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About

About

Rogério Pontes came to HASLab looking for new challenges and expand his knowledge on computer science. In his first year, he completed his Master’s thesis on a Linear Algebra Approach to OLAP systems. The master thesis, the great group environment and the possibility to work on research projects relevant to the needs of users and the industry sparked his interest for research.  With this new found interest, he enrolled on the MAP-i Doctoral programme. His thesis, still in progress, deals with one current issue on today’s society, the privacy of data. With close proximity to the european project SafeCloud-eu, his research tackles the existing issues on data storage privacy and secure data processing.

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Details

Details

  • Name

    Rogério António Pontes
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    01st July 2014
002
Publications

2021

CODBS: A cascading oblivious search protocol optimized for real-world relational database indexes

Authors
Pontes, R; Portela, B; Barbosa, M; Vilaca, R;

Publication
2021 40TH INTERNATIONAL SYMPOSIUM ON RELIABLE DISTRIBUTED SYSTEMS (SRDS 2021)

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.

2019

d'Artagnan: A Trusted NoSQL Database on Untrusted Clouds

Authors
Pontes, R; Maia, F; Vilaça, R; Machado, N;

Publication
38th Symposium on Reliable Distributed Systems, SRDS 2019, Lyon, France, October 1-4, 2019

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.

2017

Performance trade-offs on a secure multi-party relational database

Authors
Pontes, R; Pinto, M; Barbosa, M; Vilaça, R; Matos, M; Oliveira, R;

Publication
Proceedings of the Symposium on Applied Computing, SAC 2017, Marrakech, Morocco, April 3-7, 2017

Abstract