ArticleBioinformatics (Oxford, England)2017
PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- NitroGene: privacy-preserving collaborative genomic analysis using AWS nitro enclaves.Bioinformatics advances · 2026Article
- PRISM: privacy-preserving rare disease analysis using fully homomorphic encryption.Bioinformatics (Oxford, England) · 2025Article
- Learning-augmented sketching offers improved performance for privacy preserving and secure GWAS.iScience · 2025Article
- Learning-Augmented Sketching Offers Improved Performance for Privacy Preserving and Secure GWAS.bioRxiv : the preprint server for biology · 2024Article
- SVAT: Secure outsourcing of variant annotation and genotype aggregation.BMC bioinformatics · 2022Article
- Sociotechnical safeguards for genomic data privacy.Nature reviews. Genetics · 2022Review
- Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.Methods of information in medicine · 2022Article
- Privacy-preserving genotype imputation in a trusted execution environment.Cell systems · 2021Article
- HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework.Proceedings. IEEE International Conference on Cloud Computing · 2021Article
- Identifying disease-causing mutations with privacy protection.Bioinformatics (Oxford, England) · 2021Article
- A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data.Computational and structural biotechnology journal · 2021Article
- A secure system for genomics clinical decision support.Journal of biomedical informatics · 2020Article
- Cardioinformatics: the nexus of bioinformatics and precision cardiology.Briefings in bioinformatics · 2020Article
- Treating medical data as a durable asset.Nature genetics · 2020Review
- Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption.BMC medical genomics · 2020Article
- Privacy-preserving approximate GWAS computation based on homomorphic encryption.BMC medical genomics · 2020Article
- Privacy-preserving construction of generalized linear mixed model for biomedical computation.Bioinformatics (Oxford, England) · 2020Article
- Privacy challenges and research opportunities for genomic data sharing.Nature genetics · 2020Review
- Evaluation of Privacy Risks of Patients' Data in China: Case Study.JMIR medical informatics · 2020Article
- Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine.BMC medical informatics and decision making · 2019Article
Corrections and comments
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Authors and funding
19 authors.
Funding
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.
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Registered trials
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