Full Citation
Title: Synthesizing Linked Data and Detecting Per-Query Gaps Under Differential Privacy
Citation Type: Dissertation/Thesis
Publication Year: 2023
ISBN:
ISSN:
DOI:
NSFID:
PMCID:
PMID:
Abstract: The abundance of data, often containing private and sensitive information, coupled with an ever-growing interest in using the data for research and driving business value at scale has raised concerns about privacy protections. Formal policies have made access to such data heavily regulated, often resulting in users waiting months or years before they can even start analyzing the data to determine fit for their tasks. In the recent past, generation of synthetic data has emerged as an alternative. A synthetic dataset is a collection of records that is designed to capture structural and statistical properties in the sensitive dataset while ensuring that private properties of individuals are not revealed. Synthetic data generation, especially under Differential Privacy, is gaining popularity for making copies of sensitive data for testing, benchmarking, data analysis, training models and privacy preservation.
User Submitted?: No
Authors: Patwa, Shweta
Institution: Duke University
Department: Computer Science
Advisor:
Degree:
Publisher Location:
Pages: 1-146
Data Collections: IPUMS CPS
Topics: Population Data Science
Countries: