The article introduces a new approach, RQP-SGD, for privacy-preserving machine learning. The rise of IoT devices necessitates efficient and secure data processing. Traditional machine learning models can be impractical for…
Continue readingThe paper presents a new approach, PrivPGD, for differentially private data synthesis of protected tabular datasets, an important task in sensitive domains like healthcare and government. Existing methods typically use…
Continue readingThis research paper addresses the issue of maintaining privacy budget in differentially-private (DP) systems. Authors propose ElephantDP, a system designed to provide the same guarantees as a trusted curator in…
Continue readingThis study compares the TopDown algorithm and private synthetic data generation methods in terms of differential privacy. Differential privacy is used to protect hierarchical, tabular population data, such as census…
Continue readingThe paper presents an algorithm that relaxes the Differential Privacy (DP) guarantee of a randomized response gradually. The output from each relaxation maintains the same probability distribution as a standard…
Continue readingThe paper discusses the security and privacy aspects of Mobile Edge Computing (MEC) from an Artificial Intelligence (AI) perspective. MEC is a new computing paradigm that moves cloud computing to…
Continue readingThis research paper presents PrivRecourse, a solution to the privacy issues that arise when generating realistic recourse paths based on instance-based counterfactual explanations. The system uses differentially private (DP) clustering…
Continue readingThe paper addresses the challenge of performance degradation in Differentially Private Stochastic Gradient Descent with gradient clipping (DPSGD-GC), a tool for training deep learning models. The authors propose an error-feedback…
Continue readingThe paper discusses the vulnerability of Federated Learning (FL) protocols to active reconstruction attacks by dishonest servers. These attacks involve the malicious modification of global model parameters, allowing the server…
Continue readingThe research proposes a federated learning framework for intrusion detection in Internet of Vehicles (IOVs) using the CIC-IDS 2017 dataset. The framework uses SMOTE for handling class imbalance, outlier detection…
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