Reliable medical recommendation systems with patient privacy
Abstract
One of the concerns patients have when confronted with a medical condition is which physician to trust. Any recommendation system that seeks to answer this question must ensure that any sensitive medical information collected by the system is properly secured. In this article, we codify these privacy concerns in a privacy-friendly framework and present two architectures that realize it: the Secure Processing Architecture (SPA) and the Anonymous Contributions Architecture (ACA). In SPA, patients submit their ratings in a protected form without revealing any information about their data and the computation of recommendations proceeds over the protected data using secure multiparty computation techniques. In ACA, patients submit their ratings in the clear, but no link between a submission and patient data can be made. We discuss various aspects of both architectures, including techniques for ensuring reliability of computed recommendations and system performance, and provide their comparison.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 01, 2013
- Source ID
- 10.1145/2508037.2508048
Entities
People
- Aaron Steele
- Marina Blanton
- Nitesh Chawla
- T. Ryan Hoens
Organizations
- Air Force Office of Scientific Research
- Division of Behavioral and Cognitive Sciences
- University of Notre Dame