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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