Machine Learning for Data Privacy and Protection: A Systematic Review of Current Approaches and Future Directions

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@article{1709539,
      author = {Oketayo Abimbola},
      title = {Machine Learning for Data Privacy and Protection: A Systematic Review of Current Approaches and Future Directions},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {1},
      pages = {170-190},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709539.pdf},
      abstract = {The rapid growth of data-driven technologies has raised significant concerns about data privacy, emphasizing the need for innovative solutions to safeguard sensitive information. Machine learning (ML) has emerged as a promising approach to protect data privacy, offering a range of techniques to prevent unauthorized data access, ensure secure data transmission, and maintain data confidentiality. This comprehensive review provides an in-depth examination of existing methods for data privacy protection in machine learning, highlighting their strengths, limitations, and applications in various domains. We reviewed prominent Machine Learning (ML) techniques, including data anonymization, encryption, access control, and differential privacy, and discuss their effectiveness in preventing data breaches and protecting sensitive information. Our analysis also identified future research directions, including the development of more robust ML model, the integration of ML with other privacy-enhancing technologies, and the investigation of ML-based solutions for emerging data privacy challenges. This survey aims to provide a valuable resource for researchers, practitioners, and policymakers seeking to leverage ML for data privacy protection and to stimulate further research in this critical area.},
      keywords = {Data Privacy, Machine Learning, survey, Data Protection, Privacy Preservation.},
      month = {July},
  }