IoT malware dissemination seriously exacerbates the decline of IoT system availability, which deteriorates the users experience. To address the issue, we first predict the optimal IoT malware dissemination strategy based on a signaling game for edge computing-assisted IoT systems. We then develop an algorithm to obtain the solution of the signaling IoT availability assessment game, which is to factually reflect IoT malware dissemination in practice and reasonably express the probability of IoT system nodes being successfully infected by malware. Thus, a state transition diagram of IoT system nodes can be further designed, illustrating intercommunication among all six states during IoT malware dissemination. Upon this state transition diagram, we represent the state transition probability of IoT system nodes in each state utilizing a Markov matrix, and attain the steady-state availability of an IoT system node from reliability theory. Consequently, we deduce metrics to access the steady-state availability of the entire IoT system under typical star-, tree-, and mesh topologies, respectively. We also design the corresponding IoT system availability assessment algorithm from the view of practice. In this manner, an availability assessment mechanism for edge computing-based IoT systems with malware dissemination is constructed. Experiments demonstrate the influence of IoT system features on predicting IoT malware dissemination probability and assessing the steady-state availability of three typical IoT system topologies. Our results can be utilized to lay a theoretical foundation for guiding the implementation of higher availability for edge computing-assisted IoT systems with malware dissemination. 

Signaling game-based availability assessment for edge computing-assisted IoT systems with malware dissemination

In this article, aiming at a Chinese keyword-based book search service, from a technological perspective, we propose to modify a user query sequence carefully to confuse the user query topics and thus protect the user topic privacy on the untrusted server, without compromising the accuracy of each book search service. First, we propose a client-based framework for the privacy protection of book search, and then a privacy model to formulate the constraints in terms of accuracy, efficiency, and security, which the cover queries generated based on a user query sequence should meet. Second, we present a modification algorithm for a user query sequence, based on some heuristic strategies, which can quickly generate a cover query sequence meeting the privacy model by replacing, deleting, and adding keywords for each user query. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed approach, i.e., which can improve the security of users’ topic privacy on the untrusted server without compromising the efficiency, accuracy, and usability of an existing Chinese keyword book search service, so it has a positive impact for the construction of a privacy-preserving text retrieval platform under an untrusted network environment.

A Confusion Method for the Protection of User Topic Privacy in Chinese Keyword-based Book Retrieval

Medical education plays an important role in promoting the development of global medical science. Nevertheless, the intrinsic gap existing between institutional medical teaching and practical clinical tasks causes low education efficiency and students’ weak initiative. Recent developments of sensing fabric and embedded computing, along with the advances in Artificial intelligence (AI) and digital twin technology are paving the way for the transformation of medical research towards digitization. In this work, we present an intelligent fabric space based on novel functional fabric materials and digital twin networking enabled by 5G and Internet of Things (IoT) technologies. In this space, medical students can learn knowledge with collaborative mapping of the digital and real world, cyber-physical interaction and real-time tactile feedback. And the proposed service system will evaluate and feedback students’ operational behaviors to improve their experimental skills. We provide four typical applications of intelligent fabric space for medical education, including medical education training, health and behavior tracking, operation playback and reproduction, as well as medical knowledge popularization. The proposed intelligent fabric space has the potential to promote innovative technologies for training cutting-edge medical students by effective and efficient ways.

https://www.nso-journal.org/articles/nso/pdf/2022/01/nso220005s.pdf

Digital Medical Education Empowered by Intelligent Fabric Space

In the cloud-assisted Industrial Internet of Things (IIoT), ciphertext-policy attribute-based encryption (CP-ABE) could help the data owner (DO) share his sensitive data via the cloud under self-defined access structures. Among general CP-ABE schemes, the decryption overhead, the key generation cost, and the ciphertext length increase with the number of involved attributes. Additionally, only regular attributes are taking into consideration rather than weighted attributes. In this article, we proposed a secure, efficient, and weighted access control scheme (SEWAC) for cloud-assisted IIoT applications. SEWAC enables the DO to formulate any fine-grained access structure over weighted attributes without making it more complicated. Furthermore, such weighted attributes would not add the length of ciphertext. SEWAC also supports online/offline key generation to alleviate the computational cost of the authority from answering mass key requests in the online phase, while most computational tasks are executed in the offline phase. The heavy decryption overhead is offloaded to the cloud. To ensure the cloud to honestly execute the process of outsourced decryption, we design an efficient batch verification method, which allows the user to spend only three bilinear pairing operations in checking the correctness of batch results. We also give the formal security proof of the proposed scheme. Comprehensive comparisons and implementation results indicate that SEWAC can better achieve weighted access control, compressed ciphertext length, efficient key generation, and the assurance of the outsourced decryption result.

