BlockFlow: Trust in Scientific Provenance Data

  • Raiane Coelho
  • Regina Braga
  • José Maria David
  • Fernanda Campos
  • Victor Ströele


In scientific collaboration, the data sharing, the exchange of ideas and results is crucial to promote knowledge and accelerate the development of science. Trust is extremely important in this context as well as reproducibility. Although in scientific workflow the provenance has been the basis for reproducibility, in collaborative environments it is necessary to ensure integrity and trustworthiness of this provenance data. One of the technologies that have emerged and can help to address these issues is blockchain. A blockchain-based provenance system for collaborative scientific experiments could lead to a trustworthy environment for scientific experimentation. In this vein, this paper presents the specification of an architecture, named BlockFlow, that provides trust for distributed provenance data.


Androulaki, E., Barger, A., Bortnikov, V., Cachin, C., Christidis, K., De Caro, A., & Muralidharan, S. (2018, April). Hyperledger fabric: a distributed operating system for permissioned blockchains. In Proceedings of the Thirteenth EuroSys Conference (p. 30). ACM.

Baker, M. (2016). 1500 scientists lift the lid on reproducibility. Nature News, 533(7604), 452.

Cuevas-Vicenttín, V., Ludäscher, B., Missier, P., Belhajjame, K., Chirigati, F., Wei, Y., and Altintas, I. 2015. ProvONE: A PROV Extension Data Model for Scientific Workflow Provenance.

Freitas, V., David, J. M., Braga, R., and Campos, F. (2015). An architecture for scientific software ecosystem. In 9th Workshop on Distributed Software Development, Software Ecosystems and Systems-of-Systems (WDES 2015), pages 41–48. (in Portuguese)

Groth, P., and Moreau, L. 2013. PROV-Overview. An overview of the PROV Family of Documents. Herschel, M., Diestelkämper, R., & Ben Lahmar, H. (2017). A survey on provenance: What for? What form? What from?. The VLDB Journal—The International Journal on Very Large Data Bases, 26(6), 881-906.

Liang, X., Shetty, S., Tosh, D., Kamhoua, C., Kwiat, K., & Njilla, L. (2017, May). Provchain: A blockchain-based data provenance architecture in cloud environment with enhanced privacy and availability. In Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (pp. 468-477). IEEE Press

Manikas, K. (2016). Revisiting software ecosystems research: A longitudinal literature study. Journal of Systems and Software, 117:84–103. Missier, P., Dey, S., Belhajjame, K., Cuevas-Vicenttín, V., & Ludäscher, B. (2013). D-PROV: Extending the {PROV} Provenance Model with Workflow Structure. In 5th {USENIX} Workshop on the Theory and Practice of Provenance (TaPP 13). Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.

Pahl, C., El Ioini, N., Helmer, S., & Lee, B. (2018, April). An architecture pattern for trusted orchestration in IoT edge clouds. In Fog and Mobile Edge Computing (FMEC), 2018 Third International Conference on (pp. 63-70). IEEE.

Ramachandran, A., & Kantarcioglu, M. (2018, March). SmartProvenance: A Distributed, Blockchain Based DataProvenance System. In Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy (pp. 35-42). ACM

Sirqueira, T. F., Dalpra, H. L., Braga, R., Araujo, M. A., David, J. M. N., and Campos, F. (2016). E-SECO proversion: Manutenção e evolução de experimentos científicos. In BreSci – 10º Brazilian e-Science Workshop, pages 253–260. CSBC.

Stodden, V. (2010). The scientific method in practice: Reproducibility in the computational sciences. Vukolić, M. (2015, October). The quest for scalable blockchain fabric: Proof-of-work vs. BFT replication. In International workshop on open problems in network security (pp. 112-125). Springer, Cham.
Como Citar

Selecione um Formato
COELHO, Raiane; BRAGA, Regina; DAVID, José Maria; CAMPOS, Fernanda; STRÖELE, Victor. BlockFlow: Trust in Scientific Provenance Data. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 13. , 2019, Belém. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . ISSN 2763-8774. DOI: