Smart healthcare refers to the innovative medical model that enhances the efficiency and quality of medical services through the use of modern information technology. While smart healthcare brings convenience to both patients and doctors, it also raises issues of medical data security and privacy protection. In smart healthcare, secure identity authentication not only requires the verification of user identities but also ensures that user identity information is not leaked. At the same time, it requires an efficient authentication process to cope with resource-constrained situations. Existing schemes have addressed the issues of identity authentication and the confidentiality of identity information, but they have significant overhead and are not suitable for resource-constrained situations. Additionally, the schemes themselves are vulnerable to attacks. To address these issues, we propose a multi-party authentication scheme for smart healthcare. First, based on certificateless public key cryptography(CLPKC), a certificateless signature scheme(CLS) is introduced, which eliminates the need for complex bilinear pairings and map-to-point hash function computations, enabling batch authentication for multiple users. Second, the scheme incorporates the user’s contextual environment, ensuring secure user identity authentication only under safe access conditions. Finally, blockchain technology is utilized to record access logs, enabling user traceability. Security analysis proves that the proposed scheme can meet security requirements such as conditional privacy protection, unlinkability, resistance to replay attacks, and resistance to collusion attacks. Compared with existing schemes, this scheme has lower computational overhead and communication costs, making it more suitable for lightweight multi-party authentication in smart healthcare scenarios.

With the development of the Internet of Vehicles (IoV), the rapid growth of smart vehicles has generated vast amounts of data, which are of significant value for training intelligent IoV application models. Traditional methods of intelligent model training require centralized collection of raw data, consuming significant communication resources and posing issues such as privacy leakage. Federated learning offers an approach that allows multiple participants to collaboratively train models while protecting data privacy. However, in practical application scenarios, the data among vehicles often exhibits the characteristics of non-independent and identically distributed (Non-IID), which can lead to significant performance differences of the global model across different vehicles, thus posing a challenge to achieving fair collaboration among vehicles. Existing solutions tend to pursue the overall performance of all users, while neglecting the issue of fairness between individual users, which may result in significant differences in accuracy between different users. To address this issue, this paper proposes a fair federated learning algorithm that combines cross-entropy and marginal loss to assess the influence of the vehicle’s local model against the global model, and dynamically adjust the aggregation weight and achieve fair federated learning accordingly. This method was compared with existing methods on multiple datasets, and the experimental results show that the proposed method is superior to other methods in terms of fairness.

In the context of vehicular crowdsensing within the Internet of Vehicles (IoV), data aggregation techniques enable the computation and analysis of sensing data to extract valuable insights and improve transmission efficiency. However, sensing data and aggregation results often contain sensitive information about terminal vehicles, posing risks of privacy leakage. Existing privacy-preserving data aggregation schemes typically employ homomorphic encryption or bilinear pairing operations to ensure both privacy protection and integrity verification, which incur significant computational overhead. Moreover, when aggregation nodes are untrusted, it becomes challenging to verify the correctness of the aggregated results. To address these challenges, this paper proposes a lightweight and verifiable multi-dimensional data aggregation privacy-preserving scheme for vehicular crowdsensing. In the data generation phase, a blinding factor is introduced to obfuscate the sensing data, and secret sharing is employed to split the obfuscated data into multiple shares. This approach ensures data privacy and resists collusion attacks among internal vehicles. A lightweight signature aggregation method is integrated to verify multiple digital signatures in a single computation, significantly reducing the computational and communication costs associated with integrity verification. In the data recovery phase, the original data can be restored by removing the blinding factor, eliminating the need for traditional decryption algorithms and thereby reducing computational overhead. In the aggregation result verification phase, a homomorphic commitment mechanism is adopted. Users generate commitments of the obfuscated sensing data and upload them to the blockchain, effectively addressing the issue of untrusted roadside units and traffic management centers in real-world applications and ensuring the correctness of the aggregated results. Experimental results demonstrate that the proposed scheme reduces computational overhead by more than 50% compared to existing methods, exhibiting higher efficiency and better adaptability.

With its advantages are gradually emerging, the Internet of Things (IoT) is profoundly changing the way people work and live. Among all IoT applications, medical IoT is partic-ularly important, in which the user can communicate with smart medical device through the hospital gateway node. However, due to the inherent defects of these smart devices and the openness of wireless networks, medical IoT is vulnerable to kinds of attacks, such as impersonation attack and password guessing attack. Unfortunately, there are few authentication schemes for medical IoT at present, while existing three-party schemes have various weakness and are not suitable for medical IoT. Given the sensitivity of patient data and the deadly consequences of attacks on medical devices, there is an urgent need to develop a suitable authentication scheme in Medical IoT Network with high security. To alleviate the above problems, a robust ECC based three-party authentication and key agreement scheme for medical IoT(RAKI) has been proposed, which is secure with random oracle model and the informal security analysis. Besides, the performance comparisons against existing competing three-party schemes indicate that our scheme is efficient for medical IoT.

