Sana Ben Hamida1,2, Hichem Mrabet3,4, Sana Belguith5,*, Adeeb Alhomoud6, Abderrazak Jemai7
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4897-4919, 2022, DOI:10.32604/cmc.2022.019709
Abstract From fraud detection to speech recognition, including price prediction, Machine Learning (ML) applications are manifold and can significantly improve different areas. Nevertheless, machine learning models are vulnerable and are exposed to different security and privacy attacks. Hence, these issues should be addressed while using ML models to preserve the security and privacy of the data used. There is a need to secure ML models, especially in the training phase to preserve the privacy of the training datasets and to minimise the information leakage. In this paper, we present an overview of ML threats and vulnerabilities, and we highlight current progress… More >
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