PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 8591193

A microprocessor card software server to support the Quebec health microprocessor card project.

P Durant·J Bérubé·G Lavoie·A Gamache·P Ardouin·M J Papillon·J P Fortin
View original source ↗Export metadata ↓

Abstract

The Quebec Health Smart Card Project is advocating the use of a memory card software server[1] (SCAM) to implement a portable medical record (PMR) on a smart card. The PMR is viewed as an object that can be manipulated by SCAM's services. In fact, we can talk about a pseudo-object-oriented approach. This software architecture provides a flexible and evolutive way to manage and optimize the PMR. SCAM is a generic software server; it can manage smart cards as well as optical (laser) cards or other types of memory cards. But, in the specific case of the Quebec Health Card Project, SCAM is used to provide services between physicians' or pharmacists' software and IBM smart card technology. We propose to expose the concepts and techniques used to provide a generic environment to deal with smart cards (and more generally with memory cards), to obtain a dynamic an evolutive PMR, to raise the system global security level and the data integrity, to optimize significantly the management of the PMR, and to provide statistic information about the use of the PMR.

Explore related subjects

Computer SecurityMedical Records Systems, ComputerizedMicrocomputersPatient Identification SystemsQuebecSoftwareJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

P Durant, J Bérubé, G Lavoie, A Gamache, P Ardouin, M J Papillon, J P Fortin. 1995. A microprocessor card software server to support the Quebec health microprocessor card project.. https://pubmed.ncbi.nlm.nih.gov/8591193/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

PRISM: privacy-preserving rare disease analysis using fully homomorphic encryption.

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.

Computer Security↗
PubMed2025

Privacy-preserving framework for genomic computations via multi-key homomorphic encryption.

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.

Computer Security↗