations and to generate a uniformly sampled set of candidate obfuscation points. Then, a semantic-aware scoring function is designed to probabilistically select candidate points that preserve semantics while avoiding sensitive regions. Furthermore, SAPP computes the sensitivity of each location based on semantic frequency and dynamically allocates the privacy budget. Finally, random noise is added to candidate trajectories using the Laplace mechanism. Through a dual-perturbation mechanism, spatial correlations in sensitive regions are weakened. Security analysis and experimental results further demonstrate that, compared with existing approaches, SAPP reduces TPPS and SFRR by up to 18% and 14%, respectively, indicating stronger resistance against trajectory inference and semantic leakage attacks while maintaining high data utility and time efficiency.

Skyline query is a fundamental technique in multi-criteria decision-making, aiming to extract “optimal” results that are not dominated by any other data points across all attributes. It has significant value in applications that require trade-offs among multiple criteria. However, existing skyline query methods face two critical limitations: (i) conventional approaches adopt fixed dominance relationships, making it difficult to capture personalized user preferences; and (ii) cloud-based deployment models risk exposing sensitive data and query logic, making it difficult to ensure data privacy and protect query patterns while maintaining efficiency. To address these issues, we propose Privacy-Preserving User-Defined Skyline Query (PUDSQ), a novel privacy-preserving user-defined skyline query framework, which integrates efficient cryptographic techniques–secret sharing (SS) and function secret sharing (FSS)–with a secure database shuffling mechanism to achieve efficient query processing while ensuring robust privacy guarantees. PUDSQ introduces three main innovations: (i) a privacy-preserving filtering framework based on FSS provides dual protection for both data content and user preferences, effectively concealing database content and query logic; (ii) an FSS-based secure protocol suite supporting user-defined attribute retrieval, constrained-region retrieval, and secure skyline filtering; and (iii) a high-dimensional data processing strategy that integrates dimensionality reduction with an Sort-Filter-Skyline (SFS)-based presorting approach to address the high-dimensional data processing challenge and significantly improve efficiency. Experimental results demonstrate that, under equivalent security guarantees, PUDSQ reduces query latency by 8%-90% compared with state-of-the-art solution, with particularly notable advantages in high-dimensional scenarios, achieving an effective efficiency-privacy trade-off.

Location sharing, as an essential component of mobile applications, helps mobile users share location information and enhance their community connections. However, users may be reluctant to share their locations with personal privacy concerns, as anyone including location server who knows these locations can infer much sensitive information about users through analyzing these locations’ information plus their background knowledge. Therefore, it poses a natural question for mobile location-sharing applications how to share users’ locations without any breach of their privacy. To answer this question, in this article, we describe two practical attribute-based encryption schemes served privacy-preserving mobile location-sharing applications. Our proposals are quite suitable for such a mobile application—location sharing with common interests since in our designs users’ interests are also taken into consideration as well as location information. In particular, our two schemes have a higher performance in the sense that in our first construction both ciphertexts and private keys are of constant size simultaneously, and our second construction is an extension of the first that provides a tradeoff between ciphertext size and public-key size. Therefore, the two schemes we designed in this work are quite practical in mobile applications which are often equipped with limited transmission or storage resources. Finally, we offer a formal security proof under a well-defined security model, followed by an experimental evaluation and a theoretical performance comparison.

