PubMed · 15554102
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Neil Chesanow. 2004-10-08. Labs.. https://pubmed.ncbi.nlm.nih.gov/15554102/
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MOTIVATION: Rare diseases affect millions of people worldwide, yet their genomic foundations remain poorly understood due to limited patient data and strict privacy regulations, such as the General Data Protection Regulation (GDPR) (https://gdpr.eu/tag/gdpr/) in March 2025. These restrictions can hinder the collaborative analysis of genomic data necessary for uncovering disease-causing variants. RESULTS: We present PRISM, a novel privacy-preserving framework based on fully homomorphic encryption (FHE) that facilitates rare disease variant analysis across multiple institutions without exposing sensitive genomic information. To address the challenges of centralized trust, PRISM is built upon a Threshold FHE scheme. This approach decentralizes key management across participating institutions and ensures no single entity can unilaterally decrypt sensitive data. Our method filters disease-causing variants under recessive, dominant, and de novo inheritance models entirely on encrypted data. We propose two algorithmic variants: a multiplication-intensive (MUL-IN) approach and an addition-intensive (ADD-IN) approach. The ADD-IN algorithms minimize the number of costly multiplication operations, enabling up to a 17× improvement in runtime for recessive/dominant filtering and 22× for de novo filtering, compared to MUL-IN methods. While ADD-IN produces larger ciphertexts, efficient parallelization via SIMD and multithreading allows it to handle millions of variants in reasonable time. To the best of our knowledge, this is the first study that utilizes FHE for privacy-preserving rare disease analysis across multiple inheritance models, demonstrating its practicality and scalability in a single-cloud setting. AVAILABILITY AND IMPLEMENTATION: The source code and the data used in this work can be found in https://github.com/mdppml/PRISM.git.
MOTIVATION: The affordability of genome sequencing and the widespread availability of genomic data have opened up new medical possibilities. Nevertheless, they also raise significant concerns regarding privacy due to the sensitive information they encompass. These privacy implications act as barriers to medical research and data availability. Researchers have proposed privacy-preserving techniques to address this, with cryptography-based methods showing the most promise. However, existing cryptography-based designs lack (i) interoperability, (ii) scalability, (iii) a high degree of privacy (i.e. compromise one to have the other), or (iv) multiparty analyses support (as most existing schemes process genomic information of each party individually). Overcoming these limitations is essential to unlocking the full potential of genomic data while ensuring privacy and data utility. Further research and development are needed to advance privacy-preserving techniques in genomics, focusing on achieving interoperability and scalability, preserving data utility, and enabling secure multiparty computation. RESULTS: This study aims to overcome the limitations of current cryptography-based techniques by employing a multi-key homomorphic encryption scheme. By utilizing this scheme, we have developed a comprehensive protocol capable of conducting diverse genomic analyses. Our protocol facilitates interoperability among individual genome processing and enables multiparty tests, analyses of genomic databases, and operations involving multiple databases. Consequently, our approach represents an innovative advancement in secure genomic data processing, offering enhanced protection and privacy measures. AVAILABILITY AND IMPLEMENTATION: All associated code and documentation are available at https://github.com/farahpoor/smkhe.
On 1 October 2004, the most ambitious high-performance Grid project in the United States-the TeraGrid-became fully operational. Resources at nine sites--the San Diego Supercomputer Center, the California Institute of Technology, the National Center for Supercomputing Applications, the University of Chicago/Argonne National Laboratory, Pittsburgh Supercomputing Center, Texas Advanced Computing Center, Purdue University, Indiana University and Oak Ridge National Laboratory-were joined via an ultra-fast optical network, unified policies and security procedures and a sophisticated distributed computing software environment. Funded by the National Science Foundation, the TeraGrid enables scientists and engineers to combine distributed, multiple data sources with computation at any of the sites or link massively parallel computer simulations to extreme-resolution visualizations at remote sites. A single shared utility lets multiple resources be easily leveraged and provides improved access to advanced computational capabilities. One of the demonstrations of this new model for using distributed resources, Teragyroid, linked the infrastructure of the TeraGrid with computing resources in the United Kingdom via a transatlantic data fibre link. Once connected, the software framework of the RealityGrid project was used to successfully explore lattice-Boltzmann simulations involving lattices of over one billion sites.