Browsing by Author "Ashish Kumar Lal"

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Impact of Dimensionality Reduction on Membership Privacy of CNN Models
(Springer Science and Business Media Deutschland GmbH, 2023) Ashish Kumar Lal; S. Karthikeyan
Dimensionality reduction is an essential tool for exploratory data analysis, classification and clustering, manifold learning, and preprocessing in deep learning. The curse of dimensionality is a well-recognized serious problem in machine learning. Many researchers have attempted to improve machine learning accuracy and performance with dimensionality reduction. Nowadays, machine learning models’ privacy vulnerability is an essential quality measure. The effect of dimensionality reduction on the privacy leakage of deep learning models is an understudied research area. This work explored the effects of dimensionality reduction on the privacy leakage of the deep learning model. The experiments for image classification using CNN model were performed on the widely used Cifar10 dataset. The results show that although the PCA technique improves the classification task, it does not enhance the model’s privacy when tested against the membership inference attack. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.