Distributed Data Mining Research Articles

Blockchain and Access Control Encryption-Empowered IoT Knowledge Sharing for Cloud-Edge Orchestrated Personalized Privacy-Preserving Federated Learning

Federated learning (FL) is emerging as a powerful paradigm for distributed data mining in the context of Internet of Things (IoT) big data. It addresses privacy concerns associated with data outsourcing by enabling local data training and knowledge (i.e., model) sharing. However, simplistic local knowledge sharing can inadvertently expose user privacy to advanced attacks, such as model inversion or gradient leakage. Furthermore, achieving fine-grained and personalized privacy protection for IoT users remains a challenge. In this paper, we propose a novel solution called hierarchical blockchain-empowered cloud-edge orchestrated federated learning (HBCE-FL) to address these challenges. HBCE-FL is designed to provide secure, intelligent, and distributed data analysis for IoT users. To tackle FL’s privacy issues, we develop a multi-level access control encryption and blockchain-based approach for sharing IoT knowledge within the HBCE-FL framework. Our approach classifies IoT users into different levels based on their individual privacy requirements, enabling fine-grained privacy protection. The blockchain is employed for identity authentication, key management, and message sanitization. For scenarios involving IoT users with non-IID data, we integrate federated multi-task learning into HBCE-FL to ensure fairness, robustness, and privacy. Finally, we conduct experiments using classic MNIST and CIFAR10 datasets to validate our approach. The experimental results illustrate that HBCE-FL effectively achieves personalized privacy-preserving FL without losing IoT data availability. Regardless of whether IoT data are homogeneous or heterogeneous, our approach enhances model accuracy and convergence rates by enabling secure IoT knowledge access and sharing for IoT users.

Research on gaussian mixture model and its distributed data mining algorithm in wireless sensor networks

Optimization of the routing represents an important challenge when considering the rapid development of Wireless Sensor Networks (WSN), which involve efficient energy methods. Applying the effectiveness of a Deep Neural Network (DNN) and a Gaussian Mixture Model (GMM), the present article proposes an innovative method for attaining Energy-Efficient Routing (EER) in WSN. When it comes to dealing with dynamic network issues, conventional routing protocols generally conflict, resulting in unsustainable Energy consumption (EC). By applying algorithms based on data mining to adapt routing selections in an effective procedure, the GMM + DNN methodology that has been developed is able to successfully address this problem. The GMM is a fundamental Feature Extraction (FE) method for accurately representing the features of statistical analysis associated with network parameters like signal frequency, the amount of traffic, and channel states. By learning from previous data collection, the DNN, which relies on these FE, provides improved routing selections, resulting in more efficient use of energy. Since routing paths are constantly optimized to ensure dynamic adaptation, where less energy is used, networks last longer and perform more efficiently. Network simulations highlight the GMM + DNN method’s effectiveness and depict how it outperforms conventional routing methods while preserving network connectivity and data throughput. The GMM + DNN’s adaptability to multiple network topologies and traffic patterns and its durability make it an efficient EER technique in the diverse WSN context. The GMM + DNN achieves an EC of 0.561 J, outperforming the existing state-of-the-art techniques.

An Analysis of Text Mining in Big Data

The practice of extracting hidden predictive information from a database and structuring it for later use is known as data mining. Web mining, text mining, sequence mining, graph mining, temporal data mining, spatial data mining, distributed data mining, and multimedia mining are some of the diverse domains in data mining. Financial data analysis, the retail and telecommunications sectors, science and engineering, and intrusion detection and prevention are a few fields where data mining is used. Worldwide, big data analytics is a burgeoning research field in computer science and many other fields. We covered text mining methods and their uses in this paper. Text mining is becoming a significant topic of study. Text mining is the process of automatically finding new, previously undiscovered information using a computer.

A General-Purpose Distributed Analytic Platform Based on Edge Computing and Computational Intelligence Applied on Smart Grids.

Currently, in many data landscapes, the information is distributed across various sources and presented in diverse formats. This fragmentation can pose a significant challenge to the efficient application of analytical methods. In this sense, distributed data mining is mainly based on clustering or classification techniques, which are easier to implement in distributed environments. However, the solution to some problems is based on the usage of mathematical equations or stochastic models, which are more difficult to implement in distributed environments. Usually, these types of problems need to centralize the required information, and then a modelling technique is applied. In some environments, this centralization may cause an overloading of the communication channels due to massive data transmission and may also cause privacy issues when sending sensitive data. To mitigate this problem, this paper describes a general-purpose distributed analytic platform based on edge computing for distributed networks. Through the distributed analytical engine (DAE), the calculation process of the expressions (that requires data from diverse sources) is decomposed and distributed between the existing nodes, and this allows sending partial results without exchanging the original information. In this way, the master node ultimately obtains the result of the expressions. The proposed solution is examined using three different computational intelligence algorithms, i.e., genetic algorithm, genetic algorithm with evolution control, and particle swarm optimization, to decompose the expression to be calculated and to distribute the calculation tasks between the existing nodes. This engine has been successfully applied in a case study focused on the calculation of key performance indicators of a smart grid, achieving a reduction in the number of communication messages by more than 91% compared to the traditional approach.

