scholarly journals Towards an Efficient Privacy-Preserving Decision Tree Evaluation Service in the Internet of Things

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 103 ◽  
Author(s):  
Lin Liu ◽  
Jinshu Su ◽  
Baokang Zhao ◽  
Qiong Wang ◽  
Jinrong Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Decision Tree ◽  
Secret Sharing ◽  
Cloud Model ◽  
Privacy Preserving ◽  
The Internet ◽  
Fast Development ◽  
The Internet Of Things ◽  
Tree Evaluation

With the fast development of the Internet of Things (IoT) technology, normal people and organizations can produce massive data every day. Due to a lack of data mining expertise and computation resources, most of them choose to use data mining services. Unfortunately, directly sending query data to the cloud may violate their privacy. In this work, we mainly consider designing a scheme that enables the cloud to provide an efficient privacy-preserving decision tree evaluation service for resource-constrained clients in the IoT. To design such a scheme, a new secure comparison protocol based on additive secret sharing technology is proposed in a two-cloud model. Then we introduce our privacy-preserving decision tree evaluation scheme which is designed by the secret sharing technology and additively homomorphic cryptosystem. In this scheme, the cloud learns nothing of the query data and classification results, and the client has no idea of the tree. Moreover, this scheme also supports offline users. Theoretical analyses and experimental results show that our scheme is very efficient. Compared with the state-of-art work, both the communication and computational overheads of the newly designed scheme are smaller when dealing with deep but sparse trees.

Distributed Learning on Mobile Devices: a New Approach to Data Mining in the Internet of Things

2020 ◽  
pp. 1-1
Author(s):  
Xiongtao Zhang ◽  
Xiaomin Zhu ◽  
Weidong Bao ◽  
Laurence T. Yang ◽  
Ji Wang ◽  
...  
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Mobile Devices ◽  
Distributed Learning ◽  
The Internet ◽  
New Approach ◽  
The Internet Of Things

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

Design of College English Process Evaluation System Based on Data Mining Technology and Internet of Things

2020 ◽  
Vol 16 (2) ◽  
pp. 18-33 ◽  
Author(s):  
Hongli Lou
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Process Evaluation ◽  
Evaluation System ◽  
Current Situation ◽  
The Internet ◽  
Mining Technology ◽  
College English ◽  
Procedural Evaluation ◽  
The Internet Of Things

This article proposes a new idea for the current situation of procedural evaluation of college English based on Internet of Things. The Internet of Things is used to obtain the intelligent data to enhance the teaching flexibility. The data generated during the process of procedural evaluation is carefully analyzed through data mining to infer whether the teacher's procedural evaluation in English teaching can be satisfied.

Big Data in manufacturing systems engineering – close up on a machine tool

2016 ◽  
Vol 64 (7) ◽  
Author(s):  
Christian Bauer ◽  
Zaigham-Faraz Siddiqui ◽  
Manuel Beuttler ◽  
Klaus Bauer
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
Systems Engineering ◽  
Machine Tools ◽  
Manufacturing Systems ◽  
The Internet ◽  
New Approach ◽  
New Challenges ◽  
The Internet Of Things

AbstractWith the increasing connectivity of devices, the amount of data that is recorded and ready for analysis is growing correspondingly. This is also the case for shop floors in flexible sheet metal handling and production. With the growing need for flexibility in production, the availability of machine tools is imminent. This paper shows different approaches that a classical manufacturing systems company such as TRUMPF takes in applying data mining techniques to address the new challenges which come with the Internet of things. In addition to classical methods, a new approach is introduced that does not need any alteration of the machine or its interfaces.

scholarly journals Parallel Approaches for Data Mining in the Internet of Things Realm

2018 ◽  
Vol 46 (5) ◽  
pp. 807-811 ◽  
Author(s):  
Francesco Piccialli ◽  
Salvatore Cuomo ◽  
Gwanggil Jeon
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Decision Tree Based Interference Recognition for Fog Enabled IOT Architecture

2020 ◽  
Vol 2 (1) ◽  
pp. 15-25
Author(s):  
Dr. Mugunthan S. R.
Keyword(s):  
Internet Of Things ◽  
Initial Data ◽  
Decision Tree ◽  
False Alarm ◽  
Final Stage ◽  
Cyber Attacks ◽  
The Internet ◽  
Data Set ◽  
The Internet Of Things ◽  
Decision Forest

