PubMed · 11066426
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
E Gaines, M Pastin. 2000-04-10. Unnecessarily complex.. https://pubmed.ncbi.nlm.nih.gov/11066426/
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
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.
BACKGROUND: Disease registers aim to collect information about all instances of a disease or condition in a defined population of individuals. Traditionally methods of operating disease registers have required that notifications of cases be identified by unique identifiers such as social security number or national identification number, or by ensembles of non-unique identifying data items, such as name, sex and date of birth. However, growing concern over the privacy and confidentiality aspects of disease registers may hinder their future operation. Technical solutions to these legitimate concerns are needed. DISCUSSION: An alternative method of operation is proposed which involves splitting the personal identifiers from the medical details at the source of notification, and separately encrypting each part using asymmetrical (public key) cryptographic methods. The identifying information is sent to a single Population Register, and the medical details to the relevant disease register. The Population Register uses probabilistic record linkage to assign a unique personal identification (UPI) number to each person notified to it, although not necessarily everyone in the entire population. This UPI is shared only with a single trusted third party whose sole function is to translate between this UPI and separate series of personal identification numbers which are specific to each disease register. SUMMARY: The system proposed would significantly improve the protection of privacy and confidentiality, while still allowing the efficient linkage of records between disease registers, under the control and supervision of the trusted third party and independent ethics committees. The proposed architecture could accommodate genetic databases and tissue banks as well as a wide range of other health and social data collections. It is important that proposals such as this are subject to widespread scrutiny by information security experts, researchers and interested members of the general public, alike.