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.

Learning Objective 1: Apply the proposed method/implementation based on homomorphic encryption in order to share data in the cloud in a privacy-preserving way.

Learning Objective 2 (Optional): Adopt and implement advanced privacy-enhacing technologies as core components of new tools.

Learning Objective 3 (Optional): Understanding the basis of advanced privacy-enhacing technologies and how they can be used to facilitate data sharing in untrusted environments


Jean Louis Raisaro (Presenter)
École Polytechnique Fédérale de Lausanne

Jeffrey Klann, Harvard Medical School
Kavishwar Wagholikar, Harvard Medical School
Hossein Estiri, Massachusetts General Hospital
Jean-Pierre Hubaux, École Polytechnique Fédérale de Lausanne
Shawn Murphy, Harvard Medical School

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