Differential privacy data publishing in the big data platform of precise poverty alleviation

With the advent of the era of big data, people’s lives have undergone earth-shaking changes, not only getting rid of the cumbersome traditional data collection but also collecting and sorting information directly from people’s footprints on social networks. This paper explores and analyzes the privacy issues in current social networks and puts forward the protection strategies of users’ privacy data based on data mining algorithms so as to truly ensure that users’ privacy in social networks will not be illegally infringed in the era of big data. The data mining algorithm proposed in this paper can protect the user’s identity from being identified and the user’s private information from being leaked. Using differential privacy protection methods in social networks can effectively protect users’ privacy information in data publishing and data mining. Therefore, it is of great significance to study data publishing, data mining methods based on differential privacy protection, and their application in social networks.

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Differential Privacy in Power Big Data Publishing

Communications in Computer and Information Science - Parallel Architecture, Algorithm and Programming ◽

10.1007/978-981-10-6442-5_45 ◽

2017 ◽

pp. 471-479

Author(s):

Ping Kong ◽

Xiaochun Wang ◽

Boyi Zhang ◽

Yidong Li

Keyword(s):

Big Data ◽

Differential Privacy ◽

Data Publishing

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Design and Implementation of Componentized Big Data Platform

Proceedings of the 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence ◽

10.1145/3438872.3439086 ◽

2020 ◽

Author(s):

Naiwang Guo ◽

Quanjiang Shen ◽

Hongshan Yang

Keyword(s):

Big Data ◽

Design And Implementation ◽

Data Platform

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Construction of a multi-source heterogeneous hybrid platform for big data

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-215138 ◽

2021 ◽

pp. 1-10

Author(s):

Ying Wang ◽

Yiding Liu ◽

Minna Xia

Keyword(s):

Big Data ◽

Data Analysis ◽

Forest Fire ◽

Original Data ◽

Big Data Analysis ◽

Multiple Sources ◽

Data Types ◽

Fire Monitoring ◽

Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

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