A Reliable Application of MPC for Securing the Tri-Training Algorithm

Due to the widespread use of distributed data mining techniques in a variety of areas, the issue of protecting the privacy of sensitive data has received increasing attention in recent years. Privacy-preserving distributed data mining (PPDDM) focuses on decentralized data analysis without the disclosure of sensitive information from data owner. However, the previous PPDDM mostly works on a limited amount of labeled data. In contrast to the real world, unlabeled data is abundance and labeled data is scarce. The objectives of this paper are to study and to analyze privacy-preserving properties of semi-supervised learning (SSL) algorithm with the combination of labeled and unlabeled data, where data is distributed among multiple data owners. In this paper we propose a Privacy-preserving Distributed Data Mining (PPDDM) method by designing a reliable application of secure MPC to semi-supervised tri-training algorithms. We simulate the original tri-training algorithm and tri-training algorithm with secure MPC using a different types of classifiers and datasets. The simulation results show that tri-training in secure MPC has almost same accuracy compared to original tri-training algorithm. We also compare execution time in addition to performance evaluation of tri-training in secure and the original tri-training algorithms.

Privacy protection based distributed clustering with deep learning algorithm for distributed data mining

Distributed Data Mining (DDM) is vital in various applications for processing large volumes of data. The datasets are saved in the local databases and operated by local communities, but it provides the solution locally and globally. However, the datasets are stored in a distributed manner which affects the scalability and reliability issues. In addition, locally stored data is influenced by security and privacy challenges. In addition, the third party may access the DDM, which causes authorization issues. Therefore, the DDM process fuses sensor data from different sources to improve knowledge discovery. During this process, the DDM faces several issues such as security concerns, privacy restrictions, technical barriers, and trust issues. To address these issues, distributed data mining (DDM) should be improved to handle homogeneous and heterogeneous data. This work uses the privacy protection-based distributed clustering (PPDC) algorithm to handle the privacy and security challenges while analyzing the distributed data. The clustering algorithm generates the semi-trusted third parties to form the cluster, which protects the data from unauthorized users. The semi-trusted party protect the locally analyzed solution by creating the random vector-based trusted process. Further, the process uses the optimized deep learning approach and clustering to improve the heterogeneous data analysis. Then the effectiveness of the introduced PPDC method is compared with existing methods, and the PPDC algorithm ensures the 0.202 error rate, 0.95 % of accuracy and manages the data security.

Privacy-Preserving Data Mining on Blockchain-Based WSNs

Currently, the computational power present in the sensors forming a wireless sensor network (WSN) allows for implementing most of the data processing and analysis directly on the sensors in a decentralized way. This shift in paradigm introduces a shift in the privacy and security problems that need to be addressed. While a decentralized implementation avoids the single point of failure problem that typically applies to centralized approaches, it is subject to other threats, such as external monitoring, and new challenges, such as the complexity of providing decentralized implementations for data mining algorithms. In this paper, we present a solution for privacy-aware distributed data mining on wireless sensor networks. Our solution uses a permissioned blockchain to avoid a single point of failure in the system. Contracts are used to construct an onion-like structure encompassing the Hoeffding trees and a route. The onion-routed query conceals the network identity of the sensors from external adversaries, and obfuscates the actual computation to hide it from internally compromised nodes. We validate our solution on a use case related to an air quality-monitoring sensor network. We compare the quality of our model against traditional models to support the feasibility and viability of the solution.

