The effective and efficient utilization of the big data accumulated in biomedical sciences, including the genomic, imaging data collected from patients that are often integrated with their electronic health records represents a great opportunity as well as a big challenge for the biomedical data science. Because biomedical data are collected from individual patients, and thus carry identifiable information of the data donors, the projection of their privacy becomes an important concern in large-scale projects including the recently launched Precision Medicine Initiative. In the past few years, significant progresses have been made on cryptographic techniques, including the homomorphic encryption (HME) that enables a direct analysis of encrypted data without decrypting it, and the Secure Multiparty Computing (SMC) that allows two or more organizations to jointly compute a task without exposing to each other?s inputs. Here, based on these techniques, we propose to develop a suite of encryption protocols and open-source software tools that can be used by biomedical researchers in a plug-and-play manner for the statistical analysis of encrypted biomedical data. We note that our methods assume biomedical data will be protected by encryption once they are generated, and the subsequent analysis and sharing will always be performed on the encrypted form, which thus can achieve a high security standard for privacy protection.

Public Health Relevance

We propose to develop encryption methods for biomedical data mining, and to implement these methods in open-source software that can be used by biomedical researchers in a plug-and- play manner for the statistical analysis of encrypted biomedical data. Following our approach, biomedical data will be protected by encryption once they are generated, and the subsequent analysis and sharing will always be performed on the encrypted form, which thus can achieve a high security standard for privacy protection in biomedical data science.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01EB023685-03
Application #
9514169
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Peng, Grace
Project Start
2016-09-30
Project End
2019-06-30
Budget Start
2018-07-01
Budget End
2019-06-30
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Indiana University Bloomington
Department
Miscellaneous
Type
Schools of Arts and Sciences
DUNS #
006046700
City
Bloomington
State
IN
Country
United States
Zip Code
47401
Kim, Miran; Song, Yongsoo; Wang, Shuang et al. (2018) Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation. JMIR Med Inform 6:e19
Kim, Andrey; Song, Yongsoo; Kim, Miran et al. (2018) Logistic regression model training based on the approximate homomorphic encryption. BMC Med Genomics 11:83
Sadat, Md Nazmus; Jiang, Xiaoqian; Aziz, Md Momin Al et al. (2018) Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation. JMIR Med Inform 6:e14
Wang, Meng; Ji, Zhanglong; Kim, Hyeon-Eui et al. (2018) Selecting Optimal Subset to release under Differentially Private M-estimators from Hybrid Datasets. IEEE Trans Knowl Data Eng 30:573-584
Bonomi, Luca; Jiang, Xiaoqian (2018) Linking temporal medical records using non-protected health information data. Stat Methods Med Res 27:3304-3324
Bu, Diyue; Wang, Xiaofeng; Tang, Haixu (2018) Real-time Protection of Genomic Data Sharing in Beacon Services. AMIA Jt Summits Transl Sci Proc 2017:45-54
Miotto, Riccardo; Wang, Fei; Wang, Shuang et al. (2018) Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 19:1236-1246
Vaidya, Jaideep; Shafiq, Basit; Asani, Muazzam et al. (2017) A Scalable Privacy-preserving Data Generation Methodology for Exploratory Analysis. AMIA Annu Symp Proc 2017:1695-1704
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
Wang, Shuang; Jiang, Xiaoqian; Tang, Haixu et al. (2017) A community effort to protect genomic data sharing, collaboration and outsourcing. NPJ Genom Med 2:33

Showing the most recent 10 out of 14 publications