To ensure data robustness, users typically create replicas of critical data and store them on multiple servers. However, due to server failures or malicious attacks, these replicas may face the risk of loss or tampering. Therefore, periodic auditing of data replicas is necessary to ensure their integrity. Existing solutions perform periodic auditing with generating metadata for each replica individually. However, generating auditing metadata for every replica incurs significant computational overhead, and storing metadata for multiple replicas also imposes a considerable storage burden. To reduce this overhead and optimize the efficiency of auditing towards multiple replicas, we propose collusion-resistant multi-replica data auditing with optimized metadata generation (CRMRDA). First, CRMRDA enhances replica generation efficiency by employing symmetric encryption and random masking. An indistinguishable challenge strategy is introduced, making the number of generated auditing metadata copies independent of the number of replicas. Second, we construct a security model for multi-replica data possession that allows server collusion and provide a formal security proof for CRMRDA. In addition, CRMRDA supports practical features such as dynamic operations and batch auditing. Finally, the efficiency of CRMRDA is evaluated in an experimental environment consisting of four servers, one third-party auditor, and one data owner. Compared to the latest multi-replica data auditing schemes, CRMRDA demonstrates lower computational overhead in replica generation, metadata generation, and periodic auditing.

Summary The problem of maximizing the sum of a constrained submodular and a supermodular function has many applications such as social networks, machine learning, and artificial intelligence. In this article, we study the monotone submodular + supermodular maximization problem under a cardinality constraint and a p‐system constraint, respectively. For each problem, we provide a stochastic algorithm and prove the approximation ratio of each algorithm theoretically. Since the algorithm of the latter problem can also solve the former problem, we do some numerical experiments of the two algorithms to compare the time as well as the quality of the two algorithms in solving the former problem.

The side channel attacks will lead to the destruction of the security of the traditional cryptographic scheme. Leakage-resilient identity-based signature has attracted great attention. Based on the dual system encryption technology, we construct an identity-based signature scheme that can resist continuous private key leakage. In the standard model, the security of the scheme is proved. The key points of our leakage-resilient signature scheme are as follows: (1) The private key can be extended according to the security requirements. In other words, when the leakage is serious, we can select a bigger value n, where n is a parameter related to the leakage rate. (2) An elaborate key update algorithm makes the scheme resist continuous leakage attacks. Furthermore, the updated private key has the same distribution as the previous private key. (3) The proposed scheme is fully secure in the standard model rather than in the random oracle model or in the general group model. In order to achieve this goal, we use dual system encryption technology. Thus, the security of the constructed scheme does not depend on the number of queries of the attacker.

The vehicular ad hoc network (VANET) has become a hot topic in recent years. With the development of VANETs, how to achieve secure and efficient machine learning in VANETs is an urgent problem to be solved. Besides, how to ensure that users obtain the accurate results of machine learning is also a challenge. Based on the homomorphic encryption and secure multiparty computing technology, a flexible and privacy-preserving collaborative filtering scheme is proposed to accomplish the personalized recommendation for users, which is based on users’ interests and locations. On the one hand, the data can be updated by users flexibly to ensure the freshness and accuracy of the dataset of interest. On the other hand, the weighted values of user interest can be safely sorted to improve the accuracy of collaborative filtering effectively. Moreover, a novel collaborative filtering algorithm based on the homomorphic encryption technology is designed, which can guarantee that the calculated decryption result by machine learning is the same as the plaintext. Note that the privacy of user data can be preserved during machine learning in this algorithm. Both theoretical and experimental analyses demonstrate that the proposed scheme is secure and efficient for collaborative filtering in cloud computing in VANETs.

Summary Smart home networks (SHNets) are based on the Internet of Things (IoT) and provide users with a highly intelligent home life. In recent years, SHNets have become more and more popular. In SHNets, a large amount of data interactions are needed. These data related to the privacy of users may be subject to attacks such as eavesdropping attacks, replay attacks, and mobile phone stolen attacks. Privacy protection and communication security in smart home networks have been widely studied by many scholars. However, most of the existing user authentication in current researches focus on identity‐based authentication, where the encryption protocols are divided into two steps: the signing step and the encryption step. In this paper, a certificateless signcryption scheme for SHNets is proposed which can provide biometric‐based user identity authentication. The security and performance analyses show that our scheme is efficient and secure.

Internet of Things (IoT) has been introduced into smart grids, which has achieved great improvement. The statistics of power consumption is one of the important functions but could lead to the leakage of user daily behaviour. Researchers have put efforts into secure data aggregation protocols to avoid such potential risk. However, only a few schemes have considered the dynamic unit price of electricity, and no schemes have been designed for calculating the power consumption of every appliance in a specific area. This paper proposes a novel appliance-based data aggregation scheme (ABDAS) for bill generation and demand management in smart grids. In the proposed scheme, chameleon hash function (CHF) is utilised to facilitate the extraction of aggregated data due to the characteristic of collision controllability. Furthermore, indistinguishability obfuscation (IO) is utilised to keep the chameleon hash value secret and decrease the overhead of the trusted third party. The fog nodes (FNs) in our scheme are responsible for the calculation of aggregation with its powerful computing and storage capabilities. The security analysis shows that our scheme satisfies IND-CPA and multiple security goals. Additionally, the performance evaluation indicates that the computational overhead of our scheme is lower than that of existing schemes.