The large volume of spatial feature data generated by Internet of Things (IoT) devices is increasingly utilized in business location planning (BLP) services. reverse nearest neighbor (RNN) query techniques assist BLP in achieving more efficient business decisions by identifying candidate locations that are most attractive to users. However, existing RNN query schemes face significant challenges. First, there are some issues with data security and result integrity, as cloud servers can be both untrustworthy and malicious. Second, traditional query schemes commonly assume that the data users (DUs) are fully trusted and hold the key provided by the data owner (DO, IoT device users). In practice, however, once a DU’s key is compromised, the dataset of the DO is at risk. Regarding the above issues, this article proposes a privacy-preserving spatial feature data query scheme that supports multiple users without requiring key sharing. The proposed scheme is demonstrated using RNN queries, which are highly applicable in BLP services. Specifically, we first design a Quad-Tree for indexing spatial feature data. Then, we embed replicated secret sharing (RSS) technique into distributed two trapdoors public-key cryptosystem (DT-PKC) for key sharing. And based on this, a set of secure protocols that satisfy the mutual independence of DUs are designed for computing spatial distance and feature similarity. Finally, rigorous theoretical proofs and extensive experimental evaluations ensure the security and effectiveness of the scheme.

Cross-modal retrieval is a technique that uses one modality to query another modality in multimedia data (e.g., retrieving images based on text, or retrieving text based on images). It can break down the barriers between different modalities and achieve seamless information connection. Secure cross-modal retrieval focuses on privacy issues in cross-modal retrieval, including private data of data owners and private query requests of users. Current work on secure cross-modal retrieval protects private information through homomorphic encryption, which makes the efficiency of the retrieval phase not ideal. Therefore, the conflict between retrieval efficiency and security has become an important issue that needs to be resolved in secure cross-modal retrieval. We propose a scheme to achieve secure cross-modal retrieval in the form of secret sharing in the IoT environment. In the scheme, the data owner (DO) can secretly divide all the original data into two parts and upload them to two non-collusive cloud servers respectively. The servers store the data and provide cross-modal retrieval for users. The security of the scheme is proved under semi-honest model, and the experiments show that our scheme is more efficient than previous work in the search phase. When the query dimension is 512 and the number of latent factors is 500, the search time is reduced by more than half compared with previous work.

We propose a privacy-preserving machine learning scheme based on the cloud-edge–end architecture to address issues like weak computing power of Internet of Things (IoT) terminals, poor communication quality, and heavy cloud server burdens in traditional frameworks. Edge servers aggregate and forward terminal data, relieving terminals of heavy communication tasks and undertaking part of the computing tasks, which reduces the burden on cloud servers and improves system response speed. For privacy protection, we flexibly use homomorphic encryption and secret sharing techniques, and dynamically add differential privacy noise to resist member inference attacks. Task allocation is coordinated between different layers to optimize computing overhead. Shallow model training is performed on edge servers using homomorphic encryption, while deep model training is conducted on cloud servers using secret sharing. To achieve the conversion from homomorphic ciphertext to secret sharing shares, we design a distributed decryption protocol. Experimental results show our scheme reduces computation overhead by 20%–30% compared to existing privacy-preserving machine learning schemes based on the cloud-edge–end framework, while maintaining privacy protection throughout all stages.

The growth of cloud computing has led to the widespread use of location-based services, such as spatial keyword queries, which return spatial data points within a given range that have the highest similarity in keyword sets to the user’s. As the volume of spatial data increases, providers commonly outsource data to powerful cloud servers. Because cloud servers are untrustworthy, privacy-preserving keyword query schemes have been proposed. However, existing schemes consider only location queries or exact keyword matching. To address these issues, we propose the Privacy-Preserving Spatial Keyword Similarity Query Scheme (PPSKSQ), designed to search for spatial data points with the highest similarity while protecting the privacy of outsourced data, query requests, and results. First, we design two sub-protocols based on improved symmetric homomorphic encryption (iSHE): iSHE-SC for secure size comparison and iSHE-SIP for secure inner product computation. Then, we encode range information and integrate it with a quadtree to construct a novel index structure. Additionally, we use the Jaccard to measure similarity in conjunction with the iSHE-SC protocol, transforming similarity comparison into a matrix trace operation. Finally, rigorous security analysis and extensive simulation experiments confirm the flexibility, efficiency, and scalability of our scheme.

