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The project coordinator and other research assistants of this project will be responsible to input the data into SPSS, with the dataset encrypted in an assigned personal computer.
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The study dataset was encrypted, made pseudonymous (by a third party) and merged with NorPD.
Homomorphic encryption can make the encrypted dataset operable, but it requires considerable storage space and bandwidth, which are impractical.
Known background Model: In this model, except for the encrypted dataset and metadata set the owner upload, the server is assumed to have additional knowledge on the dataset, e.g. the subject and its related statistical information.
Following we rewrite some of the main predicates required for BellKor's solution using the relational algebra of Section 5.2, thus enabling their execution over an encrypted dataset.
Let us consider a model composed by an encrypted dataset stored in a remote server and a user that possesses the secret cryptographic keys.
The UHC dataset also includes encrypted hospital identifiers.
Individuals' identifiers in this dataset have been encrypted to protect privacy and confidentiality, and this study has been approved by the Johns Hopkins School of Public Health Institutional Review Board.
An intuitive thought to improve this situation is to only upload encrypted datasets.
The data providers can go offline after uploading their encrypted datasets, which is one of the critical requirements for a practical system.
The identification numbers of persons and healthcare facilities in the datasets have been encrypted to protect privacy.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com