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Publications

Publications by HASLab

2017

A Framework for Certification of Large-scale Component-based Parallel Computing Systems in a Cloud Computing Platform for HPC Services

Authors
Oliveira Dantas, ABd; Carvalho Junior, FHd; Barbosa, LS;

Publication
CLOSER

Abstract

2017

Layered Logics, Coalgebraically

Authors
Barbosa, LS;

Publication
Dynamic Logic. New Trends and Applications - First International Workshop, DALI 2017, Brasilia, Brazil, September 23-24, 2017, Proceedings

Abstract

2017

Digital Governance for Sustainable Development

Authors
Barbosa, LS;

Publication
Digital Nations - Smart Cities, Innovation, and Sustainability - 16th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2017, Delhi, India, November 21-23, 2017, Proceedings

Abstract
This lecture discusses the impact of digital transformation of governance mechanisms as a tool to promote sustainable development and more inclusive societies, in the spirit of the United Nations 2030 Agenda. Three main challenges are addressed: the pursuit of inclusiveness, trustworthiness of software infrastructures, and the mechanisms to enforce more transparent and accountable public institutions. © IFIP International Federation for Information Processing 2017.

2017

Labeled Homomorphic Encryption - Scalable and Privacy-Preserving Processing of Outsourced Data

Authors
Barbosa, M; Catalano, D; Fiore, D;

Publication
ESORICS (1)

Abstract
In privacy-preserving processing of outsourced data a Cloud server stores data provided by one or multiple data providers and then is asked to compute several functions over it. We propose an efficient methodology that solves this problem with the guarantee that a honest-but-curious Cloud learns no information about the data and the receiver learns nothing more than the results. Our main contribution is the proposal and efficient instantiation of a new cryptographic primitive called Labeled Homomorphic Encryption (labHE). The fundamental insight underlying this new primitive is that homomorphic computation can be significantly accelerated whenever the program that is being computed over the encrypted data is known to the decrypter and is not secret—previous approaches to homomorphic encryption do not allow for such a trade-off. Our realization and implementation of labHE targets computations that can be described by degree-two multivariate polynomials. As an application, we consider privacy preserving Genetic Association Studies (GAS), which require computing risk estimates from features in the human genome. Our approach allows performing GAS efficiently, non interactively and without compromising neither the privacy of patients nor potential intellectual property of test laboratories.

2017

Secure Multiparty Computation from SGX

Authors
Bahmani, R; Barbosa, M; Brasser, F; Portela, B; Sadeghi, AR; Scerri, G; Warinschi, B;

Publication
Financial Cryptography

Abstract
In this paper we show how Isolated Execution Environments (IEE) offered by novel commodity hardware such as Intel’s SGX provide a new path to constructing general secure multiparty computation (MPC) protocols. Our protocol is intuitive and elegant: it uses code within an IEE to play the role of a trusted third party (TTP), and the attestation guarantees of SGX to bootstrap secure communications between participants and the TTP. The load of communications and computations on participants only depends on the size of each party’s inputs and outputs and is thus small and independent from the intricacies of the functionality to be computed. The remaining computational load– essentially that of computing the functionality – is moved to an untrusted party running an IEE-enabled machine, an attractive feature for Cloud-based scenarios. Our rigorous modular security analysis relies on the novel notion of labeled attested computation which we put forth in this paper. This notion is a convenient abstraction of the kind of attestation guarantees one can obtain from trusted hardware in multi-user scenarios. Finally, we present an extensive experimental evaluation of our solution on SGX-enabled hardware. Our implementation is open-source and it is functionality agnostic: it can be used to securely outsource to the Cloud arbitrary off-the-shelf collaborative software, such as the one employed on financial data applications, enabling secure collaborative execution over private inputs provided by multiple parties.

2017

A Feature-Based Classification of Model Repair Approaches

Authors
Macedo, N; Jorge, T; Cunha, A;

Publication
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

Abstract
Consistency management, the ability to detect, diagnose and handle inconsistencies, is crucial during the development process in Model-driven Engineering (MDE). As the popularity and application scenarios of MDE expanded, a variety of different techniques were proposed to address these tasks in specific contexts. Of the various stages of consistency management, this work focuses on inconsistency handling in MDE, particularly in model repair techniques. This paper proposes a feature-based classification system for model repair techniques, based on an systematic literature review of the area. We expect this work to assist developers and researchers from different disciplines in comparing their work under a unifying framework, and aid MDE practitioners in selecting suitable model repair approaches.

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