As data continues to grow at an unprecedented rate and informationization accelerates, concerns over data privacy have become more prominent. In image classification tasks, the challenge of insufficient labeled data is common. Transfer learning, an effective and important machine learning method, can address this issue by leveraging knowledge from the source domain to enhance performance in the target domain. However, existing privacy-preserving transfer learning schemes continue to face challenges related to low security and multiple rounds of communication. In the following works, we design a three-party privacy-preserving transfer learning protocol based on the Joint Distributed Adaptation (JDA) algorithm, which ensures malicious security under an honest majority model. To realize this protocol, we designed a series of sub-protocols for constant-round communication, including distributed solving of eigenvalues and eigenvectors based on replicated secret sharing techniques. Compared to existing work, our protocol requires fewer rounds and satisfies malicious security. We provide formal security proofs for the designed protocol and assess its performance using real datasets. Our protocol for computing the eigenvalues of matrices in a given dimension is approximately 2.5 times faster than existing methods. The results of the experiments demonstrate both the security and effectiveness of the proposed approach.

Abstract The encryption of user data is crucial when employing electronic health record services to guarantee the security of the data stored on cloud servers. Attribute-based encryption (ABE) scheme is considered a powerful encryption technique that offers flexible and fine-grained access control capabilities. Further, the multi-user collaborative access ABE scheme additionally supports users to acquire access authorization through collaborative works. However, the existing multi-user collaborative access ABE schemes do not consider the different weights of collaboration users. Therefore, using these schemes for weighted multi-user collaborative access results in redundant attributes, which inevitably reduces the efficiency of the ABE scheme. This paper proposes a revocable and verifiable weighted attribute-based encryption with collaborative access scheme (RVWABE-CA), which can provide efficient weighted multi-user collaborative access, user revocation, and data integrity verification, as the fundamental cornerstone for establishing a robust framework to facilitate secure sharing of electronic health records in a public cloud environment. In detail, this scheme employs a novel weighted access tree to eliminate redundant attributes, utilizes encryption version information to control user revocation, and establishes Merkle Hash Tree for data integrity verification. We prove that our scheme is resistant against chosen plaintext attack. The experimental results demonstrate that our scheme has significant computational efficiency advantages compared to related works, without increasing storage or communication overhead. Therefore, the RVWABE-CA scheme can provide an efficient and flexible weighted collaborative access control and user revocation mechanism as well as data integrity verification for electronic health record systems.

As a practical machine learning method, the K-nearest neighbors (KNN) classification has received widespread attention. The achievement of the KNN classification relies heavily on a large amount of labeled data. However, in the real world, data is often held by different data owners. How to realize efficient joint computing among multiple data owners under the premise of protecting data security and privacy is an urgent problem to be solved. In this paper, we construct a secure multi-party KNN classification scheme (SecKNN) based on function secret sharing (FSS) technology, which is a novel cryptographic primitive and can achieve cheap communication and computation costs for secure computation. Compared with the existing works, our scheme dramatically reduces computational overhead and runs roughly 50.8 times faster than the state-of-the-art approach. Furthermore, our scheme supports the secure KNN classification under general distance functions such as Euclidean distance, Manhattan distance, and Hamming distance. To implement our SecKNN scheme, we design two efficient FSS schemes for Hamming distance function, which implements secure two-party and multi-party Hamming distance computation in a single round. They can be considered as independent research results. Finally, we give formal security proofs for the proposed protocols and validate the effectiveness and efficiency of our protocols through experiments.

