PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 11080014

A Web-based, secure, light weight clinical multimedia data capture and display system.

S S Wang·J Starren
View original source ↗Export metadata ↓

Abstract

Computer-based patient records are traditionally composed of textual data. Integration of multimedia data has been historically slow. Multimedia data such as image, audio, and video have been traditionally more difficult to handle. An implementation of a clinical system for multimedia data is discussed. The system implementation uses Java, Secure Socket Layer (SSL), and Oracle 8i. The system is on top of the Internet so it is architectural independent, cross-platform, cross-vendor, and secure. Design and implementations issues are discussed.

Explore related subjects

Computer SecurityData DisplayDatabases as TopicHumansInternetMedical Records Systems, ComputerizedMultimediaProgramming LanguagesJournal ArticleResearch Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

S S Wang, J Starren. 2000. A Web-based, secure, light weight clinical multimedia data capture and display system.. https://pubmed.ncbi.nlm.nih.gov/11080014/

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↗