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About

I am Associate Professor with Habilitation at the Informatics Department of University of Minho, where I teach Fault-Tolerant Distributed Systems. I serve as a member of the Board of Directors of INESC TEC, director of the Minho Advanced Computing Centre, and co-director of the UT Austin Portugal program. I am the national representative to the EuroHPC JU.

I received my PhD. degree in 2000 from the École Polytechnique Fédérale de Lausanne. My main research contributions have been in the field of fault-tolerant distributed agreement and epidemic communication algorithms and in the conception, development and assessment of dependable database systems. I have coordinated the H2020 SafeCloud project on secure processing in the Cloud and the FP6 GORDA project on open database replication, two previous national projects on scalable dependable databases, ESCADA and StrongRep, and the U. Minho team in the FP7 CumuloNimbo and LeanBigdata projects. I published over 100 research papers on large scale and dependable distributed systems, and served on the programme committee of several highly reputed conferences. I chaired the PC of IFIP DAIS and IEEE SRDS, and served as general chair of SRDS and ACM Eurosys.

Interest
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Details

Details

  • Name

    Rui Carlos Oliveira
  • Cluster

    Computer Science
  • Role

    Member of the Board of Directors
  • Since

    01st November 2011
010
Publications

2021

CAT

Authors
Esteves, T; Neves, F; Oliveira, R; Paulo, J;

Publication
Middleware '21: 22nd International Middleware Conference, Québec City, Canada, December 6 - 10, 2021

Abstract

2020

A Comparison of Message Exchange Patterns in BFT Protocols: (Experience Report)

Authors
Silva, F; Alonso, AN; Pereira, J; Oliveira, R;

Publication
Distributed Applications and Interoperable Systems - 20th IFIP WG 6.1 International Conference, DAIS 2020, Held as Part of the 15th International Federated Conference on Distributed Computing Techniques, DisCoTec 2020, Valletta, Malta, June 15-19, 2020, Proceedings

Abstract
The performance and scalability of byzantine fault-tolerant (BFT) protocols for state machine replication (SMR) have recently come under scrutiny due to their application in the consensus mechanism of blockchain implementations. This led to a proliferation of proposals that provide different trade-offs that are not easily compared as, even if these are all based on message passing, multiple design and implementation factors besides the message exchange pattern differ between each of them. In this paper we focus on the impact of different combinations of cryptographic primitives and the message exchange pattern used to collect and disseminate votes, a key aspect for performance and scalability. By measuring this aspect in isolation and in a common framework, we characterise the design space and point out research directions for adaptive protocols that provide the best trade-off for each environment and workload combination. © IFIP International Federation for Information Processing 2020.

2020

Decentralized Privacy-Preserving Proximity Tracing

Authors
Troncoso, C; Payer, M; Hubaux, JP; Salathé, M; Larus, JR; Bugnion, E; Lueks, W; Stadler, T; Pyrgelis, A; Antonioli, D; Barman, L; Chatel, S; Paterson, KG; Capkun, S; Basin, DA; Beutel, J; Jackson, D; Roeschlin, M; Leu, P; Preneel, B; Smart, NP; Abidin, A; Gürses, SF; Veale, M; Cremers, C; Backes, M; Tippenhauer, NO; Binns, R; Cattuto, C; Barrat, A; Fiore, D; Barbosa, M; Oliveira, R; Pereira, J;

Publication
IEEE Data Eng. Bull.

Abstract

2020

On the Trade-Offs of Combining Multiple Secure Processing Primitives for Data Analytics

Authors
Carvalho, H; Cruz, D; Pontes, R; Paulo, J; Oliveira, R;

Publication
Distributed Applications and Interoperable Systems - 20th IFIP WG 6.1 International Conference, DAIS 2020, Held as Part of the 15th International Federated Conference on Distributed Computing Techniques, DisCoTec 2020, Valletta, Malta, June 15-19, 2020, Proceedings

Abstract
Cloud Computing services for data analytics are increasingly being sought by companies to extract value from large quantities of information. However, processing data from individuals and companies in third-party infrastructures raises several privacy concerns. To this end, different secure analytics techniques and systems have recently emerged. These initial proposals leverage specific cryptographic primitives lacking generality and thus having their application restricted to particular application scenarios. In this work, we contribute to this thriving body of knowledge by combining two complementary approaches to process sensitive data. We present SafeSpark, a secure data analytics framework that enables the combination of different cryptographic processing techniques with hardware-based protected environments for privacy-preserving data storage and processing. SafeSpark is modular and extensible therefore adapting to data analytics applications with different performance, security and functionality requirements. We have implemented a SafeSpark’s prototype based on Spark SQL and Intel SGX hardware. It has been evaluated with the TPC-DS Benchmark under three scenarios using different cryptographic primitives and secure hardware configurations. These scenarios provide a particular set of security guarantees and yield distinct performance impact, with overheads ranging from as low as 10% to an acceptable 300% when compared to an insecure vanilla deployment of Apache Spark. © IFIP International Federation for Information Processing 2020.

2018

Proceedings of the Thirteenth EuroSys Conference, EuroSys 2018, Porto, Portugal, April 23-26, 2018

Authors
Oliveira, R; Felber, P; Hu, YC;

Publication
EuroSys

Abstract

Supervised
thesis

2021

End-to-End Software-Defined Security for Big Data Ecosystem

Author
Tânia da Conceição Araújo Esteves

Institution
UM

2021

Desenho e estudo de usabilidade de uma plataforma de gestão de pacientes on-line

Author
Artur Sousa Ferreira

Institution
UP-FEUP

2020

End-to-End Software-Defined Security for Big Data Ecosystem

Author
Tânia da Conceição Araújo Esteves

Institution
UM

2019

Transcriptomics-based prediction of human phenotypes using scalable and secure machine learning approaches

Author
Marta Carolina Cabral Moreno

Institution
UP-FCUP

2019

Sistemas Inovadores de Segurança em Bases de Dados

Author
André Alexandre Pinheiro Marinho

Institution
UM