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Conference Papers Year : 2017

Practical privacy-preserving medical diagnosis using homomorphic encryption

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Abstract

The use of remote services offered by cloud providers have been popular in the last lustrum. Services allow users to store remote files, or to analyze data for several purposes, like health-care or message analysis. However, when personal data are sent to the Cloud, users may lose privacy on the data-content, and on the other side cloud providers may use those data for their own businesses. In this paper, we present our solution to analyze users health-data directly into the Cloud while preserving users privacy. Our solution makes use of homomorphic encryption to protect users data during the analysis. In particular, we developed a mobile application that offloads users data into the Cloud, and a homomorphic encryption algorithm that processes those data without leaking any information to the Cloud provider. Performed empirical tests show that our HE algorithm is able to evaluate users data in reasonable time proving the feasibility of this emerging way of private-data evaluation.
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Dates and versions

cea-01837003 , version 1 (12-07-2018)

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S. Carpov, T.H. Nguyen, G. Constantino, F. Martinelli, R. Sirdey. Practical privacy-preserving medical diagnosis using homomorphic encryption. 2016 IEEE 9th International Conference on Cloud Computing (CLOUD), Jun 2017, San Francisco, United States. pp.593-599, ⟨10.1109/CLOUD.2016.0084⟩. ⟨cea-01837003⟩
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