scholarly journals Research on Design of Fog Computing Optimization Model for Medical Big Data

2021 ◽  
Author(s):  
Baoling Qin
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Deep Learning ◽  
Optimization Model ◽  
Data Privacy ◽  
Fog Computing ◽  
Information Service ◽  
Heterogeneous Data ◽  
Data Redundancy ◽  
Medical Big Data

Targeted at the current issues of communication delay, data congestion, and data redundancy in cloud computing for medical big data, a fog computing optimization model is designed, namely an intelligent front-end architecture of fog computing. It uses the network structure characteristics of fog computing and “decentralized and local” mind-sets to tackle the current medical IoT network’s narrow bandwidth, information congestion, heavy computing burden on cloud services, insufficient storage space, and poor data security and confidentiality. The model is composed of fog computing, deep learning, and big data technology. By full use of the advantages of WiFi and user mobile devices in the medical area, it can optimize the internal technology of the model, with the help of classification methods based on big data mining and deep learning algorithms based on artificial intelligence, and automatically process case diagnosis, multi-source heterogeneous data mining, and medical records. It will also improve the accuracy of medical diagnosis and the efficiency of multi-source heterogeneous data processing while reducing network delay and power consumption, ensuring patient data privacy and safety, reducing data redundancy, and reducing cloud overload. The response speed and network bandwidth of the system have been greatly optimized in the process, which improves the quality of medical information service.

Deep learning techniques for optimizing medical big data

2020 ◽  
pp. 187-211
Author(s):  
Muhammad Imran Tariq ◽  
Shahzadi Tayyaba ◽  
Muhammad Waseem Ashraf ◽  
Valentina Emilia Balas
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Learning Techniques ◽  
Medical Big Data

Privacy Preserving Data Mining on Unstructured Data

2017 ◽  
pp. 167-190
Author(s):  
Trupti Vishwambhar Kenekar ◽  
Ajay R. Dani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Structure Data ◽  
Data Privacy ◽  
Differential Privacy ◽  
Unstructured Data ◽  
Map Reduce ◽  
Individual Data ◽  
Data Set ◽  
Privacy Preserving Data Mining

As Big Data is group of structured, unstructured and semi-structure data collected from various sources, it is important to mine and provide privacy to individual data. Differential Privacy is one the best measure which provides strong privacy guarantee. The chapter proposed differentially private frequent item set mining using map reduce requires less time for privately mining large dataset. The chapter discussed problem of preserving data privacy, different challenges to preserving data privacy in big data environment, Data privacy techniques and their applications to unstructured data. The analyses of experimental results on structured and unstructured data set are also presented.

scholarly journals Information Security in Big Data: Privacy and Data Mining

IEEE Access ◽  
2014 ◽  
Vol 2 ◽  
pp. 1149-1176 ◽  
Author(s):  
Lei Xu ◽  
Chunxiao Jiang ◽  
Jian Wang ◽  
Jian Yuan ◽  
Yong Ren
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Information Security ◽  
Data Privacy ◽  
Big Data Privacy

scholarly journals A High-Order CFS Algorithm for Clustering Big Data

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Fanyu Bu ◽  
Zhikui Chen ◽  
Peng Li ◽  
Tong Tang ◽  
Ying Zhang
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Clustering Algorithm ◽  
Big Data Analytics ◽  
Learning Model ◽  
Heterogeneous Data ◽  
High Order ◽  
Tensor Model ◽  
Industrial Internet ◽  
Deep Learning Model

With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i) an adaptive dropout deep learning model to learn features from each type of data, (ii) a feature tensor model to capture the correlations of heterogeneous data, and (iii) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.

scholarly journals A Security Model for Preserving the Privacy of Medical Big Data in a Healthcare Cloud Using a Fog Computing Facility With Pairing-Based Cryptography

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 22313-22328 ◽  
Author(s):  
Hadeal Abdulaziz Al Hamid ◽  
Sk Md Mizanur Rahman ◽  
M. Shamim Hossain ◽  
Ahmad Almogren ◽  
Atif Alamri
Keyword(s):  
Big Data ◽  
Fog Computing ◽  
Security Model ◽  
Medical Big Data ◽  
Pairing Based Cryptography

scholarly journals Deep CNN-Assisted Personalized Recommendation over Big Data for Mobile Wireless Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Yu Zheng ◽  
Xiaolong Xu ◽  
Lianyong Qi
Keyword(s):  
Wireless Networks ◽  
Big Data ◽  
Deep Learning ◽  
Heterogeneous Data ◽  
Image Features ◽  
Mobile Wireless Networks ◽  
Personalized Recommendation ◽  
Mobile Users ◽  
Mobile Wireless ◽  
Deep Cnn

At present, to improve the accuracy and performance for personalized recommendation in mobile wireless networks, deep learning has been widely concerned and employed with social and mobile trajectory big data. However, it is still challenging to implement increasingly complex personalized recommendation applications over big data. In view of this challenge, a hybrid recommendation framework, i.e., deep CNN-assisted personalized recommendation, named DCAPR, is proposed for mobile users. Technically, DCAPR integrates multisource heterogeneous data through convolutional neural network, as well as inputs various features, including image features, text semantic features, and mobile social user trajectories, to construct a deep prediction model. Specifically, we acquire the location information and moving trajectory sequence in the mobile wireless network first. Then, the similarity of users is calculated according to the sequence of moving trajectories to pick the neighboring users. Furthermore, we recommend the potential visiting locations for mobile users through the deep learning CNN network with the social and mobile trajectory big data. Finally, a real-word large-scale dataset, collected from Gowalla, is leveraged to verify the accuracy and effectiveness of our proposed DCAPR model.

A Differential Privacy Protection Query Language for Medical Data: Study Design (Preprint)

2020 ◽  
Author(s):  
Huanhuan Wang ◽  
Xiang Wu ◽  
Yongqi Tan ◽  
Hongsheng Yin ◽  
Xiaochun Cheng ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Privacy Protection ◽  
Modular Design ◽  
Differential Privacy ◽  
Query Language ◽  
Medical Data ◽  
Medical Data Mining ◽  
Medical Big Data ◽  
Mining Algorithms

BACKGROUND Medical data mining and sharing is an important process to realize the value of medical big data in E-Health applications. However, medical data contains a large amount of personal private information of patients, there is a risk of privacy disclosure when sharing and mining. Therefore, how to ensure the security of medical big data in the process of publishing, sharing and mining has become the focus of current researches. OBJECTIVE The objective of our study is to design a framework based on differential privacy protection mechanism to ensure the security sharing of medical data. We developed a privacy Protection Query Language (PQL) that can integrate multiple machine mining methods and provide secure sharing functions for medical data. METHODS This paper adopts a modular design method with three sub-modules, including parsing module, mining module and noising module. Each module encapsulates different computing devices, such as composite parser, noise jammer, etc. In the PQL framework, we apply the differential privacy mechanism to the results of the module collaborative calculation to optimize the security of various mining algorithms. These computing devices operate independently, but the mining results depend on their cooperation. RESULTS Designed and developed a query language framework that provides medical data mining, sharing and privacy preserving functions. We theoretically proved the performance of the PQL framework. The experimental results showed that the PQL framework can ensure the security of each mining result, and the average usefulness of the output results is above 97%. CONCLUSIONS We presented a security framework that enables medical data providers to securely share the health data or treatment data, and developed a usable query language based on differential privacy mechanism that enables researchers to mine potential information securely using data mining algorithms. CLINICALTRIAL

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