With the rapid development of intelligent transportation systems (ITS), more and more intelligent applications for ITS have received widespread attention, such as the vehicle detection, inference of typical routes, and traffic forecasting. In these applications, deep learning is widely used as a key artificial intelligence technology. However, most ITS providers fail to collect enough labeled traffic data for model training. As a complement to deep learning, transfer learning is an effective way to solve the scarcity of labeled data, which can transfer knowledge from labeled datasets to unlabeled datasets, thus improving the accuracy of prediction and classification. Nevertheless, when the labeled dataset and the unlabeled dataset are held by different entities, it is still unrealistic for two mutually distrustful entities to cooperate in transfer learning regarding data security and privacy preservation. Although some existing works provide privacy-preserving transfer learning methods, such methods fail to apply to traffic data with high sample dimensions due to their high computational cost and round complexity. To address this problem, we design an efficient privacy-preserving distributed transfer learning protocol, which is appropriate for traffic data. Compared to existing works, our protocol addresses the privacy-preserving problem of transfer learning for traffic data with high sample dimensions. In addition, our protocol has fewer interaction rounds and can be proved in the semi-honest model. Finally, we validate the effectiveness, efficiency and security of the proposed protocol via experiments. Furthermore, we show the application of the proposed protocol in intelligent transportation systems.

Although secure multi-party computation breaks down data barriers, its utility is reduced when participants have limited computation and communication resources. To make secure multi-party computation more practical, there exists an approach to distribute users' private inputs to multiple servers in a secret sharing manner, and the servers accomplish secure computation tasks through interaction. We propose a new secure computation framework that enables the detection of malicious cloud servers by introducing homomorphic MACs. We utilize pairing-based homomorphic commitments to record MACs on a bulletin board, providing public verifiability while reducing the computation burden on the cloud servers. Additionally, our framework not only supports the underlying general computation, but also prepares for various types of nontrivial high-level operations, such as comparison and bit decomposition. We design a smart payment platform enabling fair payment with the help of smart contracts to protect the rights of both data owners and cloud service providers. Compared to previous works, our framework breaks the limitations of servers being restricted to semi-honest or even honest and provides public verifiability. Performance evaluations demonstrate satisfactory computation and communication efficiency during the online phase of our system.

While online medical diagnosis provides significant convenience to users, it also incurs the risk of privacy breaches, which inspired the emergence of various privacy-preserving online medical schemes. Nonetheless, existing schemes either compromise partial privacy to third parties or rely on cryptographic methods with high computational complexity. In particular, they do not anticipate user’s disputes to the extent that there is no audit process to guarantee the correctness of the diagnosis results and the fairness of the schemes. Consequently, we propose an efficient and privacy-preserving online medical diagnosis scheme based on additive secret sharing (ASS). First, the anonymity of the user is provided in the medical diagnosis process, which ensures that the cloud cannot link the diagnosis results to the user. Then, we devise a minimum value protocol and a range comparison protocol to enhance the security of the online diagnosis. In addition, considering user’s disputes that arise in realistic scenarios (e.g., malicious users may cheat the diagnosis system for personal benefits), we construct a blockchain-based audit process to detect user’s behaviors and settle controversies. Finally, we demonstrate the security and efficiency of the proposed scheme with theoretical analysis and experimental evaluation.

Abstract Lattice-based online/offline signature is attractive for the merit of resisting quantum attacks besides the short online response time. Prior to this work, the hash-sign-switch paradigm lattice-based online/offline signatures usually increase the length of each signature, and the Fiat–Shamir candidates are highly inefficient due to multiple aborts in online signing phase. In this work we mainly address its efficient issue and propose a new paradigm of its construction in the perspective of abort. In this paradigm, one tries to remove one or more aborts from online to offline signing phase by $\Gamma $-transformation. Specifically, this work proposes an efficient lattice-based online/offline signature scheme with fewer online aborts and thus allows the signer to obtain a valid signature by fewer online repetitions. Through this way, the resulting scheme can reduce much online signing time with the same signature size. The performance evaluation shows that our scheme is efficient and practical.

