Secure and efficient computation on biomedical data in a distributed environment

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Sadat, Md Nazmus
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Recent advances in cost-effective and efficient genome sequencing technologies and scalable health data management systems have resulted in a biomedical data revolution. This massive data availability has created several exciting opportunities to accelerate clinical research, develop better and efficient diagnosis and prevention techniques for patients. In order to perform a comprehensive study by utilizing a large-scale dataset, multiple institutions need to collaborate with each other. However, policy and legal challenges in biomedical data sharing result in geographic inequities in access to data, which often hinders collaborative research. As constructing a centralized data repository is infeasible due to legal and policy constraints, a distributed network model is very effective in this scenario. In this model, to protect the data and summary statistics of participating sites, traditional cryptographic means are not readily applicable due to significant computation and communication overhead. In this thesis, I propose three different frameworks for secure and efficient biomedical data analysis in a distributed environment. These frameworks can securely perform genome-wide association studies, regression analysis, and clinical notes de-identification.
Secure computation
Sadat, Md Nazmus, et al. "SAFETY: Secure gwAs in Federated Environment Through a hYbrid solution." IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018).
Sadat, Md Nazmus, et al. "Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation." JMIR medical informatics 6.1 (2018).