Core 1 - Research &Development Contemporary biomedical and behavioral sciences require sophisticated computation. In Core 1, a team of quantitative scientists (information and computer scientists, biostatisticians, mathematicians, and software engineers) will develop the software infrastructure (i.e. the BCl core), services, and tools for use by biomedical and behavioral researchers. An illustration of major components is shown in Figure B-1. Current state of the art research infrastructures containing biomedical data warehouses essentially have three levels of data disclosure: (1) query results counts, (2) de-identified data, and (3) identified data. Deidentification and anonymization are related, but different concepts. While de-identification consists of removal of particular identifiers, anonymization provides a means for data not be traced back to one particular individual. Simplistic measures (Murphy SN &Chueh HC 2002) are cun-ently applied to step (1) above to prevent the tracing of information to a particular individual using the results of several query counts, and previous research indicates that the de-identification of data disclosed at level (2) is not sufficient to preserve individual privacy (Sweeney 1997). Therefore, at both levels (1) and (2) robust anonymization algorithms are necessary. Formal proofs for adherence to quantitative privacy criteria are hard to produce, and consequently only available for a few methods in limited settings (Lasko 2007). As a consequence, most approaches in use today have not been rigorously validated theoretically or with real data. The three levels of disclosure outlined above are insufficient for responsible data sharing beyond the scope of an institutional IRB (in a HIPAA covered entity) such as a federated data warehouse to which multiple institutions or sources can contribute data. For this and other reasons, institutional clinical data repositories for research, some of which receive federal funding for their creation and/or maintenance, have been restricted to researchers who are formally affiliated with the institution. To address this limitation and progress towards a stage in which data can be shared across institutions, we propose research into: (a) a tool that interfaces between clinical data and a user, and that can answer limited queries while ensuring that privacy is preserved, (b) a tool that can simulate real data in a privacy preserving manner to the point that the simulated data can be used as a proxy in population based analyses, and (c) a cryptographic data submission protocol that hides the identity of the submitting entity.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Specialized Center--Cooperative Agreements (U54)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-BST-K)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California San Diego
La Jolla
United States
Zip Code
Groat, Danielle; Grando, Maria Adela; Soni, Hiral et al. (2017) Self-Management Behaviors in Adults on Insulin Pump Therapy. J Diabetes Sci Technol 11:233-239
Eguchi, Akiko; Lazaro, Raul G; Wang, Jiaohong et al. (2017) Extracellular vesicles released by hepatocytes from gastric infusion model of alcoholic liver disease contain a MicroRNA barcode that can be detected in blood. Hepatology 65:475-490
Weng, Wei-Hung; Wagholikar, Kavishwar B; McCray, Alexa T et al. (2017) Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach. BMC Med Inform Decis Mak 17:155
Doan, Son; Ritchart, Amanda; Perry, Nicholas et al. (2017) How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets. JMIR Public Health Surveill 3:e35
Wang, Shuang; Jiang, Xiaoqian; Singh, Siddharth et al. (2017) Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States. Ann N Y Acad Sci 1387:73-83
Chen, Feng; Wang, Shuang; Jiang, Xiaoqian et al. (2017) PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS. Bioinformatics 33:871-878
Spahn, Philipp N; Bath, Tyler; Weiss, Ryan J et al. (2017) PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens. Sci Rep 7:15854
Grando, Maria Adela; Groat, Danielle; Soni, Hiral et al. (2017) Characterization of Exercise and Alcohol Self-Management Behaviors of Type 1 Diabetes Patients on Insulin Pump Therapy. J Diabetes Sci Technol 11:240-246
Dulai, Parambir S; Singh, Siddharth; Jiang, Xiaoqian et al. (2016) The Real-World Effectiveness and Safety of Vedolizumab for Moderate-Severe Crohn's Disease: Results From the US VICTORY Consortium. Am J Gastroenterol 111:1147-55
Komenaka, Ian K; Nodora, Jesse N; Madlensky, Lisa et al. (2016) Participation of low-income women in genetic cancer risk assessment and BRCA 1/2 testing: the experience of a safety-net institution. J Community Genet 7:177-83

Showing the most recent 10 out of 164 publications