Summary With the popularity of cloud computing, more and more users store sensitive information in cloud servers. In order to protect the data over the cloud server, symmetric encryption with keyword search has been developed and the phrase search has been proposed subsequently to overcome the inefficiency produced by single/multi‐keyword search. However, most existing phrase search schemes fails to support fault‐tolerant search which is essential to users. Therefore, this paper proposes a fault‐tolerant dynamic phrase search scheme with forward privacy and backward privacy (FTPS). Piecewise Linear Chaotic Map and minhash function are used to blur information, and Bloom filter based index is constructed to realize efficient search and dynamic update simultaneously. Security analysis proves that FTPS can properly preserve the privacy of search user, and experimental results show that FTPS is practical.

With the continuous maturity of Internet of Things (IoT) technology, it has begun to be frequently used in all walks of life to improve people's work efficiency and living standards. The wide use of IoT in the medical field makes it convenient for patients to obtain medical services, and also enables doctors to obtain patients' physical conditions more timely and accurately, so as to formulate more efficient treatment plans. However, when people enjoy the convenience of medical IoT, how to ensure the security of communication and privacy of patients are all problems that cannot be ignored. In order to achieve secure access the network, this paper proposes an anonymous password authenticated key exchange protocol for medical Internet of Things (PAMI), where only a low-entropy password is required to realize the mutual authentication between medical device and telemedicine server, so as to negotiate a high-entropy session key. The security of PAMI is formally proved under the standard model, and the experiment based performance comparison demonstrates that it is more efficient than the existing similar schemes.

In a smart grid, data aggregation is a common method to evaluate regional power consumption. Data leakage in the process of data transmission poses a security threat to the privacy of users. Many existing data aggregation schemes can only aggregate one-dimensional data; however, it is necessary to aggregate multidimensional data in practical smart grid applications. Therefore, this paper proposes a privacy-preserving multidimensional data aggregation scheme, which can aggregate multidimensional data and protect the individual user’s identity and data privacy. The security of the proposed scheme is proved under the random oracle model. The simulation results show that the proposed scheme has great advantages in computing overhead, and the communication overhead also meets the requirements of the smart grid.

In the ubiquitous networks, mobile nodes can obtain roaming service that enables them to get access to the services extended by their home networks in the field of foreign network. To provide secure and anonymous communication for legal mobile users in roaming services, there should be a mutual authentication between mobile user and foreign agent with the help of home agent. There are many roaming authentication schemes which have been proposed; however, with the progress of quantum computation, quantum attack poses security threats to many traditional public key cryptography-based authentication schemes; thus, antiquantum attack roaming authentication schemes need to be investigated. On account of the limitation of computational resources for mobile nodes, a lightweight anonymous and antiquantum authentication schemes need to be developed to enable mobile nodes to roam across multiple service domains securely and seamlessly. In consideration of the advantages of lattice in antiquantum, an NTRU-based authentication scheme with provable security and conditional privacy preservation is proposed to remedy these security weaknesses. Compared with the existing scheme, the proposed scheme not only improves efficiency but also can resist the quantum attack.

Session initiation protocol (SIP), a widely used signal protocol for controlling multimedia communication sessions, is under numerous attacks when performing the authentication steps between the user and server. So secure authentication schemes are needed to be presented for SIP. Recently, Arshad et al. advanced novel schemes for SIP using elliptic curve cryptography (ECC) and claimed their schemes can resist various attacks. However, Lu et al. found that Arshad et al.’s scheme cannot resist trace and key-compromise impersonation attacks; hence, it cannot provide proper mutual authentication. Meanwhile, an enhanced scheme was advanced by Lu et al. and they stated that their scheme can stand up to possible known attacks. Nevertheless, in this paper, we conclude that Arshad and Nikooghadam’s scheme is insecure against impersonation attack and Lu et al.’s scheme is still vulnerable to impersonation attack. To overcome these weaknesses of their schemes, we present a novel anonymous ECC-based scheme for SIP. Security analysis and performance analysis show that our proposed scheme can resist various known attacks and efficient in the meantime.

Abstract The cloud storage service has been widely used in daily life because of its convenience. However, the service frequently suffers confidentiality problems. To address this problem, some efforts have been made on keyword search over encrypted data schemes. For instance, the chaotic‐based keyword search scheme over encrypted data has been proposed recently. However, the scheme just only support single‐ keyword search each time, which severely limits its utilization in cloud storage. This article proposes a novel chaotic‐based time‐aware multi‐keyword search scheme with designated server. Inner product similarity is adopted in our scheme to realize multiple keyword search and remove the constraint of single‐keyword search each time. Timed‐release encryption is integrated into the proposed scheme at the same time, which enables the data sender to specify the time when the cloud servers can search the encrypted data. Analysis indicates that our scheme not only can counter off‐line guessing attacks to the ciphertext and trapdoor, but also supports ranked search with a reasonable computational cost. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract Efficient secret sharing schemes play important roles in many areas such as key management, trust management, and secure multi‐party protocols. In order to reduce the communication costs of threshold changeable secret sharing scheme, a dynamic threshold changeable secret sharing scheme is proposed based on RSA cryptography system and discrete logarithm problem. The threshold value of the scheme can be decided according to the secure demand, and each participant's secret shadow is selected by the participant himself; therefore, secure channel is avoided, and communication costs are reduced. The shadows do not need to be changed when renewing the sharing secret, deleting old participants, or adding new participants, so the secret shadows are reusable. Moreover, the efficient solutions against multiform cheating are proposed; therefore, the scheme is highly secure and practical. Copyright © 2015 John Wiley & Sons, Ltd.