Year: 2023
Type: preprint
Abstract: This paper describes privacy-preserving approaches for the statistical analysis. It describes motivations for privacy-preserving approaches for the statistical analysis of sensitive data, presents examples of use cases where such methods may apply and describes relevant technical capabilities to assure privacy preservation while still allowing analysis of sensitive data. Our focus is on methods that enable protecting privacy of data while it is being processed, not only while it is at rest on a system or in transit between systems. The information in this document is intended for use by statisticians and data scientists, data curators and architects, IT specialists, and security and information assurance specialists, so we explicitly avoid cryptographic technical details of the technologies we describe.
Source: arXiv (Cornell University)
Authors: Archer, David W., Pigem, Borja de Balle, Bogdanov, Dan, Craddock, Mark, Gascon, Adria, Jansen, Ronald, Jug, Matjaž, Laine, Kim, McLellan, Robert, Ohrimenko, Olga, Raykova, Mariana, Trask, Andrew, Wardley, Simon
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4317438186