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Publications

Publications by HASLab

2022

Boolean Searchable Symmetric Encryption with Filters on Trusted Hardware

Authors
Ferreira, B; Portela, B; Oliveira, T; Borges, G; Domingos, H; Leitao, J;

Publication
IEEE Transactions on Dependable and Secure Computing

Abstract

2022

AIDA-DB: A Data Management Architecture for the Edge and Cloud Continuum

Authors
Faria, N; Costa, D; Pereira, J; Vilaça, R; Ferreira, L; Coelho, F;

Publication
2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)

Abstract

2022

A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis

Authors
Brito, C; Esteves, M; Peixoto, H; Abelha, A; Machado, J;

Publication
Wireless Networks

Abstract
Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients’ health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

2022

A formal treatment of the role of verified compilers in secure computation

Authors
Almeida, JCB; Barbosa, M; Barthe, G; Pacheco, H; Pereira, V; Portela, B;

Publication
Journal of Logical and Algebraic Methods in Programming

Abstract
Secure multiparty computation (SMC) allows for complex computations over encrypted data. Privacy concerns for cloud applications makes this a highly desired technology and recent performance improvements show that it is practical. To make SMC accessible to non-experts and empower its use in varied applications, many domain-specific compilers are being proposed. We review the role of these compilers and provide a formal treatment of the core steps that they perform to bridge the abstraction gap between high-level ideal specifications and efficient SMC protocols. Our abstract framework bridges this secure compilation problem across two dimensions: 1) language-based source- to target-level semantic and efficiency gaps, and 2) cryptographic ideal- to real-world security gaps. We link the former to the setting of certified compilation, paving the way to leverage long-run efforts such as CompCert in future SMC compilers. Security is framed in the standard cryptographic sense. Our results are supported by a machine-checked formalisation carried out in EasyCrypt. © 2021 Elsevier Inc.

2022

Sense, Feel, Design - INTERACT 2021 IFIP TC 13 Workshops, Bari, Italy, August 30 - September 3, 2021, Revised Selected Papers

Authors
Ardito, C; Lanzilotti, R; Malizia, A; Lárusdóttir, M; Spano, LD; Campos, JC; Hertzum, M; Mentler, T; Abdelnour Nocera, JL; Piccolo, LSG; Sauer, S; der Veer, GCv;

Publication
INTERACT (Workshops)

Abstract

2022

Towards a Cross-domain Semantically Interoperable Ecosystem

Authors
Tosic, M; Coelho, FA; Nouwt, B; Rua, DE; Tomcic, A; Pesic, S;

Publication
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Abstract

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