iDASH: Integrating data for analysis, anonymization, and sharing

Lucila Ohno-Machado, Vineet Bafna, Aziz A. Boxwala, Brian E. Chapman, Wendy W. Chapman, Kamalika Chaudhuri, Michele E. Day, Claudiu Farcas, Nathaniel D. Heintzman, Xiaoqian Jiang, Hyeoneui Kim, Jihoon Kim, Michael E. Matheny, Frederic S. Resnic, Staal A. Vinterbo, Winston Armstrong, Natasha Balac, Jane Burns, James Chen, Rex ChisholmRichard Cope, Sanjoy Dasgupta, Cynthia Dwork, Robert El-Kareh, Fern Fitzhenry, Anthony Gamst, Amilcare Gentili, Peter Good, Amarnath Gupta, Mayuko Inoue, Ronald Joyce, Ingolf Krueger, Grace Kuo, Jennie Larkin, Karen Messer, Lalit Nookala, Greg Norman, Keith Norris, Kiltesh Patel, Paulina Paul, Pavel Pevzner, Kevin Patrick, Sergei Pond, Jialan Que, Susan Rathbun, Susan Robbins, Anand Sarwate, Chisato Shimizu, Heidi Sofia, Peter Tarczy-Hornoch, Dallas Thornton, Florin Vaida, Faramarz Valafar, George Varghese, Nicole Wolter, Cindy Wong, Mona Wong, Alex Zambon

Research output: Contribution to journalArticlepeer-review

90 Scopus citations


iDASH (integrating data for analysis, anonymization, and sharing) is the newest National Center for Biomedical Computing funded by the NIH. It focuses on algorithms and tools for sharing data in a privacy-preserving manner. Foundational privacy technology research performed within iDASH is coupled with innovative engineering for collaborative tool development and datasharing capabilities in a private Health Insurance Portability and Accountability Act (HIPAA)-certified cloud. Driving Biological Projects, which span different biological levels (from molecules to individuals to populations) and focus on various health conditions, help guide research and development within this Center. Furthermore, training and dissemination efforts connect the Center with its stakeholders and educate data owners and data consumers on how to share and use clinical and biological data. Through these various mechanisms, iDASH implements its goal of providing biomedical and behavioral researchers with access to data, software, and a high-performance computing environment, thus enabling them to generate and test new hypotheses.

Original languageEnglish (US)
Pages (from-to)196-201
Number of pages6
JournalJournal of the American Medical Informatics Association
Issue number2
StatePublished - Mar 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Health Informatics


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