Feb. 5, 2024, 8:10 p.m. | Hamed Poursiami Ihsen Alouani Maryam Parsa

cs.CR updates on arXiv.org arxiv.org

With the mainstream integration of machine learning into security-sensitive domains such as healthcare and finance, concerns about data privacy have intensified. Conventional artificial neural networks (ANNs) have been found vulnerable to several attacks that can leak sensitive data. Particularly, model inversion (MI) attacks enable the reconstruction of data samples that have been used to train the model. Neuromorphic architectures have emerged as a paradigm shift in neural computing, enabling asynchronous and energy-efficient computation. However, little to no existing work has …

architectures artificial attacks can cs.cr cs.lg cs.ne data data privacy domains enable finance found healthcare integration leak machine machine learning mainstream networks neural networks privacy security sensitive sensitive data vulnerable

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