Algorithms for Distributed Data Stream Mining

Abstract With the advancement of network technology and large-scale computing, distributed data streams have been widely used in the application of financial risk analysis. However, while data mining reveals financial models, it also increasingly poses a threat to privacy. Therefore, how to prevent privacy leakage during the efficient mining process poses new challenges to the data mining technology. This article is mainly aimed at the current privacy data leakage in financial data mining, combined with existing data mining technology to study data mining and privacy protection. First, a data mining model for dual privacy protection is defined, which can better meet the characteristics of distributed data streams while achieving privacy protection effects. Secondly, a privacy-oriented data stream mining algorithm is proposed, which uses random interference technology to effectively protect the original sensitive data. Finally, the analysis and discussion of the algorithm in this paper through simulation experiments show that the algorithm is feasible and effective, and can better adapt to the distributed data flow distribution and dynamic characteristics, while achieving better privacy protection effects, effectively Reduced communication load.

Research on Distributed Data Stream Mining of Financial Risk Based on Double Privacy Protection

10.21203/rs.3.rs-38957/v1 ◽

2020 ◽

Author(s):

Yuhao Zhao

Keyword(s):

Data Mining ◽

Data Streams ◽

Privacy Protection ◽

Data Stream ◽

Financial Risk ◽

Data Stream Mining ◽

Distributed Data ◽

Stream Mining ◽

Mining Technology ◽

Distributed Data Streams

Abstract With the advancement of network technology and large-scale computing, distributed data streams have been widely used in the application of financial risk analysis. However, while data mining reveals financial models, it also increasingly poses a threat to privacy. Therefore, how to prevent privacy leakage during the efficient mining process poses new challenges to the data mining technology. This article is mainly aimed at the current privacy data leakage in financial data mining, combined with existing data mining technology to study data mining and privacy protection. First, a data mining model for dual privacy protection is defined, which can better meet the characteristics of distributed data streams while achieving privacy protection effects. Secondly, a privacy-oriented data stream mining algorithm is proposed, which uses random interference technology to effectively protect the original sensitive data. Finally, the analysis and discussion of the algorithm in this paper through simulation experiments show that the algorithm is feasible and effective, and can better adapt to the distributed data flow distribution and dynamic characteristics, while achieving better privacy protection effects, effectively Reduced communication load.

The Research on Distributed Data Stream Mining based on Mobile Agent

Procedia Engineering ◽

10.1016/j.proeng.2011.11.2473 ◽

2011 ◽

Vol 23 ◽

pp. 103-108 ◽

Cited By ~ 2

Author(s):

Yue Fuqiang

Keyword(s):

Mobile Agent ◽

Data Stream ◽

Data Stream Mining ◽

Distributed Data ◽

Stream Mining

Research on wireless distributed financial risk data stream mining based on dual privacy protection

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01842-x ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Yuhao Zhao

Keyword(s):

Data Mining ◽

Data Streams ◽

Privacy Protection ◽

Data Stream ◽

Financial Risk ◽

Data Stream Mining ◽

Distributed Data ◽

Stream Mining ◽

Mining Technology ◽

Distributed Data Streams

AbstractWith the advancement of network technology and large-scale computing, distributed data streams have been widely used in the application of financial risk analysis. However, while data mining reveals financial models, it also increasingly poses a threat to privacy. Therefore, how to prevent privacy leakage during the efficient mining process poses new challenges to the data mining technology. This article is mainly aimed at the current privacy data leakage in financial data mining, combined with existing data mining technology to study data mining and privacy protection. First, a data mining model for dual privacy protection is defined, which can better meet the characteristics of distributed data streams while achieving privacy protection effects. Secondly, a privacy-oriented data stream mining algorithm is proposed, which uses random interference technology to effectively protect the original sensitive data. Finally, the analysis and discussion of the algorithm in this paper through simulation experiments show that the algorithm is feasible and effective, and can better adapt to the distributed data flow distribution and dynamic characteristics, while achieving better privacy protection effects, effectively reduced communication load.

Measuring Different Tasks for Unstructured Data and High Speed Data in Data Stream Mining

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i5.582589 ◽

2019 ◽

Vol 7 (5) ◽

pp. 582-589

Author(s):

K. Rajasekhar ◽

P. Venkata Maheswara

Keyword(s):

Data Stream ◽

High Speed ◽

Unstructured Data ◽

Data Stream Mining ◽

Stream Mining ◽

High Speed Data

An Ideology for the Prediction of Critical Haplotype Blocks of Variants in Genes (Cyp2c9 And Vkorc1) for Warfarin (Anticoagulant) Drug Dosage to Treat Heart Patients Efficiently by Using Ml (Machine Learning) and Data Stream Mining Techniques

Proceedings of the 2019 3rd International Conference on Advances in Image Processing ◽

10.1145/3373419.3373424 ◽

2019 ◽

Author(s):

Hina Saeeda ◽

Muhammad Adil Abid

Keyword(s):

Machine Learning ◽

Data Stream ◽

Drug Dosage ◽

Data Stream Mining ◽

Stream Mining ◽

Haplotype Blocks ◽

Heart Patients ◽

Anticoagulant Drug

Kennard-Stone Balance Algorithm for Time-series Big Data Stream Mining

2020 International Conference on Data Mining Workshops (ICDMW) ◽

10.1109/icdmw51313.2020.00122 ◽

2020 ◽

Author(s):

Tengyue Li ◽

Simon Fong ◽

Yaoyang Wu ◽

Antonio J. Tallon-Ballesteros

Keyword(s):

Time Series ◽

Big Data ◽

Data Stream ◽

Data Stream Mining ◽

Stream Mining