Summary Difficulties in financing and low utilization of funds are main financial problems that plague the development of small and medium‐sized enterprises. The key to solving this problem lies in opening up the social data circulation between enterprises. It is a good solution for enterprises with frequent data interactions to form groups. Using group loans, the borrowing enterprises could solve the funding difficulties and the loan enterprises could improve the utilization rate of funds. In this article, we construct a group loan system based on blockchain technology, which can promote the free flow of funds among enterprises in the group. We combine the blockchain with the trusted execution environment to realize the automatic determination of loan conditions and realize the automatic execution of smart contracts. We also use the linkable group signature technology to ensure the traceability of loan users while protecting the anonymity. In addition, we use homomorphic encryption technology to make the statement confidential and computable.

Summary Compared with traditional voting methods, electronic voting can effectively avoid the phenomenon of fraud for personal gains in various links, it is faster and more accurate in the tallying stage. However, many electronic voting systems have many problems such as inability to verify ballots, easy to be forged, and low computing efficiency. We propose an electronic voting protocol based on homomorphic signcryption and blockchain. The protocol makes the voting process public through blockchain and replaces the traditional trusted third party with the smart contract. It uses the homomorphic encryption algorithm and the homomorphic signcryption algorithm to encrypt and sign the ballot and uses their aggregation properties to perform homomorphic tally on the encrypted votes. This not only reduces the excessive burden on the voters but also improves the voting efficiency. At the same time, it can satisfy the security of electronic voting, and the amount of calculation is small, so it is more convenient and flexible to use in large‐scale voting.

Traffic density monitoring is an important method to predict road traffic conditions, which can bring some convenience to people's travel in daily life. The common method of traffic density monitoring is to collect and process the location information uploaded by vehicles, but the information of these vehicle location contains a large amount of personal privacy information of vehicle owners, and there is a risk of privacy disclosure. In this paper, we propose a traffic density monitoring system by adding a pseudonym server and a location anonymisation server; the identity information and location information of the vehicles are saved separately. The system can protect both the location privacy of vehicles and the query privacy of users. To prevent dummy locations from being filtered, we calculate the probability distribution of historical location service requests to generate location anonymous sets, which can improve the success rate of anonymity. The location anonymisation server uses the location anonymous set instead of the real location of the vehicle to send to the location-based service provider, which can increase the location privacy security of the vehicle. According to the experimental results of this paper, compared with SimpMaxMinDistds algorithm and MMDS algorithm, our system has better location anonymous set generation efficiency and location privacy protection level.

In numerous data application scenarios, various data can be represented in the form of data sets, and the intersection is often the common concern of multiple users. Using private set intersection (PSI) protocol, users can securely compute the intersection of their sets without disclosing their private input and other additional information. At the same time, there is also a strong practical demand for statistical analysis of intersection data. However, when multiple parties are involved, the efficiency of the multiparty PSI protocol decreases dramatically as the number of users increases. In this paper, we construct a novel server-aided multiparty PSI protocol, which can transform the complex multiparty computation problem into an efficient two-party computation problem. In addition, we design a series of server-aided party PSI statistical protocols to compute the statistics of the intersection elements, such as the sum, average, variance, range (maximum, minimum), and the cardinality of intersection (the size of intersection). In our protocol, the clients only need to upload their private data to the servers in blinded form and do not need to keep online during server computing. Experiments show that our protocol has high computation and communication efficiency and is suitable for lightweight clients. In addition, we also introduce an application of our protocols in the field of intelligent logistics.

With the rapid development of Internet and the widespread application of distributed computing, people enjoy various conveniences while at the same time their privacy has also been threatened. Secure multiparty computation (MPC) can solve the problem of how data owners who do not trust each other jointly compute in distributed scenarios. Using MPC technique, people can not only realize data joint computing, but also ensure data privacy. In most data application scenarios, private data held by different parties can often be represented by sets. To complete the relevant statistical computations of the intersection of two private sets, we propose a suite of protocols based on MPC. These protocols can compute the statistical functions of the associated data of the intersection, including cardinality, sum, average, variance, range, and so forth, without revealing any additional information other than the result. To achieve these functions, we design a private membership test protocol with the result as the arithmetic sharing value, called the arithmetic shared private membership test (ASPMT) protocol. On the basis of the ASPMT protocol, the size and other statistics of the intersection can be computed securely and efficiently. All fundamental computations are constructed based on secret sharing and oblivious transfer techniques. Thanks to the use of precomputation technique, all protocols are highly efficient.

