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Estiri, Hossein

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Estiri

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Hossein

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Estiri, Hossein
Author's Publications: search.filters.isAuthorOfPublication.921bc8ff-7d38-4063-9506-36b991e847f3

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    Feasibility of Homomorphic Encryption for Sharing I2B2 Aggregate-Level Data in the Cloud
    (American Medical Informatics Association, 2017) Raisaro, Jean Louis; Klann, Jeffrey; Wagholikar, Kavishwar; Estiri, Hossein; Hubaux, Jean-Pierre; Murphy, Shawn
    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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    DQe-v: A Database-Agnostic Framework for Exploring Variability in Electronic Health Record Data Across Time and Site Location
    (Ubiquity Press, 2018) Estiri, Hossein; Stephens, Kari
    Data variability is a commonly observed phenomenon in Electronic Health Records (EHR) data networks. A common question asked in scientific investigations of EHR data is whether the cross-site and -time variability reflects an underlying data quality error at one or more contributing sites versus actual differences driven by various idiosyncrasies in the healthcare settings. Although research analysts and data scientists have commonly used various statistical methods to detect and account for variability in analytic datasets, self service tools to facilitate exploring cross-organizational variability in EHR data warehouses are lacking and could benefit from meaningful data visualizations. DQe-v, an interactive, database-agnostic tool for visually exploring variability in EHR data provides such a solution. DQe-v is built on an open source platform, R statistical software, with annotated scripts and a readme document that makes it fully reproducible. To illustrate and describe functionality of DQe-v, we describe the DQe-v’s readme document which includes a complete guide to installation, running the program, and interpretation of the outputs. We also provide annotated R scripts and an example dataset as supplemental materials. DQe-v offers a self service tool to visually explore data variability within EHR datasets irrespective of the data model. GitHub and CIELO offer hosting and distribution of the tool and can facilitate collaboration across any interested community of users as we target improving usability, efficiency, and interoperability.