Feng Chen1, Shuang Wang1, Xiaoqian Jiang1, Sijie Ding1, Yao Lu2, Jihoon Kim1, S Cenk Sahinalp3, Chisato Shimizu4, Jane C Burns4, Victoria J Wright5, Eileen Png6, Martin L Hibberd6, David D Lloyd7, Hai Yang1, Amalio Telenti8, Cinnamon S Bloss9, Dov Fox10, Kristin Lauter11, Lucila Ohno-Machado1. 1. Health System Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA. 2. Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA. 3. Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA. 4. Department of Pediatrics, University of California San Diego, La Jolla, CA, USA. 5. Section of Pediatrics, Imperial College London, London, UK. 6. Genome Institute of Singapore, ASTAR, Singapore, Singapore. 7. Deparment of Pediatrics, School of Medicine, Emory University, Atlanta, GA, USA. 8. J. Craig Venter Institute, La Jolla, CA, USA. 9. Department of Psychiatry, University of California San Diego, La Jolla, CA, USA. 10. School of Law, University of San Diego, San Diego, CA, USA. 11. Cryptography Group, Microsoft Research, San Diego, CA, USA.
Abstract
Motivation: 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. Contact: shw070@ucsd.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: 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. Contact: shw070@ucsd.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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