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Feasibility of Homomorphic Encryption for Sharing I2B2 Aggregate-Level Data in the Cloud

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2017

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American Medical Informatics Association
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Raisaro, Jean Louis, Jeffrey G Klann, Kavishwar B Wagholikar, Hossein Estiri, Jean-Pierre Hubaux, and Shawn N Murphy. 2017. “Feasibility of Homomorphic Encryption for Sharing I2B2 Aggregate-Level Data in the Cloud.” AMIA Summits on Translational Science Proceedings 2017 (1): 176-185.

Abstract

The biomedical community is lagging in the adoption of cloud computing for the management of medical data. The primary obstacles are concerns about privacy and security. In this paper, we explore the feasibility of using advanced privacy-enhancing technologies in order to enable the sharing of sensitive clinical data in a public cloud. Our goal is to facilitate sharing of clinical data in the cloud by minimizing the risk of unintended leakage of sensitive clinical information. In particular, we focus on homomorphic encryption, a specific type of encryption that offers the ability to run computation on the data while the data remains encrypted. This paper demonstrates that homomorphic encryption can be used efficiently to compute aggregating queries on the ciphertexts, along with providing end-to-end confidentiality of aggregate-level data from the i2b2 data model.

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