PPSF: an open-source privacy-preserving and security mining framework
Paper
Paper/Presentation Title | PPSF: an open-source privacy-preserving and security mining framework |
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Presentation Type | Paper |
Authors | Lin, Jerry Chun-Wei (Author), Fournier-Viger, Philippe (Author), Wu, Lintai (Author), Gan, Wensheng (Author), Djenouri, Youcef (Author) and Zhang, Ji (Author) |
Editors | Tong, Hanghang, Li, Zhenhui (Jessie), Zhu, Feida and Yu, Jeffrey |
Journal or Proceedings Title | Proceedings of the 18th IEEE International Conference on Data Mining Workshops (ICDMW 2018) |
ERA Conference ID | 42936 |
Article Number | 8637434 |
Number of Pages | 5 |
Year | 2018 |
Place of Publication | New York, United States |
ISBN | 9781538692882 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICDMW.2018.00208 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/8637434 |
Conference/Event | 18th IEEE International Conference on Data Mining Workshops (ICDMW 2018) |
IEEE International Conference on Data Mining | |
Event Details | IEEE International Conference on Data Mining ICDM Rank A A A A A A A A A |
Event Details | 18th IEEE International Conference on Data Mining Workshops (ICDMW 2018) Event Date 17 to end of 20 Nov 2018 Event Location Singapore |
Abstract | In recent decades, preserving privacy and ensuring the security of data has emerged as important issues as confidential information or private data may be revealed by powerful data mining tools. Although several frameworks and tools have been presented to handle such issues, they mostly implement data anonymity techniques. Thus, this paper presents a novel Privacy-Preserving and Security Mining Framework (PPSF), which focuses on privacy-preserving data mining and data security. PPSF is an open-source data mining library, which offers several algorithms for: (1) data anonymity, (2) privacy-preserving data mining (PPDM), and (3) privacy-preserving utility mining (PPUM). PPSF has a user-friendly interface that allows to run algorithms and display the results, and it is an active project with regular releases of new algorithms, optimizations and documentation. |
Keywords | privacy-preserving data mining, privacy-preservingutility mining, security, data anonymity |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | c. 2018 IEEE. |
Byline Affiliations | Western Norway University of Applied Sciences, Norway |
Harbin Institute of Technology, China | |
Norwegian University of Science and Technology, Norway | |
School of Agricultural, Computational and Environmental Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q52w2/ppsf-an-open-source-privacy-preserving-and-security-mining-framework
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