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Publications

Publications by HASLab

2024

Course mapping dataset for the paper "State of the Practice in Software Testing Teaching in Four European Countries"

Authors
Tramontana, P; Marín, B; Paiva, ACR; Mendes, A; Vos, TEJ; Amalfitano, D; Cammaerts, F; Snoeck, M; Fasolino, AR;

Publication

Abstract

2024

A Distributed Computing Solution for Privacy-Preserving Genome-Wide Association Studies

Authors
Brito, C; Ferreira, P; Paulo, J;

Publication

Abstract
AbstractBreakthroughs in sequencing technologies led to an exponential growth of genomic data, providing unprecedented biological in-sights and new therapeutic applications. However, analyzing such large amounts of sensitive data raises key concerns regarding data privacy, specifically when the information is outsourced to third-party infrastructures for data storage and processing (e.g., cloud computing). Current solutions for data privacy protection resort to centralized designs or cryptographic primitives that impose considerable computational overheads, limiting their applicability to large-scale genomic analysis.We introduce Gyosa, a secure and privacy-preserving distributed genomic analysis solution. Unlike in previous work, Gyosafollows a distributed processing design that enables handling larger amounts of genomic data in a scalable and efficient fashion. Further, by leveraging trusted execution environments (TEEs), namely Intel SGX, Gyosaallows users to confidentially delegate their GWAS analysis to untrusted third-party infrastructures. To overcome the memory limitations of SGX, we implement a computation partitioning scheme within Gyosa. This scheme reduces the number of operations done inside the TEEs while safeguarding the users’ genomic data privacy. By integrating this security scheme inGlow, Gyosaprovides a secure and distributed environment that facilitates diverse GWAS studies. The experimental evaluation validates the applicability and scalability of Gyosa, reinforcing its ability to provide enhanced security guarantees. Further, the results show that, by distributing GWASes computations, one can achieve a practical and usable privacy-preserving solution.

2024

Mastering Artifact Correction in Neuroimaging Analysis: A Retrospective Approach

Authors
Oliveira, A; Cepa, B; Brito, C; Sousa, A;

Publication

Abstract
The correction of artifacts in Magnetic Resonance Imaging (MRI) is increasingly relevant as voluntary and involuntary artifacts can hinder data acquisition. Reverting from corrupted to artifact-free images is a complex task. Deep Learning (DL) models have been employed to preserve data characteristics and to identify and correct those artifacts. We propose MOANA, a novel DL-based solution to correct artifacts in multi-contrast brain MRI scans. MOANA offers two models: the simulation and the correction models. The simulation model introduces perturbations similar to those occurring in an exam while preserving the original image as ground truth; this is required as publicly available datasets rarely have motion-corrupted images. It allows the addition of three types of artifacts with different degrees of severity. The DL-based correction model adds a fourth contrast to state-of-the-art solutions while improving the overall performance of the models. MOANA achieved the highest results in the FLAIR contrast, with a Structural Similarity Index Measure (SSIM) of 0.9803 and a Normalized Mutual Information (NMI) of 0.8030. With this, the MOANA model can correct large volumes of images in less time and adapt to different levels of artifact severity, allowing for better diagnosis.

2024

Mastering Artifact Correction in Neuroimaging Analysis: A Retrospective Approach

Authors
Oliveira, A; Cepa, B; Brito, C; Sousa, A;

Publication

Abstract
The correction of artifacts in Magnetic Resonance Imaging (MRI) is increasingly relevant as voluntary and involuntary artifacts can hinder data acquisition. Reverting from corrupted to artifact-free images is a complex task. Deep Learning (DL) models have been employed to preserve data characteristics and to identify and correct those artifacts. We propose MOANA, a novel DL-based solution to correct artifacts in multi-contrast brain MRI scans. MOANA offers two models: the simulation and the correction models. The simulation model introduces perturbations similar to those occurring in an exam while preserving the original image as ground truth; this is required as publicly available datasets rarely have motion-corrupted images. It allows the addition of three types of artifacts with different degrees of severity. The DL-based correction model adds a fourth contrast to state-of-the-art solutions while improving the overall performance of the models. MOANA achieved the highest results in the FLAIR contrast, with a Structural Similarity Index Measure (SSIM) of 0.9803 and a Normalized Mutual Information (NMI) of 0.8030. With this, the MOANA model can correct large volumes of images in less time and adapt to different levels of artifact severity, allowing for better diagnosis.

2024

An adequacy theorem between mixed powerdomains and probabilistic concurrency

Authors
Neves, R;

Publication
CoRR

Abstract

2024

Flow Correlation Attacks on Tor Onion Service Sessions with Sliding Subset Sum

Authors
Lopes, D; Dong, JD; Medeiros, P; Castro, D; Barradas, D; Portela, B; Vinagre, J; Ferreira, B; Christin, N; Santos, N;

Publication
31st Annual Network and Distributed System Security Symposium, NDSS 2024, San Diego, California, USA, February 26 - March 1, 2024

Abstract

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