PPSF: an open-source privacy-preserving and security mining framework

Lin, Jerry Chun-Wei and Fournier-Viger, Philippe and Wu, Lintai and Gan, Wensheng and Djenouri, Youcef and Zhang, Ji (2018) PPSF: an open-source privacy-preserving and security mining framework. In: 18th IEEE International Conference on Data Mining Workshops (ICDMW 2018), 17-20 Nov 2018, 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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Poster)
Refereed: Yes
Item Status: Live Archive
Additional Information: c. 2018 IEEE.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Date Deposited: 23 Jul 2020 06:33
Last Modified: 14 Sep 2020 05:45
Uncontrolled Keywords: privacy-preserving data mining, privacy-preserving utility mining, security, data anonymity
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: https://doi.org/10.1109/ICDMW.2018.00208
URI: http://eprints.usq.edu.au/id/eprint/36147

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