<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vinterbo, Staal A</style></author><author><style face="normal" font="default" size="100%">Sarwate, Anand D</style></author><author><style face="normal" font="default" size="100%">Boxwala, Aziz A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Protecting count queries in study design.</style></title><secondary-title><style face="normal" font="default" size="100%">J Am Med Inform Assoc</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Apr 17</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">ObjectiveToday's clinical research institutions provide tools for researchers to query their data warehouses for counts of patients. To protect patient privacy, counts are perturbed before reporting; this compromises their utility for increased privacy. The goal of this study is to extend current query answer systems to guarantee a quantifiable level of privacy and allow users to tailor perturbations to maximize the usefulness according to their needs.MethodsA perturbation mechanism was designed in which users are given options with respect to scale and direction of the perturbation. The mechanism translates the true count, user preferences, and a privacy level within administrator-specified bounds into a probability distribution from which the perturbed count is drawn.ResultsUsers can significantly impact the scale and direction of the count perturbation and can receive more accurate final cohort estimates. Strong and semantically meaningful differential privacy is guaranteed, providing for a unified privacy accounting system that can support role-based trust levels. This study provides an open source web-enabled tool to investigate visually and numerically the interaction between system parameters, including required privacy level and user preference settings.ConclusionsQuantifying privacy allows system administrators to provide users with a privacy budget and to monitor its expenditure, enabling users to control the inevitable loss of utility. While current measures of privacy are conservative, this system can take advantage of future advances in privacy measurement. The system provides new ways of trading off privacy and utility that are not provided in current study design systems.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kim, Jihoon</style></author><author><style face="normal" font="default" size="100%">Grillo, Janice M</style></author><author><style face="normal" font="default" size="100%">Boxwala, Aziz A</style></author><author><style face="normal" font="default" size="100%">Jiang, Xiaoqian</style></author><author><style face="normal" font="default" size="100%">Mandelbaum, Rose B</style></author><author><style face="normal" font="default" size="100%">Patel, Bhakti A</style></author><author><style face="normal" font="default" size="100%">Mikels, Debra</style></author><author><style face="normal" font="default" size="100%">Vinterbo, Staal A</style></author><author><style face="normal" font="default" size="100%">Ohno-Machado, Lucila</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Anomaly and Signature Filtering Improve Classifier Performance For Detection Of Suspicious Access To EHRs.</style></title><secondary-title><style face="normal" font="default" size="100%">AMIA Annu Symp Proc</style></secondary-title><alt-title><style face="normal" font="default" size="100%">AMIA Annu Symp Proc</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">2011</style></volume><pages><style face="normal" font="default" size="100%">723-31</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Our objective is to facilitate semi-automated detection of suspicious access to EHRs. Previously we have shown that a machine learning method can play a role in identifying potentially inappropriate access to EHRs. However, the problem of sampling informative instances to build a classifier still remained. We developed an integrated filtering method leveraging both anomaly detection based on symbolic clustering and signature detection, a rule-based technique. We applied the integrated filtering to 25.5 million access records in an intervention arm, and compared this with 8.6 million access records in a control arm where no filtering was applied. On the training set with cross-validation, the AUC was 0.960 in the control arm and 0.998 in the intervention arm. The difference in false negative rates on the independent test set was significant, P=1.6×10(-6). Our study suggests that utilization of integrated filtering strategies to facilitate the construction of classifiers can be helpful.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ohno-Machado, Lucila</style></author><author><style face="normal" font="default" size="100%">Bafna, Vineet</style></author><author><style face="normal" font="default" size="100%">Boxwala, Aziz A</style></author><author><style face="normal" font="default" size="100%">Chapman, Brian E</style></author><author><style face="normal" font="default" size="100%">Chapman, Wendy W</style></author><author><style face="normal" font="default" size="100%">Chaudhuri, Kamalika</style></author><author><style face="normal" font="default" size="100%">Day, Michele E</style></author><author><style face="normal" font="default" size="100%">Farcas, Claudiu</style></author><author><style face="normal" font="default" size="100%">Heintzman, Nathaniel D</style></author><author><style face="normal" font="default" size="100%">Jiang, Xiaoqian</style></author><author><style face="normal" font="default" size="100%">Kim, Hyeoneui</style></author><author><style face="normal" font="default" size="100%">Kim, Jihoon</style></author><author><style face="normal" font="default" size="100%">Matheny, Michael E</style></author><author><style face="normal" font="default" size="100%">Resnic, Frederic S</style></author><author><style face="normal" font="default" size="100%">Vinterbo, Staal A</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">and the iDASH team</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">iDASH: integrating data for analysis, anonymization, and sharing.</style></title><secondary-title><style face="normal" font="default" size="100%">J Am Med Inform Assoc</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Am Med Inform Assoc</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Nov 10</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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 data-sharing 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.</style></abstract></record></records></xml>