With the development of the Internet of Things and the demand for telemedicine, the smart healthcare system has attracted much attention in recent years. As a platform for medical data interaction, the smart healthcare system is demanded to ensure the privacy of both the receiver and the sender, as well as the security of data transmission. In this paper, we propose a privacy-preserving data transmission scheme where both secure ciphertext conversion and malicious users identification are supported. In particular, the OT m n protocol is introduced to guarantee the two-way privacy of communication parties. Meanwhile, we adopt proxy reencryption algorithm to support secure ciphertext conversion so as to ensure the confidentiality of data in many-to-many communication pattern. In addition, by taking advantage of the concept of blockchain technology, a novel OT m n protocol is proposed to prevent data from being tampered with and effectively identify malicious users. Theoretical and experimental analyses indicate that the proposed scheme is practical for smart healthcare with high security and efficiency.

Intelligent transportation systems (ITS) have always been an important application of Internet of Things (IoT). Today, big data and cloud computing have further promoted the construction and development of ITS. At the same time, the development of blockchain has also brought new features and convenience to ITS. However, due to the endless emergence of increasingly advanced types of attacks, the security of blockchain-based ITS needs more attention from industry and academia. In this paper, we focus on exploring the primitives in cryptography to guarantee the security of blockchain-based ITS. In particular, the authentication, encryption, and key management schemes in cryptography are discussed. Furthermore, we propose two methods for achieving the threshold key management in blockchain-based ITS. The proposed threshold key management scheme (with threshold t ) enables various stakeholders to recover a secret if the number of participated stakeholders is at least t . It should be noted that the proposed threshold key management scheme is efficient and secure for multiple users in blockchain-based ITS, especially for the data-sharing scenario.

The vehicular ad hoc network (VANET) is a type of mobile wireless networks, where vehicles are allowed to broadcast a message to its neighbors and access data from other participants. However, how to guarantee the reliability of these broadcast messages and prevent malicious vehicles from accessing the private data of the VANETs is still an open problem to be solved. As a countermeasure, a fuzzy-based trustworthiness evaluation scheme for privilege management in VANETs is proposed in this article. In the proposed scheme, to ensure the result of trustworthiness is valid, mutual authentication with conditional anonymity between the evaluator and the vehicle to be evaluated is first employed. Then, based on the vehicle's behavioral big data, the trustworthiness of each vehicle is evaluated by utilizing the fuzzy theory. Note that the privilege of a vehicle and the reliability of the vehicle's messages are determined by its trustworthiness. Moreover, the mobility of vehicles is also considered in this article, since the location of a vehicle is not constant and the monitoring area of an road side unit is limited. The results of theoretical and experimental analyses demonstrate that the proposed scheme performs well in terms of security and efficiency.

The renewable energy plays an increasingly important role in many fields such as lighting, automobile, and electric power. In order to make full use of the renewable energy, various smart Internet of Thing (IoT) devices are deployed. However, in the field of energy management, the two-way mismatch between the demand and the supply of the renewable energy will greatly affect the efficiency of the renewable energy. In addition, the security threat of the energy data and the privacy leakage of the user may hinder the further development of smart IoT devices. Therefore, how to achieve consistency and balance between the demand and the renewable energy supply and how to guarantee the security and privacy of smart IoT devices become the key problems of the energy-efficient smart environment. In this paper, a secure and intelligent energy data management scheme for smart IoT devices is proposed. It is worth noting that, with the help of artificial intelligence (AI) technologies and secure cryptography primitives, the proposed scheme realizes high-efficient and secure energy utilization in a smart environment. Specifically, the proposed scheme aims at improving the efficiency of the energy utilization in the multidimensions of a smart environment. In order to realize the fine-grain energy management of smart IoT devices, strategies of three different dimensions are considered and realized in the proposed scheme. Moreover, technologies in AI are applied and integrated into the energy management scheme. The analysis shows that the proposed scheme can make full use of the renewable energy in smart IoT devices.

Smart mobile devices are one of the core components of the wireless body area networks (WBANs). These devices shoulder the important task of collecting, integrating, and transmitting medical data. When a personal computer collects information from these devices, it needs to authenticate the identity of them. Some effective schemes have been put forward to the device authentication in WBANs. However, few researchers have studied the WBANs device authentication in emergency situations. In this paper, we present a novel system named emergency medical system without the assistance of doctors. Based on the system, we propose an identity‐based fast authentication scheme for smart mobile devices in WBANs. The scheme can shorten the time of device authentication in an emergency to achieve fast authentication. The analysis of this paper proves the security and efficiency of the proposed scheme.