Construction of Higher Education Management Data Analysis Model Based on Association Rules

This paper adopts the analytical method of intelligent programming to conduct in-depth research and analysis on university education management and designs a corresponding university education management model to be applied in actual teaching. Based on the basic theories and methods of big data distributed data mining and machine learning, automated machine learning, and big data programming computational methods, this paper combines the importance and technical challenges of the algorithms themselves and the background of the practical application needs of the industry and firstly selects a series of data mining and machine learning algorithms that are commonly used based on high complexity, outstanding computational efficiency problems, and difficult to design distributed algorithms. The research on efficient large-scale distributed parallelized data mining and machine learning methods and algorithms is carried out. As an innovative teaching model, the experiential teaching model focuses on the cultivation of individual students’ independent learning ability and subjective initiative, which not only can effectively activate the classroom atmosphere and improve the teaching effect but also meet the requirements of classroom teaching for the development of the times. The empirical evidence of the experiential teaching model proposed in this paper is carried out, and the implementation process of the experiential teaching model of high school physics is further improved. In terms of the overall implementation effect, the experiential teaching model proposed in the paper can deepen students’ understanding of concepts and physical laws, thus enabling them to solve physics problems better, and has a positive and active effect on enhancing students’ learning interests and attitudes. In the case of system security, the average comprehensive index of each unit is 61.11, 22 are higher than the average index, accounting for 61.11%, 14 are lower than the average index, accounting for 38.89%, and 23 management departments’ management systems passed the safety monitoring, accounting for 63.89%. At the same time, the proposed model also provides educational practitioners with a more reliable framework for instructional design, which has realistic reference value and significance.

Privacy-Aware and Secure Decentralized Air Quality Monitoring

Indoor Air Quality monitoring is a major asset to improving quality of life and building management. Today, the evolution of embedded technologies allows the implementation of such monitoring on the edge of the network. However, several concerns need to be addressed related to data security and privacy, routing and sink placement optimization, protection from external monitoring, and distributed data mining. In this paper, we describe an integrated framework that features distributed storage, blockchain-based Role-based Access Control, onion routing, routing and sink placement optimization, and distributed data mining to answer these concerns. We describe the organization of our contribution and show its relevance with simulations and experiments over a set of use cases.

Clustering Optimization Algorithm for Data Mining Based on Artificial Intelligence Neural Network

Social production and life have become increasingly prominent. Cluster analysis is the basis for further processing of the data. The concept of data mining and the application of neural networks in data mining are introduced. According to the related technology of data mining, this article introduces in detail the two-layer perceptron, backpropagation (BP) neural network, RBF radial basis function network for processing classification problems, and self-organizing map (SOM) self-organizing neural network for unsupervised clustering problems. According to the characteristics of self-adaptive and self-organizing capabilities of these algorithms, we learn and design and implement data mining clustering optimization algorithms. In this paper, the neural network-based data mining process consists of three stages: data preparation, rule extraction, and rule evaluation. This paper studies the teaching-type and decomposition-type rule extraction algorithms. After analyzing the BP decomposition-type algorithm, the correlation method is used to calculate the correlation of the input and output neurons. After sorting by the degree of correlation, the RBF neural network is used for node selection. This can greatly reduce the number of input nodes of the neural network, simplify the network structure, reduce the number of recursive splits of the subnet, and improve calculation efficiency. Taking the model as an example, the training error is calculated through data mining technology and clustering algorithm. Data mining clustering optimization algorithm mainly improves the popular neural network from two aspects: finer model design and model pruning, and simulates model complexity, computational complexity, and errors through simulation experiments. The rate is measured, and finally, the simulation experiment is performed. The results show that the proposed algorithm for differential distributed data mining has higher accuracy and stronger convergence ability and overcomes the shortcomings and shortcomings of several original genetic algorithm optimization neural network data mining models; it can effectively improve the searchability and search accuracy of the algorithm and improve the efficiency of data mining. Accuracy and accuracy have a wide range of applications.

Novel method for optimizing performance in resource constrained distributed data streams

The Big Data Era has presented many opportunities for using data mining techniques to discover knowledge patterns across large and diverse collections of data where the volume of data is growing at an exponential rate. Recent approaches to Distributed Data Mining (DDM) have focused on addressing the heterogeneous nature of data sources. However, such approaches do not prioritize the reduction of data communication costs which could be prohibitive in large scale sensor networks where bandwidth is a limited resource. In fact, higher communication and computational costs are the two most prominent problems that have been encountered in heterogeneous distributed environments. Moreover, an effort to decrease the communications load in the distributed environment has an adverse influence on the classification accuracy. Therefore, the research challenge lies in maintaining a balance between transmission cost, computational cost, and accuracy. This paper proposes an algorithm Performance Optimizer in Distributed Stream Mining (PODSM) based on Bayesian Inference to reduce the communication volume and resource time in a heterogeneous distributed data mining environment while retaining prediction accuracy. The approach used in this work exploits the past data for calculating statistics and these statistics are then utilized for the new data. In other words, it imparts the ability to learn from experiences. As a result, our experimental evaluation reveals that a significant reduction in the communication load and an improvement in classification response time can be achieved across a diverse range of dataset types. Reduction of 34.66% was obtained with regard to communication overhead for one of the datasets with huge savings of nearly 27% in resource time. Importantly, instead of showing a negative effect on accuracy, this dataset observes an increment of 0.44% in accuracy.

