There is increasing concern in disclosing sensitive information when clinical data are disseminated, given the potential for breach of individual privacy. Data sharing has become critical in the acceleration of biomedical research and healthcare quality improvement. We will develop new methods for privacy protection that can adapt to the amount of the data being disseminated and the sensitivity of certain variables.
Our first aim i s to measure fine-grained privacy risk of individual records in patient sub- populations This index can be used to monitor and customize privacy protection of individual clinical records and help prioritize efforts in privacy protection.
The second aim i s to develop a new and practical method to support privacy-preserving data dissemination in both centralized and distributed environments, with or without knowledge of which analytic techniques will be applied to the disclosed data.
The third aim i s to speed up privacy preserving algorithms through advanced parallelization techniques. If successful, these new methods will allow privacy protection for large data set dissemination/analysis in real time.
These aims are faithful to the mission of the National Library of Medicine, and they are tightly related to the mentors'efforts i leading the development of trustworthy data sharing and individualized predictive models as part of the National Center for Biomedical Computing (NCBC), iDASH (integrating Data for analysis, Anonymization, and SHaring). The applicant wishes to use this funding opportunity to complement his computer science skills with biomedical knowledge, and specialized training in parallel computing to investigate new algorithms for privacy protection in disseminated data. Success in this project will lead to his long-term goal of becoming an independently funded investigator and joining the core faculty of the Division of Biomedical Informatics at UCSD.
There are important tradeoffs between disseminating clinical and genetic data for societal benefits and protecting personal privacy. We will develop practical solutions to address fine-grained privacy and usability trade-offs, provide multi-resolution protection to satisfy needs of different stakeholders, and accelerate privacy-preserving algorithms to support efficient data anonymization, analysis, and sharing.
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|Bonomi, Luca; Jiang, Xiaoqian (2018) Linking temporal medical records using non-protected health information data. Stat Methods Med Res 27:3304-3324|
|Bonomi, Luca; Jiang, Xiaoqian (2017) A Mortality Study for ICU Patients using Bursty Medical Events. Proc Int Conf Data Eng 2017:1533-1540|
|Wenrui Dai; Yangmei Shen; Hongkai Xiong et al. (2017) Progressive Dictionary Learning With Hierarchical Predictive Structure for Low Bit-Rate Scalable Video Coding. IEEE Trans Image Process 26:2972-2987|
|Ghasemi, Reza; Al Aziz, Md Momin; Mohammed, Noman et al. (2017) Private and Efficient Query Processing on Outsourced Genomic Databases. IEEE J Biomed Health Inform 21:1466-1472|
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|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|
|Raisaro, Jean Louis; Tramèr, Florian; Ji, Zhanglong et al. (2017) Addressing Beacon re-identification attacks: quantification and mitigation of privacy risks. J Am Med Inform Assoc 24:799-805|
|Kim, Yejin; El-Kareh, Robert; Sun, Jimeng et al. (2017) Discriminative and Distinct Phenotyping by Constrained Tensor Factorization. Sci Rep 7:1114|
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