Fractional Link scheduling is one of the most fundamental problems in wireless networks. The prevailing approach for shortest fractional link scheduling is based on a reduction to the maximum-weighted independent set problem, which itself may not admit efficient approximation algorithms. In addition, except for the wireless networks under the protocol interference model, none of the existing scheduling algorithms can produce a link schedule with explicit upper bounds on its length in terms of the link demands. As the result, the polynomial approximate capacity regions in these networks remain blank. This paper develops a purely combinatorial paradigm for fractional link scheduling in wireless networks. In addition to the superior efficiency, it is able to provide explicit upper bounds on the lengths of the produced link schedule. By exploiting these upper bounds, polynomial approximate capacity regions are derived. The effectiveness of this new paradigm is demonstrated by its applications in wireless networks under the physical interference model and wireless MIMO networks under the protocol interference model.

With the increasing adoption of cloud computing, a growing number of users outsource their datasets into cloud. The datasets usually are encrypted before outsourcing to preserve the privacy. However, the common practice of encryption makes the effective utilization difficult, for example, search the given keywords in the encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we proposed an innovative semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. More specifically, our scheme first indexes the documents and builds trapdoor based on the concept hierarchy. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. Our experiment results based on the real world datasets show the scheme is more efficient than previous scheme. We also study the threat model of our approach and prove it does not introduce any security risk.

Linear interference alignment (LIA) is one of the key interference mitigation techniques to enhance the wireless MIMO network capacity. The generic LIA feasibility amounts to whether or not a well-structured random matrix with entries drawn from a continuous distribution has full row-rank almost surely. Recently, a randomized algebraic test of feasibility was proposed in the literature. It is a pseudo-polynomial bounded-error probabilistic algorithm in nature, and has intrinsic limitations of requiring an inordinate amount of running time and memory even for a moderate sized input and being prone to round-off errors in floating-point computations. This paper presents necessary conditions and sufficient conditions of the generic LIA feasibility and develops fast and robust tests of them based on network flow. In certain settings, these conditions are both necessary and sufficient, and their flow-based tests yield efficient algorithm for feasibility test.

Multiflow problems are one of the most fundamental problems in both wired networks and wireless networks. Due to the cross-layer nature, multiflow problems in wireless networks are significantly harder than their counterparts in wired networks and have received much research interest over the past decade. Common to most other early-staged research, the characterization of computational hardness and the “war” on achievable approximation bounds have been the priority to the existing studies of multiflow problems in wireless networks while their practical feasibility in both running time and memory requirement is ignored as long they are polynomial. In fact, almost all of the state-of-the-art approximation algorithms for multiflow problems in wireless networks are all resorted to the traditional linear programming (LP) methods exclusively. However, those traditional LP methods can require an inordinate amount of running time and memory even for a moderate sized input, and consequently they often prove unusable in practice. This paper presents a completely new paradigm for multiflow problems in general wireless networks which is radically different from the prevailing LP-based paradigm, and develops practical algorithmic solutions which are much faster and simpler.

Maximum multiflow and maximum concurrent mul-tiflow in multi-channel multi-radio (MC-MR) wireless networks have been well-studied in the literature. They are NP-hard even in single-channel single-radio (SC-SR) wireless networks when all nodes have uniform (and fixed) interference radii and the positions of all nodes are available. While they admit a polynomial-time approximation scheme (PTAS) when the number of channels is bounded by a constant, such PTAS is quite infeasible practically. Other than the PTAS, all other known approximation algorithms, in both SC-SR wireless networks and MC-MR wireless networks, resorted to solve a polynomial-sized linear program (LP) exactly. The scalability of their running time is fundamentally limited by the general-purposed LP solvers. In this paper, we first introduce the concept of interference costs and prices of a path and explore their relations with the maximum (concurrent) multiflow. Then we develop purely combinatorial approximation algorithms which compute a sequence of least interference-cost routing paths along which the flows are routed. These algorithms are faster and simpler, and achieve nearly the same approximation bounds known in the literature.

Multiple-input multiple-output (MIMO) technology provides a means of boosting network capacity without requiring additional spectrum. It has received widespread attention over the past decade from both industry and academic researchers, now forming a key component of nearly all emerging wireless standards. Despite the huge promise and considerable attention, a rigorous algorithm-theoretic framework for maximizing network capacity in multihop wireless MIMO\ networks is missing in the state of the art. The existing algorithms and protocols for maximizing network capacity in multihop wireless MIMO networks are purely heuristic without any provable performance guarantees. In this paper we conduct a comprehensive algorithm study for maximizing network capacity in multihop wireless MIMO networks with receiver-side interference suppression, including the full characterization of NP-hardness and APX-hardness, the polynomial time approximation schemes, and the practical approximation algorithms with provable performance guarantees.