• Journal of Internet Computing and Services
• /
• v.24 no.6
• /
• pp.1-11
• /
• 2023

Federated learning (FL) is a ground breaking machine learning paradigm that allow smultiple participants to collaboratively train models in a cloud environment, all while maintaining the privacy of their raw data. This approach is in valuable in applications involving sensitive or geographically distributed data. However, one of the challenges in FL is dealing with heterogeneous and non-independent and identically distributed (non-IID) data across participants, which can result in suboptimal model performance compared to traditionalmachine learning methods. To tackle this, we introduce FedGCD, a novel FL algorithm that employs Graph Neural Network (GNN)-based community detection to enhance model convergence in federated settings. In our experiments, FedGCD consistently outperformed existing FL algorithms in various scenarios: for instance, in a non-IID environment, it achieved an accuracy of 0.9113, a precision of 0.8798,and an F1-Score of 0.8972. In a semi-IID setting, it demonstrated the highest accuracy at 0.9315 and an impressive F1-Score of 0.9312. We also introduce a new metric, nonIIDness, to quantitatively measure the degree of data heterogeneity. Our results indicate that FedGCD not only addresses the challenges of data heterogeneity and non-IIDness but also sets new benchmarks for FL algorithms. The community detection approach adopted in FedGCD has broader implications, suggesting that it could be adapted for other distributed machine learning scenarios, thereby improving model performance and convergence across a range of applications.

• International conference on construction engineering and project management
• /
• 2024.07a
• /
• pp.1256-1263
• /
• 2024

In the realm of Architecture, Engineering, and Construction (AEC) education, various factors play a crucial role in shaping students' acceptance of the learning environments facilitated by visualization technologies, such as virtual reality (VR). Works on leveraging the heterogeneous educational information (i.e., pedagogical data, student performance data, and student survey data) to identify essential factors influencing students' learning experience and performance in virtual environments are still insufficient. This research proposed KnowLearn, an interactive learning assistant system, to integrate an educational knowledge graph (KG) and a locally deployed large language model (LLM) to generate real-time personalized learning recommendations. As the knowledge base of KnowLearn, the educational KG accommodated multi-faceted educational information from twelve perspectives, such as the teaching content, students' academic performance, and their perceived confidence in a specific course from the AEC discipline. A heterogeneous graph attention network (HAN) was utilized to infer the latent information in the KG and, thus, identified the perceived confidence, intention to use, and performance in a relevant quiz as the top three indicators that significantly influenced students' learning outcomes. Based on the information preserved in the KG and learned from the HAN model, the LLM enhanced the personalization of recommendations concerning adopting virtual learning environments while protecting students' privacy. The proposed KnowLearn system is expected to feasibly provide enhanced recommendations on the teaching module design for educators from the AEC domain.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.57-84
• /
• 2023

With the development of information and communications technology (ICT) and big data technology, anyone can easily obtain and utilize vast amounts of data through the Internet. Therefore, the capability of selecting high-quality data from a large amount of information is becoming more important than the capability of just collecting them. This trend continues in academia; literature reviews, such as systematic and non-systematic reviews, have been conducted in various research fields to construct a healthy knowledge structure by selecting high-quality research from accumulated research materials. Meanwhile, after the COVID-19 pandemic, remote healthcare services, which have not been agreed upon, are allowed to a limited extent, and new healthcare services such as health recommender systems (HRS) equipped with artificial intelligence (AI) and big data technologies are in the spotlight. Although, in practice, HRS are considered one of the most important technologies to lead the future healthcare industry, literature review on HRS is relatively rare compared to other fields. In addition, although HRS are fields of convergence with a strong interdisciplinary nature, prior literature review studies have mainly applied either systematic or non-systematic review methods; hence, there are limitations in analyzing interactions or dynamic relationships with other research fields. Therefore, in this study, the overall network structure of HRS and surrounding research fields were identified using citation network analysis (CNA). Additionally, in this process, in order to address the problem that the latest papers are underestimated in their citation relationships, the GraphSAGE algorithm was applied. As a result, this study identified 'recommender system', 'wireless & IoT', 'computer vision', and 'text mining' as increasingly important research fields related to HRS research, and confirmed that 'personalization' and 'privacy' are emerging issues in HRS research. The study findings would provide both academic and practical insights into identifying the structure of the HRS research community, examining related research trends, and designing future HRS research directions.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.3
• /
• pp.505-514
• /
• 2012

Because people usually establish their online social network based on their offline relationship, the social networks (i.e., the graph of friendship relationships) are often used to share contents. Mobile devices let it easier in these days, but it also increases the privacy risk such as access control of shared data and relationship exposure to untrusted server. To control the access on encrypted data and protect relationship from the server, M. Atallah et al. proposed a hop-based scheme in 2009. Their scheme assumed a distributed environment such as p2p, and each user in it shares encrypted data on their social network. On the other hand, it is very inefficient to keep their relationship private, so we propose an improved scheme. In this paper, among encrypted contents and relationships, some authenticated users can only access the data in distributed way. For this, we adopt 'circular-secure symmetric encryption' first. Proposed scheme guarantees the improved security and efficiency compared to the previous work.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.4
• /
• pp.731-751
• /
• 2021

The Internet of Things is a huge group of devices communicating each other and the interconnection of objects in the network is a basic requirement. Choosing a reliable device is critical because malicious devices can compromise networks and services. However, it is difficult to create a trust management model due to the mobility and resource constraints of IoT devices. For the centralized approach, there are issues of single point of failure and resource expansion and for the distributed approach, it allows to expand network without additional equipment by interconnecting each other, but it has limitations in data exchange and storage with limited resources and is difficult to ensure consistency. Recently, trust management models using fog nodes and blockchain have been proposed. However, blockchain has problems of low throughput and delay. Therefore, in this paper, a trust management model for selecting reliable devices in a fog-based IoT environment is proposed by applying IOTA, a blockchain technology for the Internet of Things. In this model, Directed Acyclic Graph-based ledger structure manages trust data without falsification and improves the low throughput and scalability problems of blockchain.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.4
• /
• pp.657-667
• /
• 2020

The Android framework allows apps to take full advantage of personal information through granting single permission, and does not determine whether the data being leaked is actual personal information. To solve these problems, we propose a tool with static/dynamic analysis. The tool analyzes the Source and Sink used by the target app, to provide users with information on what personal information it used. To achieve this, we extracted the Source and Sink through Control Flow Graph and make sure that it leaks the user's privacy when there is a Source-to-Sink flow. We also used the sensitive permission information provided by Google to obtain information from the sensitive API corresponding to Source and Sink. Finally, our dynamic analysis tool runs the app and hooks information from each sensitive API. In the hooked data, we got information about whether user's personal information is leaked through this app, and delivered to user. In this process, an automated Source/Sink classification model was applied to collect latest Source/Sink information, and the we categorized latest release version of Android(9.0) with 88.5% accuracy. We evaluated our tool on 2,802 APKs, and found 850 APKs that leak personal information.

• Journal of Internet Computing and Services
• /
• v.22 no.5
• /
• pp.27-33
• /
• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.