Status : Verified
Personal Name | Abad, Earl Andrea G. |
---|---|
Resource Title | On the release of differentially private graph statistics and synthetic graphs |
Date Issued | December 2019 |
Abstract | The analysis of networks is a significant endeavor that enables researchers to understand certain patterns and behavior, which benefits individuals and society as a whole. However, this is accompanied by privacy risks among participating individuals. Mechanisms to preserve privacy in databases have been proposed throughout the years. One of them is differential privacy, a perturbation technique wherein noise is added to the aggregated query answers to protect individuals and hide their participation in a database. In the context of networks, differential privacy is still young. It roughly falls into two directions: the release of differentially private graph statistics (i.e., the use of direct approach) and the release of synthetic graphs with differentially private parameters (i.e., the use of model-based approach). In this study, we see how each technique provides utility and privacy, and which network generation models are appropriate for different datasets and queries. |
Degree Course | MS Computer Science |
Language | English |
Keyword | Differential Privacy, privacy, complex networks, small world networks, graph statistics |
Material Type | Thesis/Dissertation |
Preliminary Pages