Yohannis, Alfa, De La Vega, Alfonso, Kahrobaei, Delaram orcid.org/0000-0001-5467-7832 et al. (1 more author) (2020) Towards Model-Based Development of Decentralised Peer-to-Peer Data Vaults. In: ACM / IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS). ACM / IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS), 16-23 Oct 2020 ACM
Abstract
Using centralised data storage systems has been the standard practice followed by online service providers when managing the personal data of their users. This method requires users to trust these providers and, to some extent, users are not in full control over their data. The development of applications around decentralised data vaults, i.e., encrypted storage systems located in user-managed devices, can give this control back to the users as sole owners of the data. However, the development of such applications is not effort-free, and it requires developers to have specialised knowledge, such as how to deploy secure and peer-to-peer communication systems. We present Vaultage, a model-based framework that can simplify the development of data vault applications. We demonstrate its core features through a social network application case study and include some initial evaluation results, showing Vaultage's code generation capabilities and some profiling analysis of the generated network components.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Association for Computing Machinery. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 02 Sep 2020 10:10 |
Last Modified: | 01 Jan 2025 00:18 |
Published Version: | https://doi.org/10.1145/3417990.3420043 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/3417990.3420043 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:165014 |