Abstract

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.

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+delta. We integrate this method with the FIWARE platform, as a way to provision and authenticate IoT devices.

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.

Abstract
The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.

Abstract
There is no generally accepted definition for conditional Tsallis entropy. The standard definition of (unconditional) Tsallis entropy depends on a parameter alpha that converges to the Shannon entropy as alpha approaches 1. In this paper, we describe three proposed definitions of conditional Tsallis entropy suggested in the literature-their properties are studied and their values, as a function of alpha, are compared. We also consider another natural proposal for conditional Tsallis entropy and compare it with the existing ones. Lastly, we present an online tool to compute the four conditional Tsallis entropies, given the probability distributions and the value of the parameter alpha.