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Non-Cryptographic Privacy Preserving Machine Learning Methods: A Review

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Advanced Engineering, Technology and Applications (ICAETA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1983))

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Abstract

In recent years, the use of Machine Learning (ML) techniques to exploit data and produce predictive models has become widespread in decision-making and problem-solving across various fields, including healthcare, energy, retail, transportation, and many more. Generally, a well-performing ML model requires large volumes of training data. However, collecting data and using it to predict behavior poses significant challenges to the privacy of individuals and organizations, such as data breaches, loss of privacy, and corresponding financial damage. Therefore, well-designed privacy-preserving ML (PPML) methods are significantly required for many emerging applications to mitigate these problems. This paper provides a comprehensive review of non-cryptographic privacy-preserving ML along with selected methods, such as differential privacy and federated learning. This paper aims to provide a roadmap for future research directions in the PPML field.

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Author information

Authors and Affiliations

  1. Istanbul Medipol University, Istanbul, Turkey

    Kevser Şahinbaş

  2. University of Stavanger, Rogaland, Norway

    Ferhat Ozgur Catak

  3. Old Dominion University, Norfolk, VA, USA

    Murat Kuzlu & Maliha Tabassum

  4. Virginia Commonwealth University, Richmond, VA, USA

    Salih Sarp

Authors
  1. Kevser Şahinbaş
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  2. Ferhat Ozgur Catak
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  3. Murat Kuzlu
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  4. Maliha Tabassum
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  5. Salih Sarp
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Corresponding author

Correspondence to Murat Kuzlu .

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Editors and Affiliations

  1. University of Catania, Catania, Italy

    Alessandro Ortis

  2. Istinye University, Istanbul, Türkiye

    Alaa Ali Hameed

  3. National University of Computer and Emerging Sciences, Islamabad, Pakistan

    Akhtar Jamil

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Şahinbaş, K., Catak, F.O., Kuzlu, M., Tabassum, M., Sarp, S. (2024). Non-Cryptographic Privacy Preserving Machine Learning Methods: A Review. In: Ortis, A., Hameed, A.A., Jamil, A. (eds) Advanced Engineering, Technology and Applications. ICAETA 2023. Communications in Computer and Information Science, vol 1983. Springer, Cham. https://doi.org/10.1007/978-3-031-50920-9_32

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  • DOI: https://doi.org/10.1007/978-3-031-50920-9_32

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  • Online ISBN: 978-3-031-50920-9

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