Implementing reverse up growth tracking approach under distributed data mining

Data mining is the methodology which discovers useful and hidden information from large databases. Many researchers have proposed innumerable algorithms in the field of data mining. In this system improvised UP-growth is considered for mining high utility itemset from potential high utility itemset and improvised under different constraints. The node utility and reorganised transaction utility are the aspects considered as the key term in the proposed system which are manipulated using the technique as in UP-growth. However, mining potential high utility itemset from RTU using UP-growth needs number of tree traversals. This is reduced in the proposed system by introducing bottom-up approach and merging certain manipulations. However, working the system as a sequential process will be time consuming. Distributed environment is considered in the proposed system to overwhelm the problem in existing methodology.

An ensemble random forest algorithm for privacy preserving distributed medical data mining

As the voluminous amount of data is generated because of inexorably widespread proliferation of electronic data maintained using the Electronic Health Records (EHRs). Medical health facilities have great potential to discern the patterns from this data and utilize them in diagnosing a specific disease or predicting outbreak of an epidemic etc. This discern of patterns might reveal sensitive information about individuals and this information is vulnerable to misuse. This is, however, a challenging task to share such sensitive data as it compromises the privacy of patients. In this paper, a random forest-based distributed data mining approach is proposed. Performance of the proposed model is evaluated using accuracy, f-measure and appa statistics analysis. Experimental results reveal that the proposed model is efficient and scalable enough in both performance and accuracy within the imbalanced data and also in maintaining the privacy by sharing only useful healthcare knowledge in the form of local models without revealing and sharing of sensitive data.

Evaluation Platform for DDM Algorithms With the Usage of Non-Uniform Data Distribution Strategies

Huge amounts of data are collected in numerous independent data storage facilities around the world. However, how the data is distributed between physical locations remains unspecified. Downloading all of the data for the purpose of processing it is undesirable and sometimes even impossible. Various methods have been proposed for performing data mining tasks, but the main problem is the lack of an objective strategy for comparing them. The authors present current research on a novel evaluation platform for distributed data mining (DDM) algorithms. The proposed platform opens up a new field to evaluate algorithms in terms of the quality of the results, transfer used, and speed, but also for the use of a non-uniform data distribution among independent nodes during algorithm evaluation. This work introduces a ‘data partitioning strategy’ term referring to a specific, not necessarily uniform data distribution. A brief evaluation for three clustering algorithms is also reported, showing the usability and simplicity of identifying differences in processing with the use of the platform.

A systematic review on privacy-preserving distributed data mining

Combining and analysing sensitive data from multiple sources offers considerable potential for knowledge discovery. However, there are a number of issues that pose problems for such analyses, including technical barriers, privacy restrictions, security concerns, and trust issues. Privacy-preserving distributed data mining techniques (PPDDM) aim to overcome these challenges by extracting knowledge from partitioned data while minimizing the release of sensitive information. This paper reports the results and findings of a systematic review of PPDDM techniques from 231 scientific articles published in the past 20 years. We summarize the state of the art, compare the problems they address, and identify the outstanding challenges in the field. This review identifies the consequence of the lack of standard criteria to evaluate new PPDDM methods and proposes comprehensive evaluation criteria with 10 key factors. We discuss the ambiguous definitions of privacy and confusion between privacy and security in the field, and provide suggestions of how to make a clear and applicable privacy description for new PPDDM techniques. The findings from our review enhance the understanding of the challenges of applying theoretical PPDDM methods to real-life use cases, and the importance of involving legal-ethical and social experts in implementing PPDDM methods. This comprehensive review will serve as a helpful guide to past research and future opportunities in the area of PPDDM.

A Survey of Data Mining Activities in Distributed Systems

Distributed systems, which may be utilized to do computations, are being developed as a result of the fast growth of sharing resources. Data mining, which has a huge range of real applications, provides significant techniques for extracting meaningful and usable information from massive amounts of data. Traditional data mining methods, on the other hand, suppose that the data is gathered centrally, stored in memory, and is static. Managing massive amounts of data and processing them with limited resources is difficult. Large volumes of data, for instance, are swiftly generated and stored in many locations. This becomes increasingly costly to centralize them at a single location. Furthermore, traditional data mining methods typically have several issues and limitations, such as memory restrictions, limited processing ability, and insufficient hard drive space, among others. To overcome the following issues, distributed data mining's have emerged as a beneficial option in several applications According to several authors, this research provides a study of state-of-the-art distributed data mining methods, such as distributed common item-set mining, distributed frequent sequence mining, technical difficulties with distributed systems, distributed clustering, as well as privacy-protection distributed data mining. Furthermore, each work is evaluated and compared to the others.

