BDSS: Blockchain-based Data Sharing Scheme With Fine-grained Access Control And Permission Revocation In Medical Environment

Lejun Zhang,Yanfei Zou,Muhammad Hassam. Yousuf,Weizheng Wang,Zilong Jin,Yansen Su,Kim Seokhoon 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.5

Due to the increasing need for data sharing in the age of big data, how to achieve data access control and implement user permission revocation in the blockchain environment becomes an urgent problem. To solve the above problems, we propose a novel blockchain-based data sharing scheme (BDSS) with fine-grained access control and permission revocation in this paper, which regards the medical environment as the application scenario. In this scheme, we separate the public part and private part of the electronic medical record (EMR). Then, we use symmetric searchable encryption (SSE) technology to encrypt these two parts separately, and use attribute-based encryption (ABE) technology to encrypt symmetric keys which used in SSE technology separately. This guarantees better fine-grained access control and makes patients to share data at ease. In addition, we design a mechanism for EMR permission grant and revocation so that hospital can verify attribute set to determine whether to grant and revoke access permission through blockchain, so it is no longer necessary for ciphertext re-encryption and key update. Finally, security analysis, security proof and performance evaluation demonstrate that the proposed scheme is safe and effective in practical applications.

Research on Covert Communication Technology Based on Matrix Decomposition of Digital Currency Transaction Amount

Lejun Zhang,Bo Zhang,Ran Guo,Zhujun Wang,Guopeng Wang,Jing Qiu,Shen Su,Yuan Liu,Guangxia Xu,Zhihong Tian,Sergey Gataullin 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.4

With the development of covert communication technologies, the number of covert communication technologies using blockchain as a carrier is increasing. However, using the transaction amount of digital currency as a carrier for covert communication has problems such as low embedding rate, large consumption of transaction amount, and easy detection. In this paper, firstly, by experimentally analyzing the distribution of bitcoin transaction amounts, we determine the most suitable range of amounts for matrix decomposition. Secondly, we design a novel matrix decomposition method that can successfully decompose a large amount matrix into two small amount matrices and utilize the elements in the small amount matrices for covert communication. Finally, we analyze the feasibility of the novel matrix decomposition method in this scheme in detail from four aspects, and verify it by experimental comparison, which proves that our scheme not only improves the embedding rate and reduces the consumption of transaction amount, but also has a certain degree of resistance to detection.

Optimizing Energy-Latency Tradeoff for Computation Offloading in SDIN-Enabled MEC-based IIoT

Xinchang Zhang,Changsen Xia,Tinghuai Ma,Lejun Zhang,Zilong Jin 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.12

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

AN ANALYSIS AND RESEARCH OF ROUTING PROTOCOL FOR MANET

Lejun Chi,Zhongxiao Hao,Chunlong Yao,Yating Zhang,Kun Wang,Yang Liu 한국멀티미디어학회 2006 한국멀티미디어학회 국제학술대회 Vol.2006 No.-

In typical methods for accessing Internet or intranet in mobile wireless environment, users can access to fixed networks without multi-relay based on Broad Band access networks. Especially in some special applications, messages from source users can only arrive at destination terminals by multi-relay among several mobile users because wireless network is infrastructural, which is so called Ad hoc networks. There are many up-to-date research results in table driven routing and on-demand routing for ad hoc mobile networks are proposed in this paper. In the mentioned two kinds of routing protocols, DSR and DSDV, this paper not only introduces the contents of the routing protocol, but also points out their advantages and drawbacks, and evaluates these protocols based on a given set of parameters such as deliverance, end-to-end-delay, meanhop and load. This paper also evaluates some drawbacks of above routing protocols. Finally, the authors suggest the research direction in routing for ad hoc mobile wireless networks in the future

A Context-aware Task Offloading Scheme in Collaborative Vehicular Edge Computing Systems

( Zilong Jin ),( Chengbo Zhang ),( Guanzhe Zhao ),( Yuanfeng Jin ),( Lejun Zhang ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.2

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.

An Inference Similarity-based Federated Learning Framework for Enhancing Collaborative Perception in Autonomous Driving

Zilong Jin,Chi Zhang,Lejun Zhang 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.5

Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.

Trajectory-prediction based relay scheme for time-sensitive data communication in VANETs

( Zilong Jin ),( Yuxin Xu ),( Xiaorui Zhang ),( Jin Wang ),( Lejun Zhang ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.8

In the Vehicular Ad-hoc Network (VANET), the data transmission of time-sensitive applications requires low latency, such as accident warnings, driving guidance, etc. However, frequent changes of topology in VANET will result in data transmission failures. In order to improve the efficiency of VANETs data transmission and increase the timeliness of data, this paper proposes a relay scheme based on Recurrent Neural Network (RNN) trajectory prediction, which can be used to select the optimal relay vehicle to transmit data. The proposed scheme learns vehicle trajectory in a distributed manner and calculates the predicted trajectory, and then the optimal vehicle can be selected to complete the data transmission, which ensures the timeliness of the data. Finally, we carry out a set of simulations to demonstrate the performance of the algorithm. Simulation results show that the proposed scheme enhances the timeliness of the data and the accuracy of the predicted driving trajectory.