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About

I am associate professor at the Informatics Department of University of Minho, where I teach Distributed Systems in undergraduate, master and doctoral courses. I've been the director of the Computer Science and Technology Center (CCTC) of University of Minho from 2005 to 2010 and director of the High-Assurance Software Laboratory (HASLab), a research unit of University of Minho and INESC TEC, from 2010 to 2015. Currently, I am member of the Board of Directors of INESC TEC.

I received my PhD. degree in 2000 from the École Polytechnique Fédérale de Lausanne under the supervision of André Schiper and Rachid Guerraoui. In this work I studied the distributed consensus problem in an environment where participants could fail by crashing and then recover. My research interests are in dependable distributed systems, in particular with application to dependable distributed database systems and large scale systems. My work has been focused on on epidemic communication protocols, large scale data management and high-performace transactional middleware for cloud computing and data science. I've been involved in several research projects funded by the EU, FCT and national and international companies, having coordinated GORDA, ESCADA, StrongRep and Stratus. I currently coordinate the H2020 SafeCloud project.

I currently serve on the Steering Committees of the IEEE SRDS, ACM/IFIP/USENIX Middleware, IFIP DAIS conferences, on the Scientific and Technologic Committee of Instituto do Petróleo e Gás (ISPG) and as vice-chair of IFIP Working Group 6.1.

Interest
Topics
Details

Details

  • Name

    Rui Carlos Oliveira
  • Role

    Member of the Board of Directors
  • Since

    01st November 2011
  • Nationality

    Portugal
  • Contacts

    +351222094030
    rui.oliveira@inesctec.pt
011
Publications

2020

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

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

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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
CoRR

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

2017

SafeFS: a modular architecture for secure user-space file systems: one FUSE to rule them all

Authors
Pontes, Rogerio; Burihabwa, Dorian; Maia, Francisco; Paulo, Joao; Schiavoni, Valerio; Felber, Pascal; Mercier, Hugues; Oliveira, Rui;

Publication
Proceedings of the 10th ACM International Systems and Storage Conference, SYSTOR 2017, Haifa, Israel, May 22-24, 2017

Abstract
The exponential growth of data produced, the ever faster and ubiquitous connectivity, and the collaborative processing tools lead to a clear shift of data stores from local servers to the cloud. This migration occurring across different application domains and types of users|individual or corporate|raises two immediate challenges. First, outsourcing data introduces security risks, hence protection mechanisms must be put in place to provide guarantees such as privacy, confidentiality and integrity. Second, there is no \one-size-fits-all" solution that would provide the right level of safety or performance for all applications and users, and it is therefore necessary to provide mechanisms that can be tailored to the various deployment scenarios. In this paper, we address both challenges by introducing SafeFS, a modular architecture based on software-defined storage principles featuring stackable building blocks that can be combined to construct a secure distributed file system. SafeFS allows users to specialize their data store to their specific needs by choosing the combination of blocks that provide the best safety and performance tradeoffs. The file system is implemented in user space using FUSE and can access remote data stores. The provided building blocks notably include mechanisms based on encryption, replication, and coding. We implemented SafeFS and performed indepth evaluation across a range of workloads. Results reveal that while each layer has a cost, one can build safe yet efficient storage architectures. Furthermore, the different combinations of blocks sometimes yield surprising tradeoffs. © 2017 ACM.

Supervised
thesis

2019

Sistemas Inovadores de Segurança em Bases de Dados

Author
André Alexandre Pinheiro Marinho

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

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

Author
Tânia da Conceição Araújo Esteves

Institution
UM

2018

Towards a Transactional and Analytical Data Management System for Big Data

Author
Fábio André Castenheira Luís Coelho

Institution
UM

2018

Processamento analítico seguro

Author
Daniel Carvalho da Cruz

Institution