Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 9 of 9 for “"Differential Privacy (DP)"”.
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Local differential privacy in decentralized optimization
Privacy concerns with sensitive data are receiving increasing attention. In this thesis, we study local differential privacy (LDP) in interactive decentralized optimization. Comparing to central differential privacy (DP), where a centralized curator maintains the dataset, LDP is a stronger notion …
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Hyperparameters and neural architectures in differentially private deep learning
… in machine learning for health has ignored privacy attacks against the models. Differential privacy (DP) is the state-of-the-art concept for protecting individuals' data from privacy attacks. Using optimization algorithms such as the DP stochastic gradient descent (DP-SGD), one can train …
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Analysis of Privacy-aware Data Sharing in Cyber-physical Energy Systems
… the key factors and correlations among the privacy, security, and utility requirements of grid networks to ensure effective inter-and intra-actions within physical layer equipment (e.g., distributed energy resources (DERs), intelligent electronic devices (IEDs), etc.). We have conducted a …
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CLASSIFICATION ALGORITHMS WITH DIFFERENTIAL PRIVACY AND FAIRNESS GUARANTEES
… their trustworthiness, especially regarding privacy and fairness. Models must protect individual privacy and avoid discriminating against demographic subgroups. Differential Privacy (DP) has become the standard for privacy-preserving machine learning. It is generally divided into central DP, …
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Advancing SCRAM: Privacy-Centric Approaches in Cyber Risk Measurement
… we tackle the challenging problem of preserving privacy in small datasets while maximizing utility, a critical issue in the context of the SCRAM framework. We first construct a linear programming problem that demonstrates how the aggregate outputs of SCRAM do not provide adequate privacy, …
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Visualization and differential privacy
Privacy-preserving statistical databases are designed to provide information about a population while preventing end-users from learning about an individual. Meanwhile, scholars have shown that a sophisticated adversary can break such assumption against primitive privacy protections. Differential …
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Understanding and mitigating privacy risk in machine learning systems
… the widespread adoption of machine learning, privacy issues have emerged. This thesis studies the privacy risk in modern machine learning systems in two ways. First, we improve the understanding on machine learning privacy through attacks and measurements. Due to the increasing complexity and …
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Advances in Few-Shot Learning for Image Classification and Tabular Data
… include personalisation, federated learning, and privacy-sensitive applications -- each requiring robust learning from minimal data. These constraints underscore the need for few-shot learning methods, enabling models to adapt from just a handful of examples. In addition, many of these …
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Budget allocation on differentially private decision trees and random forests
Privacy-preserving techniques are necessary to minimize the possibility of identifying and learning sensitive information about individuals from any datasets that have been released or shared. Datasets containing sensitive information on individuals are becoming increasingly public. Although this …