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About

I am currently a senior researcher at INESC TEC and University of Minho. I have obtained a PhD degree in Computer Science from the MAP-i Doctoral Program in Computer Science, which is a joint program of Minho, Aveiro and Porto Universities with the collaboration of CMU and UT-Austin Universities (2015). Also, I have a M.Sc. in Informatics Engineering (2009) and a B.Sc. in Informatics Engineering (2007), both concluded at the University of Minho.

Currently, my research is focused on large scale distributed systems with an emphasis on storage systems and data management. I have several publications on journals and international conferences, and I have participated in the research and development of EU (CoherentPaaS, SafeCloud) and national (Pastramy, RED) projects.

For more information you can check my web page at https://jtpaulo.github.io, as well as, HASLab's research lines on the topics mentioned above: https://dsr-haslab.github.io and https://dbr-haslab.github.io

Interest
Topics
Details

Details

  • Name

    João Tiago Paulo
  • Role

    Assistant Researcher
  • Since

    01st November 2011
  • Nationality

    Portugal
  • Contacts

    +351253604440
    joao.t.paulo@inesctec.pt
005
Publications

2020

A Survey and Classification of Software-Defined Storage Systems

Authors
Macedo, R; Paulo, J; Pereira, J; Bessani, A;

Publication
ACM Computing Surveys

Abstract

2020

GenoDedup: Similarity-Based Deduplication and Delta-Encoding for Genome Sequencing Data

Authors
Cogo, VV; Paulo, J; Bessani, A;

Publication
IEEE Transactions on Computers

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

TrustFS: An SGX-Enabled Stackable File System Framework

Authors
Esteves, T; Macedo, R; Faria, A; Portela, B; Paulo, J; Pereira, J; Harnik, D;

Publication
2019 38th International Symposium on Reliable Distributed Systems Workshops (SRDSW)

Abstract

2019

A Case for Dynamically Programmable Storage Background Tasks

Authors
Macedo, R; Faria, A; Paulo, J; Pereira, J;

Publication
2019 38th International Symposium on Reliable Distributed Systems Workshops (SRDSW)

Abstract

Supervised
thesis

2019

Towards a Privacy-Preserving Distributed Machine Learning Framework

Author
Cláudia Vanessa Martins de Brito

Institution
UM

2019

Towards a Dependable and Decentralized Software-Defined Storage Architecture

Author
Ricardo Gonçalves Macedo

Institution
UP-FCUP

2019

Processamento Analítico de Dados Seguros

Author
Hugo Alves Carvalho

Institution
UM

2019

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

Author
Tânia da Conceição Araújo Esteves

Institution
UM

2018

Avaliação Realista de Sistemas de Armazenamento

Author
Alexandre Silva

Institution
UM