Secure, Efficient, and Weighted Access Control for Cloud-Assisted Industrial IoT

Federated learning (FL) is an emerging collaborative machine learning method. In FL processing, the data quality shared by users directly affects the accuracy of the federated learning model, and how to encourage more data owners to share data is crucial. In other words, how to design a good incentive mechanism is the key problem in FL. In this paper, we propose an incentive mechanism based on the enhanced Shapley value method for FL. In the proposed mechanism, the enhanced Shapley value method is proposed to measure income distribution, which takes multiple influence factors as weights. The analytic hierarchy process (AHP) is used to find the corresponding weight value of the influence factors. Finally, the numerical experiments are carried to verify the performance of the proposed incentive mechanism. The results show that compared with the Shapley value method considering the single factor, the income distribution of all participants can better reflect multiple factor contribution when using the enhanced Shapley value method.

/pdf/federated-learning-incentive-mechanism-design-via-enhanced-2s5s5cr0.pdf

Federated Learning Incentive Mechanism Design via Enhanced Shapley Value Method

Mobile healthcare (mHealth) enables people to collect and share their personal health records (PHRs) and gain rapid medical treatment via mobile 5G-enabled Industrial Internet of Things (IIoT) devices, which also brings the challenge of keeping the PHRs confidentiality and preventing unauthorized access. By the emerging Ciphertext-Policy Attribute-based Encryption (CP-ABE), the PHR owner can encrypt his PHR data under self-defined access policies. However, existing CP-ABE schemes are suffering from either heavy computation cost and storage overhead or traitor tracing and direct revocation. In this paper, we propose an efficient, traceable and revocable access control scheme named TRAC for mHealth in 5G-enabled IIoT. In TRAC, the ciphertext is composed of the attribute-relevant ciphertext encrypted under an AND-gate access structure and the identity-relevant ciphertext associated with some potential receivers. The malicious user who leaks his/her privilege to unauthorized entities will be precisely tracked and added in the revocation list, by which the cloud server can update the identity-relevant ciphertext by itself. The length of final ciphertext and the time of bilinear pairing operations used in decryption are constant. The security analysis and performance evaluation indicate the security, efficiency and practicality of TRAC.

TRAC: Traceable and Revocable Access Control Scheme for mHealth in 5G-enabled IIoT

Differential privacy protection scheme based on community density aggregation and matrix perturbation

Existing studies on neural architecture search (NAS) mainly focus on efficiently and effectively searching for network architectures with better performance. Little progress has been made to systematically understand if the NAS-searched architectures are robust to privacy attacks while abundant work has already shown that human-designed architectures are prone to privacy attacks. In this paper, we fill this gap and systematically measure the privacy risks of NAS architectures. Leveraging the insights from our measurement study, we further explore the cell patterns of cell-based NAS architectures and evaluate how the cell patterns affect the privacy risks of NAS-searched architectures. Through extensive experiments, we shed light on how to design robust NAS architectures against privacy attacks, and also offer a general methodology to understand the hidden correlation between the NAS-searched architectures and other privacy risks.

On the Privacy Risks of Cell-Based NAS Architectures

The establishment of a complete drug traceability system is essentially important for public drug security and the business of pharmaceutical companies which is aimed at tracking where the drug has gone along the drug supply chain. Traditional centralized server-client technical solutions have been far from satisfaction for their bad performances in data authenticity, privacy, system resilience, and flexibility. In this paper, we propose a drug traceability scheme called MB-BC, which realizes the security and traceability of drug data through a novel multibranched blockchain scheme. Different from the characteristics of transparency of traditional blockchains, MB-BC realizes fine-grained access control of data between all levels in the system, which improves the security and privacy of data. MB-BC has further improved the existing consensus mechanism, strengthened the supervision of pharmaceutical companies, and further improved the safety and robustness of the system. Furthermore, the system combines data access strategies with smart contracts; each branch chain can also issue its smart contract to provide personalized services. Finally, security and performance evaluations show that the solution is advantageous in terms of data security, system robustness, supervisibility, and traceability, as well as efficient in terms of blockchain throughput, data query time, and blockchain consensus consumptions, compared with other typical approaches.

Haiping Huang

Papers

TRAC: Traceable and Revocable Access Control Scheme for mHealth in 5G-enabled IIoT

Secure, Efficient, and Weighted Access Control for Cloud-Assisted Industrial IoT

Differential privacy protection scheme based on community density aggregation and matrix perturbation

On the Privacy Risks of Cell-Based NAS Architectures

MB-BC: Drug Traceability System Based on Multibranched Blockchain Structure