In last few years, achievements in information and communications technologies (ICTs), such as the electronic health record (EHR), have improved the healthcare system. However to be effective, paramedics and doctors have to consult the most recent version of EHRs anytime and anywhere. A possible solution is to store EHRs on remote storage services. However, the EU General Data Protection Regulation (GDPR) does not allow to store plain files containing personal data in services accessible remotely. To solve this challenge, a possible solution is to use Edge computing devices running Secret Sharing algorithms to split and merge EHRs on demand; however, these techniques have not been evaluated before for these purposes. To address this issue, in this work we analyse the redundant residue number system (RRNS). In particular, considering different EHR sizes (from 10kB to 1 MB), we evaluated computation time (split and recomposition), transfer time (upload and download) from/to public Cloud storage providers (Google Drive, Mega and Dropbox) and storage requirement. Results showed that, in configuration with seven levels of redundancy, the RRNS uses only 50% of the storage required for the simple file replication. We also discovered that Google Drive, due to synchronization overhead, is slower than other Cloud service providers for the upload of chunks but faster for the download.
Smart hospitals enabled by edge computing
Galletta A.;Buzachis A.;Fazio M.;Celesti A.;Villari M.
2020-01-01
Abstract
In last few years, achievements in information and communications technologies (ICTs), such as the electronic health record (EHR), have improved the healthcare system. However to be effective, paramedics and doctors have to consult the most recent version of EHRs anytime and anywhere. A possible solution is to store EHRs on remote storage services. However, the EU General Data Protection Regulation (GDPR) does not allow to store plain files containing personal data in services accessible remotely. To solve this challenge, a possible solution is to use Edge computing devices running Secret Sharing algorithms to split and merge EHRs on demand; however, these techniques have not been evaluated before for these purposes. To address this issue, in this work we analyse the redundant residue number system (RRNS). In particular, considering different EHR sizes (from 10kB to 1 MB), we evaluated computation time (split and recomposition), transfer time (upload and download) from/to public Cloud storage providers (Google Drive, Mega and Dropbox) and storage requirement. Results showed that, in configuration with seven levels of redundancy, the RRNS uses only 50% of the storage required for the simple file replication. We also discovered that Google Drive, due to synchronization overhead, is slower than other Cloud service providers for the upload of chunks but faster for the download.Pubblicazioni consigliate
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