Secure wildcard pattern matching (WPM) allows the pattern holder to obtain the matched positions without revealing pattern and text information about both parties. However, standard secure WPM may have limitations in practical applications, as users may prefer to have access to the actual data of the match in many scenarios. Fortunately, secure wildcard pattern matching with query (SWMQ) extends standard secure WPM by allowing the pattern holder to obtain the matched positions and the actual data, which has important applications in many scenarios, such as electronic healthcare and gene matching. This also motivates the research of SWMQ in this paper. In this study, we focus on the efficient construction of SWMQ in the semihonest adversary setting. First, we propose two new primitives, hereafter referred to as shared wildcard pattern matching (Sh-WPM) and choice-sharing oblivious transfer (CSOT). Furthermore, we propose an SWMQ protocol via Shared WPM and CSOT. In addition, we evaluate the performance of SWMQ. More specifically, the running time in local area network and wide area network settings is less than 0.4 and 2 s, respectively, when the text length is 2 16 ${2}^{16}$ and the pattern length is 2 12 ${2}^{12}$ . In fact, our evaluation results suggest that SWMQ is not only more broadly functional, but also comparable in efficiency to state-of-the-art approaches.

Convolutional neural network (CNN) is one of the representative models of deep learning, commonly used to analyze visual images. CNN model is more accurate when trained on large amounts of data from multiple sources, and the huge training cost makes the model much more valuable. However, data from various sources is often privacy-sensitive. Therefore, the privacy of these data should be protected during CNN model training and inference. In this paper, we propose an efficient and secure two-party computation (2PC) framework PPCNN for privacy-preserving CNN training and inference. Specifically, we use a new secret sharing technique introduced in ABY2.0 to securely compute various computational tasks involved in the CNN training and inference processes. This secret sharing technique can significantly reduce the communication overhead. Meanwhile, we assign these computationally intensive tasks to cloud servers to reduce the computational burden on local devices. We demonstrate the security of these protocols in the semihonest model. In addition, we use the MP-SPDZ library to simulate our PPCNN framework, and the experiments prove its high efficiency and accuracy.

Federated learning (FL), as a prospective way to process and analyze the massive data from the Internet of Things (IoT) devices, has attracted increasing attention from academia and industry. However, considering the unreliable nature of IoT devices, ensuring the efficiency of FL while protecting the privacy of devices’ input data is a challenging task. To address these issues, we propose a secure aggregation protocol based on efficient additive secret sharing in the fog-computing (FC) setting. As the secure aggregation is performed frequently in the training process of FL, the protocol should have low communication and computation overhead. First, we use a fog node (FN) as an intermediate processing unit to provide local services which can assist the cloud server aggregated the sum during the training process. Second, we design a light Request-then-Broadcast method to ensure our protocol has the robustness to dropped-out clients. Our protocol also provides two simple new client selection methods. The security and performance of our protocol are analyzed and compared with existed schemes. We conduct experiments on high-dimensional inputs, and our experimental results demonstrate about 24–$168\times $improvement in computation overhead and 87–$287\times $improvement in communication overhead compared to Google’s secure aggregation protocol (Bonwatiwz et al. CCS17).

Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and the requirement of high model performance, many applications in industry call for building a secure and efficient LR model for multiple parties. Most existing work uses either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they incur potential security risks. SS based methods have provable security, but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security. We then present the distributed implementation of CAESAR for scalability requirement. We have deployed CAESAR in a risk control task and conducted comprehensive experiments. Our experimental results show that CAESAR improves the state-of-the-art model by around 130 times.

