Trusted Data Transformation with Blockchain Technology in Open Data

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Bruno Tavares (Author) Other; Filipe Figueiredo Correia (Author) FEUP; André Restivo (Author) FEUP

Conference proceedings International

Title: Distributed Computing and Artificial Intelligence, 16th International Conference, DCAI 2019, Avila, Spain, 26-28 June, 2019, Special Sessions

Pages: 213-216

16th International Conference on Distributed Computing and Artificial Intelligence, DCAI 2019

26 June 2019 through 28 June 2019

Indexing

Scopus - 2 Citations

Google Scholar

Other information

Authenticus ID: P-00Q-J0B

DOI: 10.1007/978-3-030-23946-6_28

Abstract (EN): Trusted open data can be used for auditing, accountability, business development, or as an anti-corruption mechanism. Metadata information can address provenance concerns, and trust issues can somehow be mitigated by digital signatures. Those approaches can trace the data origin, but usually lack information about the transformation process. Creating trust in an open data service through technology can reduce the need for third-party certifications, and creating a distributed consensus mechanism capable of validating all the transformations can guarantee that the datasets are reliable and easy to use. This work aims to leverage blockchain technologies to track open data transformations, allowing consumers to verify the data using a distributed ledger, and providing a mechanism capable of publishing trusted transformed data without relying on third-party certifications. To validate the proposed approach, use cases for data transformation will be used. The consensus protocol must be capable of validating the transformations according to a predefined algorithm, the provider must be capable of publishing verifiable transformed data, and the consumer should be able to check if a dataset originated by a transformation is legit. © 2020, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4