Fuzzy rule-based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent human-understandable knowledge. They have been applied to various applications and areas throughout the soft computing literature. However, FRBSs suffers from many drawbacks such as uncertainty representation, high number of rules, interpretability loss, high computational time for learning. To overcome these issues with FRBSs, there exists many extensions of FRBSs. This paper presents an overview and literature review of recent trends on various types and prominent areas of fuzzy systems (FRBSs) namely genetic fuzzy system, hierarchical fuzzy system, neuro fuzzy system, evolving fuzzy system, FRBSs for big data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which use cluster centroids as fuzzy rules. The review is for years 2010–2021. This paper also highlights important contributions, publication statistics and current trends in the field. The paper also addresses several open research areas which need further attention from the FRBSs research community.
Deep neural networks (DNNs) are one of the most widely used machine learning algorithm. In the literature, most of the privacy related work to DNNs focus on adding perturbations to avoid attacks in the output which can lead to significant utility loss. Large number of weights and biases in DNNs can result in a unique model for each set of training data. In this case, an adversary can perform model comparison attacks which lead to the disclosure of the training data. In our work, we first introduce the model comparison attack for DNNs which accounts for the permutation of nodes in a layer. To overcome this, we introduce a relaxed notion of integral privacy called $\epsilon$-integral privacy. We further provide a methodology for recommending $\epsilon$-Integrally private models. We use a data-centric approach to generate subsamples which have the same class-distribution as the original data. We have experimented with 6 datasets of varied sizes (10k to 7 million instances) and our experimental results show that our recommended private models achieve benchmark comparable utility. We also achieve benchmark comparable test accuracy for 4 different DNN architectures. The results from our methodology show superiority under comparison with three different levels of differential privacy.
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