With the maturity of Internet-of-Things technology, location-based service (LBS) is developing rapidly in intelligent terminal devices, and it brings new vitality to the fields of logistics, transportation, product traceability and so on. The popularity of LBS produces a lot of spatial data, which inevitably brings burden to the storage and management of LBS provider (LBSP). With the help of cloud computing and cloud storage, outsourcing spatial data to cloud server has become a new trend. However, due to the cloud server is not trusted, data outsourcing will face the problems of data disclosure and query disclosure. Range query is a common query in LBS, considering the situation of data outsourcing, this article proposes an accurate range query (ARQ) scheme, which can realize efficient range query while preserving LBSP's data privacy and user's query privacy from being disclosed to the cloud server. The ARQ scheme is suitable for spatial data in any form without being limited to the case that the data points are only integers, which has a certain practical significance. In addition, by dividing the region into atomic regions, ARQ can realize sublinear search time and ensure dynamic update of spatial data. We proved the security of the proposed scheme through security analysis, and demonstrated the effectiveness of the scheme through experiments.

Equipped with mobile devices, people relied on location-based services (LBSs) can expediently and reasonably organize their activities. But location information may disclose people's sensitive information, such as interests and health status. Besides, the limited resources of mobile devices restrict the further development of LBSs. In this article, aiming at the fair meeting position determination service, we design a lightweight privacy-preserving solution. In our scheme, mobile users only need to submit service requests. A cloud server and a location services provider are responsible for service response, where the cloud server achieves most of the calculation, and the location services provider determines the fair meeting location based on the computational results of the cloud server and broadcasts it to mobile users. The proposed scheme adopts homomorphic encryptions and random permutation methods to preserve the location privacy of mobile users. The security analyses show that the proposed scheme is privacy preserving under our defined threat models. Besides, the presented solution only needs to calculate $n$ Euclidean distances, and hence, our scheme has linear computation and communication complexity.

In this paper, we design a new lattice-based linearly homomorphic signature scheme over F 2 . The existing schemes are all constructed based on hash-and-sign lattice-based signature framework, where the implementation of preimage sampling function is Gaussian sampling, and the use of trapdoor basis needs a larger dimension m ≥ 5 n log q . Hence, they cannot resist potential side-channel attacks and have larger sizes of public key and signature. Under Fiat–Shamir with aborting signature framework and general SIS problem restricted condition m ≥ n log q , we use uniform sampling of filtering technology to design the scheme, and then, our scheme has a smaller public key size and signature size than the existing schemes and it can resist side-channel attacks.

In this paper, we propose an ID-based strong designated verifier signature (SDVS) over R - SIS assumption in the random model. We remove pre-image sampling function and Bonsai trees such complex structures used in previous lattice-based SDVS schemes. We only utilize simple rejection sampling to protect the security of our scheme. Hence, we will show our design has the shortest signature size comparing with existing lattice-based ID-based SDVS schemes. In addition, our scheme satisfies anonymity (privacy of signer’s identity) proved in existing schemes rarely, and it can resist side-channel attacks with uniform sampling.

Due to the rapid development of new technologies such as cloud computing, Internet of Things (IoT), and mobile Internet, the data volumes are exploding. Particularly, in the industrial field, a large amount of data is generated every day. How to manage and use industrial Big Data primely is a thorny challenge for every industrial enterprise manager. As an emerging form of service, cloud computing technology provides a good solution. It receives more and more attention and support due to its flexible configuration, on-demand purchase, and easy maintenance. Using cloud technology, enterprises get rid of the heavy data management work and concentrate on their main business. Although cloud technology has many advantages, there are still many problems in terms of security and privacy. To protect the confidentiality of the data, the mainstream solution is encrypting data before uploading. In order to achieve flexible access control to encrypted data, attribute-based encryption (ABE) is an outstanding candidate. At present, more and more applications are using ABE to ensure data security. However, the privacy protection issues during the key generation phase are not considered in the current ABE systems. That is to say, the key generation center (KGC) knows both of attributes and corresponding keys of each user. This problem is especially serious in the industrial big data scenario, because it will cause great damage to the business secrets of industrial enterprises. In this paper, we design a new ABE scheme that protects user’s privacy during key issuing. In our new scheme, we separate the functionality of attribute auditing and key generating to ensure that the KGC cannot know user’s attributes and that the attribute auditing center (AAC) cannot obtain the user’s secret key. This is ideal for many privacy-sensitive scenarios, such as industrial big data scenario.