The cyber-attacks nowadays are becoming more and more erudite causing challenges in distinguishing them and confining. These attacks affect the sensitized information’s of the network by penetrating into the network and behaving normally. The paper devises a system for such interference recognition in the internet of things architecture that is aided by the FOG. The proposed system is a combination of variety of classifiers that are founded on the decision tree as well as the rule centered conceptions. The system put forth involves the JRip and the REP tree algorithm to utilize the features of the data set as input and distinguishes between the benign and the malicious traffic in the network and includes an decision forest that is improved with the penalizing attributes of the previous trees in the final stage to classify the traffic in the network utilizing the initial data set as well as the outputs of the classifiers that were engaged in the former stages. The proffered system was examined using the dataset such BOT-Internet of things and the CICIDS2017 to evince its competence in terms of rate of false alarm, detection, and accuracy. The attained results proved that the performance of the proposed system was better compared to the exiting methodologies to recognize the interference.

scholarly journals A Smart Microcontroller Architecture for the Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1821 ◽  
Author(s):  
Zhenyu Wu ◽  
Kai Qiu ◽  
Jianguo Zhang
Keyword(s):  
Internet Of Things ◽  
Real World ◽  
Simulation Experiment ◽  
Cloud Model ◽  
Experimental Results ◽  
The Internet ◽  
Complex Problems ◽  
Logic Control ◽  
The Internet Of Things ◽  
Embedded Intelligence

The interoperations of endpoint devices are generally achieved by gateways in Internet of Things (IoT) systems. However, the gateways mainly focus on networking communication, which is lack of data logic control capabilities. The microcontrollers with embedded intelligence could work as an intermediate device to help the interconnections of the endpoint devices. Moreover, they could help control the endpoint devices. In this paper, a microcontroller architecture with intelligent and scalable characteristics is proposed. The intelligence means that the microcontroller could control the target endpoint devices by its logical circuits, and the scalability means that the microcontroller architecture could be easily extended to deal with more complex problems. Two real world industrial implementations of the proposed architecture are introduced. The implementations show that the microcontroller is important to provide the intelligent services to users in IoT systems. Furthermore, a simulation experiment based on the cloud model is designed to evaluate the proposed method. The experimental results demonstrate the effectiveness of the proposed architecture.

scholarly journals Authenticated and Privacy-Preserving Consent Management in the Internet of Things

2019 ◽  
Vol 151 ◽  
pp. 256-263 ◽  
Author(s):  
Maryline Laurent ◽  
Jean Leneutre ◽  
Sophie Chabridon ◽  
Imane Laaouane
Keyword(s):  
Internet Of Things ◽  
Privacy Preserving ◽  
The Internet ◽  
The Internet Of Things

A Semantic Sensor Web Architecture in the Internet of Things

2019 ◽  
pp. 75-97
Author(s):  
Eliot Bytyçi ◽  
Besmir Sejdiu ◽  
Arten Avdiu ◽  
Lule Ahmedi
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Sensor Data ◽  
Wireless Sensor ◽  
The Internet ◽  
Web Architecture ◽  
Hidden Knowledge ◽  
Semantic Sensor Web ◽  
The Internet Of Things ◽  
The Web

The Internet of Things (IoT) vision is connecting uniquely identifiable devices to the internet, best described through ontologies. Furthermore, new emerging technologies such as wireless sensor networks (WSN) are recognized as essential enabling component of the IoT today. Hence, the interest is to provide linked sensor data through the web either following the semantic web enablement (SWE) standard or the linked data approach. Likewise, a need exists to explore those data for potential hidden knowledge through data mining techniques utilized by a domain ontology. Following that rationale, a new lightweight IoT architecture has been developed. It supports linking sensors, other devices and people via a single web by mean of a device-person-activity (DPA) ontology. The architecture is validated by mean of three rich-in-semantic services: contextual data mining over WSN, semantic WSN web enablement, and linked WSN data. The architecture could be easily extensible to capture semantics of input sensor data from other domains as well.

Sign in / Sign up

Export Citation Format

Share Document