In:
Bioinformatics, Oxford University Press (OUP), Vol. 33, No. 6 ( 2017-03-15), p. 871-878
Kurzfassung:
We introduce PRINCESS, a privacy-preserving international collaboration framework for analyzing rare disease genetic data that are distributed across different continents. PRINCESS leverages Software Guard Extensions (SGX) and hardware for trustworthy computation. Unlike a traditional international collaboration model, where individual-level patient DNA are physically centralized at a single site, PRINCESS performs a secure and distributed computation over encrypted data, fulfilling institutional policies and regulations for protected health information. Results To demonstrate PRINCESS’ performance and feasibility, we conducted a family-based allelic association study for Kawasaki Disease, with data hosted in three different continents. The experimental results show that PRINCESS provides secure and accurate analyses much faster than alternative solutions, such as homomorphic encryption and garbled circuits (over 40 000× faster). Availability and Implementation https://github.com/achenfengb/PRINCESS_opensource Supplementary information Supplementary data are available at Bioinformatics online.
Materialart:
Online-Ressource
ISSN:
1367-4803
,
1367-4811
DOI:
10.1093/bioinformatics/btw758
Sprache:
Englisch
Verlag:
Oxford University Press (OUP)
Publikationsdatum:
2017
ZDB Id:
1468345-3
SSG:
12