More and more users are uploading their data to the cloud without storing any copies locally. Under the premise that cloud users cannot fully trust cloud service providers, how to ensure the integrity of users’ shared data in the cloud storage environment is one of the current research hotspots. In this paper, we propose a secure and effective data sharing scheme for dynamic user groups. (1) In order to realize the user identity tracking and the addition and deletion of dynamic group users, we add a new role called Rights Distribution Center (RDC) in our scheme. (2) To protect the privacy of user identity, when performing third party audit to verify data integrity, it is not possible to determine which user is a specific user. Therefore, the fairness of the audit can be promoted. (3) Define a new integrity audit model for shared cloud data. In this scheme, the user sends the encrypted data to the cloud and the data tag to the Rights Distribution Center (RDC) by using data blindness technology. Finally, we prove the security of the scheme through provable security theory. In addition, the experimental data shows that our proposed scheme is more efficient and scalable than the state-of-the-art solution.

There exist some inappropriate citations and typos in our paper ‘Social Rational Secure Multi-party Computation’, Vol. 26, No. 5 (2014), Pages: 1067–1083. We would like to use this corrigendum to point out these places so that the readers can understand the context of this paper in a better way. We are sorry for the unexpected inconvenience we brought for ‘Concurrency and Computation: Practice and Experience’ and the authors of the reference [27] cited in our paper, as well as the readers of our paper. The following corrections should be included.

Oblivious transfer is a fundamental tool in modern cryptography. In the past few years, many studies concentrate on oblivious transfer variants with more powerful functions. In this paper, the authors propose several variants of oblivious transfer in cut-and-choose scenario, providing multiple ways of transferring data in an oblivious manner. In addition, based on homomorphic encryption, the authors construct instantiations of these primitives, which can be proven secure in malicious model under ideal/real simulation paradigm and achieve the highest security level in the real world.

SUMMARY Rational party is a new kind of parties who behave neither like honest parties nor like malicious adversaries. The crux point of rational party is the definition of the utility function, as rational parties only care about how to maximize their utility. In other words, rational parties choose the strategies, which can bring them the highest utilities. In rational secure two‐party computation protocol, the main task is how to boost mutual cooperation to complete the protocol. Social rational secure multi‐party computation (SRSMPC) means that in a social network, some distributed and rational parties with reputation properties want to jointly compute a functionality. The seemingly simple task becomes tough under three conditions. The first condition is that the network composed by parties may not be complete. That is, two parties may not be neighbors and they are connected through other parties. The second is that the network may be not secure. That is, messages may be tempered by malicious parties. The third condition is that parties may run the protocol under incomplete information scenario. That is, parties may have types and each type has a corresponding utility function. Under the first and second conditions, parties need to consider how to securely transmit messages between two parties who are not neighbors. Under the third condition, we propose the Tit‐for‐Tat strategy and prove that mutual cooperation is a sequential equilibrium between two parties. In this paper, we construct an SRSMPC protocol by using mechanism design under incomplete information to facilitate the implementation of the SRSMPC protocol within constant rounds. Meanwhile, newcomers are allowed to participate in the protocol. To the best of our knowledge, this is the first social rational secure computation protocol for multi‐party under an incomplete information scenario and an incomplete network. Copyright © 2013 John Wiley & Sons, Ltd.