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    Non-cryptographic privacy preserving machine learning methods: a review
    (Springer Science and Business Media Deutschland GmbH, 2024) Şahinbaş, Kevser; Çatak, Ferhat Özgür; Kuzlu, Murat; Tabassum, Maliha; Sarp, Salih
    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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    Secure future healthcare applications through federated learning approaches
    (Springer Science and Business Media Deutschland GmbH, 2024) Tabassum, Maliha; Kuzlu, Murat; Çatak, Ferhat Özgür; Sarp, Salih; Şahinbaş, Kevser
    The healthcare field is so sensitive to data privacy and security due to including medical and personal information. Almost all healthcare applications are required to increase data security and privacy, which use traditional machine learning approaches relying on centralized systems, both computing resources and the entirety of the data. Federated learning, a sort of machine learning technique, has been used to exactly address this issue. The training data is disseminated across numerous devices in federated learning, and the learning process is collaborative. There are numerous privacy attacks on Deep Learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in healthcare applications that use sensitive medical data. This paper provides a comprehensive review of federated learning on future healthcare applications. It also discusses the types of federated learning along with its implementation in healthcare applications.