https://www.plomscience.com/journals/index.php/PLOMSAI <p><strong>PLOMS AI </strong>is a comprehensive journal aiming to proceed science-society interactions . The open access strategy offers increased vulnerability of the research and help in dissemination of research results, as well. We believe that all accurate scientific results have to be published and disseminated by being freely accessible to all.<br /><br /><strong>PLOMS AI</strong> accepts research in areas related to Artificial Intelligence, Computational Intelligence, and bio inspired related areas. The submitted manuscripts are evaluated on the basis of high ethical standards, accurate methodology, scientific and perceived novelty.<br /><br /><strong>Types of articles:</strong><br /><strong>Original research</strong> that contributes to the base of scientific knowledge <br /><strong>Systematic reviews </strong>whose methods ensure the comprehensive and unbiased sampling of existing literature.<br /><strong>Qualitative research</strong> that adheres to appropriate study design and reporting guidelines.<br /><strong>Other submissions</strong> that describes methods, software, databases, or other tools that if they follow the appropriate reporting guidelines. accepts research in areas related to Artificial Intelligence, Computational Intelligence, and bio inspired related areas. 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Additionally, you may be required to pay legal fees.</p> <p>If you become aware of any unauthorized use of PLOMS LLC. copyright content that violates or may violate the license above, please contact :</p> <p><strong>[email protected].</strong></p> <p><strong>Infringing content</strong></p> <p>If you become aware of any content on the website that you feel violates your or another person's copyright, please notify <strong>[email protected]</strong>.</p> https://www.plomscience.com/journals/index.php/PLOMSAI/article/view/24 <p>Privacy-preserving data mining (PPDM) has become a significant area of<br>interest for researchers, facilitating the sharing and analysis of sensitive information while<br>ensuring privacy protection. This paper investigates methods for maintaining data confidentiality<br>while retaining the critical attributes necessary for analysis. The authors assess<br>the efficacy of various PPDM techniques against criteria such as performance, data usability,<br>and levels of uncertainty. The key findings and limitations of each approach are<br>thoroughly reviewed and summarized. Various PPDM techniques present distinct advantages<br>alongside certain limitations: Anonymization guarantees the anonymity of data<br>owners but is vulnerable to linking attacks. Perturbation protects attributes independently<br>but does not allow for the reconstruction of original values from the altered data.<br>Randomization provides robust privacy protection but diminishes data utility due to the<br>introduction of noise. Cryptographic methods offer strong security and utility but tend to<br>be less efficient than other strategies. No single technique outperforms all criteria; rather,<br>each is more effective under particular circumstances. This paper delivers a comparative<br>analysis of PPDM techniques, emphasizing their strengths and weaknesses, and offers<br>insights into their applicability across different scenarios.</p> Aziza Aldfaii Rabie Ramadan Copyright (c) 2025 PLOMS AI https://creativecommons.org/licenses/by-nc/4.0 2025-05-01 2025-05-01 5 1 12 12