With the rapid advancement of Internet of Things (IoT) and the growing complexity of network infrastructures, the traditional network management model based on manual configuration is no longer able to meet the dynamic IoT secure communication requirements. Intent-based networking (IBN) significantly improves network manageability and agility by driving network automation through high-level business intent. However, policies in IBN are highly dynamic, and issues such as frequent changes in user intent, role or attribute adjustments, and ad-hoc access requirements in multi-tenant environments make it necessary for the system to quickly adapt to policy changes to ensure secure data sharing. Attribute-based encryption has become a key enabler for securing IoT communications, offering fine grained access control and adaptability to users’ dynamic access requirements in real time. In this paper, we propose an attribute-based access control supporting policy reconfiguration (ABACPR) for the highly dynamic nature of policies in IBN, aiming to improve the flexibility and efficiency of policy updating and ensure the security and consistency. Finally, We compared the theoretical and experimental analysis with related works and the results demonstrate that ABACPR is better suited for secure IoT communication in IBN.

As a cloud storage side in the cloud-edge-terminal collaboration, which empowers the artificial intelligence of things (AIoT), the accuracy of data retrieval and data privacy in the cloud can significantly impact the quality of service in AIoT. Typically, the cloud facilitates data sharing through keyword-based private information retrieval (PIR). However, these keywords may contain the privacy of patients, causing the server to gain patients privacy during database retrieval, resulting in privacy exposure. Concurrently, malicious users seek to access more datasets than those corresponding to the keywords. It is worth to consider the construction of a secure and private retrieval system in AIoT. To protect the privacy of AIoT, this paper proposes two multi-keyword PIR schemes: the fuzzy multi-keyword PIR scheme and the fine-grained flexible multi-keyword PIR scheme. The fuzzy multi-keyword PIR scheme utilizes the proposed batch oblivious pseudo-random function (B-OPRF) based on OTEn1 to implement the batch search. If one of the n keywords in a dataset matches a requested keyword, the corresponding datasets are returned to the user, achieving fuzzy retrieval. The fine-grained flexible multi-keyword PIR scheme incorporates the proposed batch flexible OPRF (BF-OPRF) algorithm, wherein k out of the n keywords in the dataset must match the k requested keywords from users for the corresponding datasets to be returned to the user. Additionally, the cloud server may tamper with the data, and the correctness of the data is periodically verified using a verifiable mechanism. The effectiveness and performance of the proposed schemes are validated through experiments and theoretical analysis.

Recently, Industrial Internet of Things (IoT) has experienced significant growth and has garnered widespread interest. With industrial IoT, remote users can link and manage various sensing devices and gather instantaneous data from devices, thereby improving efficiency and productivity in industrial settings. However, challenges with existing authentication protocols in the Industrial IoT environment have been identified. To address these shortcomings, we propose a high-availability authentication and key agreement protocol for IoT-based devices, which reduces computational and communication overheads. In addition, our protocol offers privacy protection to users as the data message is transmitted by the users' pseudonym, helping to conceal their real identity. Furthermore, we also consider device dynamic updates in our proposed protocol. Security proof and analysis are provided to prove that our proposed protocol can withstand known attacks. In addition, performance analysis shows that our proposed protocol is a more efficient option than other related protocols.

In recent years, to improve the quality of medical services in e-health systems, various types of sensors supporting collection and online/offline consultation have appeared in life; effectively facilitating doctors' disease prediction and consultation. However, data in e-health systems come from a wide range of sources and are mostly related to patient privacy. Therefore, how to ensure patient privacy and data confidentiality in data transmission is considered serious issues. In addition, the storage volume of cloud servers continues to grow, and how to guarantee that servers can quickly respond to requests has become a pressing problem. To this end, a privacy-preserving data trans-mission protocol is proposed, which only needs constant times interactions to complete the batching requests. In particular, a lightweight OTnk protocol is designed, employing the idea of matrix transformation, which effectively reduces the number of interactions while protecting the privacy of both communicating parties. The security and performance analysis indicate that the proposed protocol can be instantiated in e-health with high security and efficiency.

With the development of cloud computing, the great amount of storage data requires safe and efficient data sharing. In multiparty storage data sharing, first, the confidentiality of shared data is ensured to achieve data privacy preservation. Second, the security of stored data is ensured. That is, when stored shared data are subject to frequent access operations, the address sequence or access pattern of data is hidden. Therefore, determining how to ensure the untraceability of stored data or efficient hide the data access pattern in sharing stored data is a challenge. By employing the proxy re-encryption algorithm and oblivious random access memory (ORAM), a privacy-preserving and untraceable scheme is proposed to support multiple users in sharing data in cloud computing. On the one hand, group members and a proxy use the key exchange phase to obtain keys and resist multiparty collusion if necessary. The ciphertext obtained according to the proxy re-encryption phase enables group members to implement access control and store data, thereby completing secure data sharing. On the other hand, this article realizes data untraceability and a hidden data access pattern through a one-way circular linked table in a binary tree (OCLT) and obfuscation operation. Additionally, based on the designed structure and pointer tuple, malicious users are identified and data tampering is prevented. The security analysis shows that the protocol designed in this article can meet the security requirements of proxy re-encryption and ORAM. Both theoretical and experimental analyses demonstrate that the proposed scheme is secure and efficient for group data sharing in cloud computing.

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.

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.