Secure remote access to a clinical data repository using a wireless personal digital assistant (PDA).

R G Duncan; M M Shabot

Home/Archive/PubMed

PubMed · 11079875

Secure remote access to a clinical data repository using a wireless personal digital assistant (PDA).

R G Duncan·M M Shabot

View original source ↗Export metadata ↓

Abstract

TCP/IP and World-Wide-Web (WWW) technology have become the universal standards for networking and delivery of information. Personal digital assistants (PDAs), cellular telephones, and alphanumeric pagers are rapidly converging on a single pocket device that will leverage wireless TCP/IP networks and WWW protocols and can be used to deliver clinical information and alerts anytime, anywhere. We describe a wireless interface to clinical information for physicians based on Palm Corp.'s Palm VII pocket computer, a wireless digital network, encrypted data transmission, secure web servers, and a clinical data repository (CDR).

Explore related subjects

Computer Security Confidentiality Hospital Information Systems Internet Microcomputers Modems User-Computer Interface Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

R G Duncan, M M Shabot. 2000. Secure remote access to a clinical data repository using a wireless personal digital assistant (PDA).. https://pubmed.ncbi.nlm.nih.gov/11079875/

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↗

PubMed2007

Data protection in biomaterial banks for Parkinson's disease research: the model of GEPARD (Gene Bank Parkinson's Disease Germany).

Parkinson's disease (PD) is the second most common neurodegenerative disease. Although 10 gene loci have been identified to cause a Parkinsonian syndrome, these loci account only for a minority of PD patients. Large, systematic research programs are required to collect, store, and analyze DNA samples and clinical information to support further discovery of additional genetic components of PD or other movement disorders. Such programs facilitate research into the relationship between genotype and phenotype. The German Competence Network on Parkinson's disease (CNP) initiated the Gene Bank Parkinson's Disease Germany (GEPARD), providing an administrative and scientific infrastructure for the storage of DNA and clinical data that are electronically accessible and protective of patient rights. In this article, we offer guidance on how to establish a framework for a clinical genetic data and DNA bank, and describe GEPARD as a model that may be useful to other local, national, and international research groups developing similar programs.

Computer Security↗