An Ensemble Random Forest Algorithm for Privacy Preserving Distributed Medical Data Mining

As the voluminous amount of data is generated because of inexorably widespread proliferation of electronic data maintained using the Electronic Health Records (EHRs). Medical health facilities have great potential to discern the patterns from this data and utilize them in diagnosing a specific disease or predicting outbreak of an epidemic etc. This discern of patterns might reveal sensitive information about individuals and this information is vulnerable to misuse. This is, however, a challenging task to share such sensitive data as it compromises the privacy of patients. In this paper, a random forest-based distributed data mining approach is proposed. Performance of the proposed model is evaluated using accuracy, f-measure and appa statistics analysis. Experimental results reveal that the proposed model is efficient and scalable enough in both performance and accuracy within the imbalanced data and also in maintaining the privacy by sharing only useful healthcare knowledge in the form of local models without revealing and sharing of sensitive data.

Development Trend of Intelligent Transportation in the Era of Big Data

The arrival of the big data era has brought great convenience to people’s real life. All aspects of our lives follow the development of the big data era. In the context of the era of big data, through data collection and analysis, the current urban transportation has been reasonably developed, laying a good foundation for the development of intelligent transportation in the future, and can solve various current problems. Based on the analysis of the traffic system, this article finds that the real-time road condition prediction of common functions is highly correlated with route guidance. Therefore, this paper uses a method based on historical data to predict road conditions, and uses the prediction results to dynamically allocate road segments, and then uses the relevant single-source shortest path algorithm to simulate the optimal path. By analyzing the application advantages of data mining in intelligent transportation, Baum Welch’s algorithm is used for data mining in a real-time traffic prediction system based on historical data. Real-time road conditions are matched with historical data models to predict road conditions. Finally, analyzed and designed the big data cloud platform framework applied to it to achieve higher efficiency. The experimental research results show that, by studying the development of urban intelligent transportation systems, the purpose of this paper is to make better use of existing resources to process data more efficiently, and then combine distributed data mining algorithms to optimize the processed data and apply it to in the intelligent transportation system, an intelligent management and control system that is more efficient, accurate, and user experience better can be established.

A differentially private distributed data mining scheme with high efficiency for edge computing

A wide range of data mining applications benefit from the low latency offered by edge computing. However, edge computing suffers from limited computing resources, which inhibits the applications of the computationally expensive data mining methods. In the edge-cloud environment, usually, the participants turn to collaboratively train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to sharing the own data for the privacy concerns. To handle such disparate goals, we focus on tree-based distributed data mining scheme with differential privacy, which is computationally friendly. The basic idea of our approach is based on a distributed ensemble strategy. Each participant builds an elegant decision model based on their own data, which has a good tradeoff between the computation and the accuracy of the data distribution, and shares it with other participants after being injected with the elaborate noise. Then the useful knowledge transferred from the decision models is acquired by other participants in an adaptive ensemble strategy. Both the theoretical analysis and the experiments show that our scheme provides an efficient data mining manner that can achieve a good prediction accuracy while providing rigorous privacy guarantee over the distributed data.

Multi-Agent System Combined With Distributed Data Mining for Mutual Collaboration Classification

Distributed Data Mining (DDM) has been proposed as a means to deal with the analysis of distributed data, where DDM discovers patterns and implements prediction based on multiple distributed data sources. However, DDM faces several problems in terms of autonomy, privacy, performance and implementation. DDM requires homogeneity regarding environment, control, administration and the classification algorithm(s), and such that requirements are too strict and inflexible in many applications. In this paper, we propose the employment of a Multi-Agent System (MAS) to be combined with DDM (MAS-DDM). MAS is a mechanism for creating goal-oriented autonomous agents within shared environments with communication and coordination facilities. We shall show that MAS-DDM is both desirable and beneficial. In MAS-DDM, agents could communicate their beliefs (calculated classification) by covering private and non-sharable data, and other agents decide whether the use of such beliefs in classifying instances and adjusting their prior assumptions about each class of data. In MAS-DDM, we will develop and use a modified Naive Bayesian algorithm because (1) Naive Bayesian has been shown to be the most used algorithm to deal with uncertain data, and (2) to show that even if all agents in MAS-DDM use the same algorithm, MAS-DDM preforms better than DDM approaches with non-communicating processes. Point (2) provide an evidence that the exchange of information between agents helps in increasing the accuracy of the classification task significantly.