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.

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.

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.

Abstract
The Robot Operating System (ROS) is nowadays one of the most popular frameworks for developing robotic applications. To ensure the (much needed) dependability and safety of such applications we forecast an increasing demand for ROS-specific coding standards, static analyzers, and tools alike. Unfortunately, the development of such standards and tools can be hampered by ROS modularity and configurability, namely the substantial number of primitives (and respective variants) that must, in principle, be considered. To quantify the severity of this problem, we have mined a large number of existing ROS packages to understand how its primitives are used in practice, and to determine which combinations of primitives are most popular. This paper presents and discusses the results of this study, and hopefully provides some guidance for future standardization efforts and tool developers.

Abstract
The advancement of constraint solvers and model checkers has enabled the effective analysis of high-level formal specification languages. However, these typically handle a specification in an opaque manner, amalgamating all its constraints in a single monolithic verification task, which often proves to be a performance bottleneck. This paper addresses this issue by proposing a solving strategy that exploits user-provided partial knowledge, namely by assigning symbolic bounds to the problem’s variables, to automatically decompose a verification task into smaller ones, which are prone to being independently analyzed in parallel and with tighter search spaces. An effective implementation of the technique is provided as an extension to the Kodkod relational constraint solver. Evaluation shows that, in average, the proposed technique outperforms the regular amalgamated verification procedure.

Abstract