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 make use of Fully Homomorphic Encryption (FHE) to protect users' data during the analysis. In particular, we developed a mobile application that offload users' data into the Cloud, and a Fully Homomorphic Encryption algorithm that processes those data without leaking any information to the Cloud provider. Performed empirical tests show that our FHE algorithm is able to evaluate users' data in reasonable time proving the feasibility of this emerging way of private-data evaluation.
Practical Privacy Preserving Medical Diagnosis using Homomorphic Encryption
Martinelli F;
2016
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 make use of Fully Homomorphic Encryption (FHE) to protect users' data during the analysis. In particular, we developed a mobile application that offload users' data into the Cloud, and a Fully Homomorphic Encryption algorithm that processes those data without leaking any information to the Cloud provider. Performed empirical tests show that our FHE algorithm is able to evaluate users' data in reasonable time proving the feasibility of this emerging way of private-data evaluation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.