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Details

  • Name

    Luís Filipe Antunes
  • Cluster

    Computer Science
  • Role

    Centre Coordinator
  • Since

    15th December 2011
006
Publications

2021

The Entropy Universe

Authors
Ribeiro, M; Henriques, T; Castro, L; Souto, A; Antunes, L; Costa Santos, C; Teixeira, A;

Publication
Entropy

Abstract
About 160 years ago, the concept of entropy was introduced in thermodynamics by Rudolf Clausius. Since then, it has been continually extended, interpreted, and applied by researchers in many scientific fields, such as general physics, information theory, chaos theory, data mining, and mathematical linguistics. This paper presents The Entropy Universe, which aims to review the many variants of entropies applied to time-series. The purpose is to answer research questions such as: How did each entropy emerge? What is the mathematical definition of each variant of entropy? How are entropies related to each other? What are the most applied scientific fields for each entropy? We describe in-depth the relationship between the most applied entropies in time-series for different scientific fields, establishing bases for researchers to properly choose the variant of entropy most suitable for their data. The number of citations over the past sixteen years of each paper proposing a new entropy was also accessed. The Shannon/differential, the Tsallis, the sample, the permutation, and the approximate entropies were the most cited ones. Based on the ten research areas with the most significant number of records obtained in the Web of Science and Scopus, the areas in which the entropies are more applied are computer science, physics, mathematics, and engineering. The universe of entropies is growing each day, either due to the introducing new variants either due to novel applications. Knowing each entropy’s strengths and of limitations is essential to ensure the proper improvement of this research field.

2021

Privacy Technologies and Policy - 9th Annual Privacy Forum, APF 2021, Oslo, Norway, June 17-18, 2021, Proceedings

Authors
Gruschka, N; Coelho Antunes, LF; Rannenberg, K; Drogkaris, P;

Publication
APF

Abstract

2021

Towards a Modular On-Premise Approach for Data Sharing

Authors
Resende, JS; Magalhaes, L; Brandao, A; Martins, R; Antunes, L;

Publication
Sensors

Abstract
The growing demand for everyday data insights drives the pursuit of more sophisticated infrastructures and artificial intelligence algorithms. When combined with the growing number of interconnected devices, this originates concerns about scalability and privacy. The main problem is that devices can detect the environment and generate large volumes of possibly identifiable data. Public cloud-based technologies have been proposed as a solution, due to their high availability and low entry costs. However, there are growing concerns regarding data privacy, especially with the introduction of the new General Data Protection Regulation, due to the inherent lack of control caused by using off-premise computational resources on which public cloud belongs. Users have no control over the data uploaded to such services as the cloud, which increases the uncontrolled distribution of information to third parties. This work aims to provide a modular approach that uses cloud-of-clouds to store persistent data and reduce upfront costs while allowing information to remain private and under users’ control. In addition to storage, this work also extends focus on usability modules that enable data sharing. Any user can securely share and analyze/compute the uploaded data using private computing without revealing private data. This private computation can be training machine learning (ML) models. To achieve this, we use a combination of state-of-the-art technologies, such as MultiParty Computation (MPC) and K-anonymization to produce a complete system with intrinsic privacy properties.

2021

Provisioning, authentication and secure communications for iot devices on fiware

Authors
Sousa, P; Magalhaes, L; Resende, J; Martins, R; Antunes, L;

Publication
Sensors

Abstract
The increasing pervasiveness of the Internet of Things is resulting in a steady increase of cyberattacks in all of its facets. One of the most predominant attack vectors is related to its identity management, as it grants the ability to impersonate and circumvent current trust mechanisms. Given that identity is paramount to every security mechanism, such as authentication and access control, any vulnerable identity management mechanism undermines any attempt to build secure systems. While digital certificates are one of the most prevalent ways to establish identity and perform authentication, their provision at scale remains open. This provisioning process is usually an arduous task that encompasses device configuration, including identity and key provisioning. Human configuration errors are often the source of many security and privacy issues, so this task should be semi-autonomous to minimize erroneous configurations during this process. In this paper, we propose an identity management (IdM) and authentication method called YubiAuthIoT. The overall provisioning has an average runtime of 1137.8 ms ± 65.11 + d. We integrate this method with the FIWARE platform, as a way to provision and authenticate IoT devices. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

2021

Fundamental privacy rights in a pandemic state

Authors
Carvalho, T; Faria, P; Antunes, L; Moniz, N;

Publication
PLOS ONE

Abstract
Faced with the emergence of the Covid-19 pandemic, and to better understand and contain the disease's spread, health organisations increased the collaboration with other organisations sharing health data with data scientists and researchers. Data analysis assists such organisations in providing information that could help in decision-making processes. For this purpose, both national and regional health authorities provided health data for further processing and analysis. Shared data must comply with existing data protection and privacy regulations. Therefore, a robust de-identification procedure must be used, and a re-identification risk analysis should also be performed. De-identified data embodies state-of-the-art approaches in Data Protection by Design and Default because it requires the protection of direct and indirect identifiers (not just direct). This article highlights the importance of assessing re-identification risk before data disclosure by analysing a data set of individuals infected by Covid-19 that was made available for research purposes. We stress that it is highly important to make this data available for research purposes and that this process should be based on the state of the art methods in Data Protection by Design and by Default. Our main goal is to consider different re-identification risk analysis scenarios since the information on the intruder side is unknown. Our conclusions show that there is a risk of identity disclosure for all of the studied scenarios. For one, in particular, we proceed to an example of a re-identification attack. The outcome of such an attack reveals that it is possible to identify individuals with no much effort.

Supervised
thesis

2020

A secure mobile based health information system solution - from understanding and conceptualizing to prototyping the patient experience

Author
Cátia Andreia Santos Pereira Augusto

Institution
UP-FEUP

2020

Anomaly Detection in Cybersecurity

Author
Maria Inês Pinto Bastos Martins

Institution
UP-FCUP

2020

Security Enhancing Technologies for Cloud-of-Clouds

Author
João Miguel Maia Soares de Resende

Institution
UP-FCUP

2020

Nonlinear Analysis Methods in Physiological Signals

Author
Maria do Rosário Campos Ribeiro

Institution
UP-FCUP

2020

Automated standard based security assessment for IoT

Author
André Nuno de Pinho Tavares Gurgo e Cirne

Institution
UP-FCUP