Trust in the Balance: Data Protection Laws as Tools for Privacy and Security in the Cloud
AbstractA popular bumper sticker states: “There is no cloud. It’s just someone else’s computer.” Despite the loss of control that comes with its use, critical records are increasingly being entrusted to the cloud, generating ever-growing concern about the privacy and security of those records. Ultimately, privacy and security constitute an attempt to balance competing needs: privacy balances the need to use information against the need to protect personal data, while security balances the need to provide access to records against the need to stop unauthorized access. The importance of these issues has led to a multitude of legal and regulatory efforts to find a balance and, ultimately, to ensure trust in both digital records and their storage in the cloud. Adding a particular challenge is the fact that distinct jurisdictions approach privacy differently and an in-depth understanding of what a jurisdiction’s laws may be, or even under what jurisdiction particular data might be, requires a Herculean effort. And yet, in order to protect privacy and enhance security, this effort is required. This article examines two legal tools for ensuring the privacy and security of records in the cloud, data protection laws, and data localization laws, through the framework of “trust” as understood in archival science. This framework of trust provides new directions for algorithmic research, identifying those areas of digital record creation and preservation most in need of novel solutions. View Full-Text
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Hofman, D.; Duranti, L.; How, E. Trust in the Balance: Data Protection Laws as Tools for Privacy and Security in the Cloud. Algorithms 2017, 10, 47.
Hofman D, Duranti L, How E. Trust in the Balance: Data Protection Laws as Tools for Privacy and Security in the Cloud. Algorithms. 2017; 10(2):47.Chicago/Turabian Style
Hofman, Darra; Duranti, Luciana; How, Elissa. 2017. "Trust in the Balance: Data Protection Laws as Tools for Privacy and Security in the Cloud." Algorithms 10, no. 2: 47.
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