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Sobre

Atualmente sou um investigador auxiliar no INESC TEC e Universidade do Minho. Obtive o grau de doutoramento em 2015 através do programa doutoral em Ciências da Computação MAP-I, um programa conjunto das Universidades do Minho, Aveiro e Porto e em colaboração com as Universidades de UT-Austin e CMU. Anteriormente, obtive o grau de Mestre (2009) e Licenciado (2007) em Engenharia Informática pela Universidade do Minho.

A minha investigação foca-se principalmente na àrea de sistemas distribuídos de larga escala com um ênfase nas àreas de armazenamento e gestão de dados. Possuo diversas publicações em conferências e revistas internacionais e com a participação em diversos projetos Europeus (CoherentPaaS, SafeCloud) e Nacionais (Pastramy, RED).

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    João Tiago Paulo
  • Cargo

    Investigador Auxiliar
  • Desde

    01 novembro 2011
  • Nacionalidade

    Portugal
  • Contactos

    +351253604440
    joao.t.paulo@inesctec.pt
004
Publicações

2020

A Survey and Classification of Software-Defined Storage Systems

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

Publicação
ACM Computing Surveys

Abstract

2020

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

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

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

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.

2020

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

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

Publicação
IEEE Transactions on Computers

Abstract

2019

TrustFS: An SGX-Enabled Stackable File System Framework

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

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

Abstract

2019

A Case for Dynamically Programmable Storage Background Tasks

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

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

Abstract

Teses
supervisionadas

2019

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

Autor
Tânia da Conceição Araújo Esteves

Instituição
UM

2019

Towards a Privacy-Preserving Distributed Machine Learning Framework

Autor
Cláudia Vanessa Martins de Brito

Instituição
UM

2019

Towards a Dependable and Decentralized Software-Defined Storage Architecture

Autor
Ricardo Gonçalves Macedo

Instituição
UP-FCUP

2019

Processamento Analítico de Dados Seguros

Autor
Hugo Alves Carvalho

Instituição
UM

2018

End-to-end Software-Defined Security for BigData Ecosystems

Autor
Tânia da Conceição Araújo Esteves

Instituição
UM