Secure Declassification in Faceted JavaScript

Information leaks currently represent a major security vulnerability. Malicious code, when injected into a trusted environment and executed in the context of the victim’s privileges, often results in the loss of sensitive information. To address this security issue, this paper focuses on the idea of information flow control using faceted execution [3]. This mechanism allows the interpreter to efficiently keep track of variables across multiple security levels, achieving termination-insensitive non-interference (TINI). With TINI, a program can only leak one bit of data, caused by the termination of a program. One key benefit of having faceted execution is that flow policy can be enforced automatically on the basis of its architecture, rather than relying on filtering, validation, and encoding, over user inputs. Despite the fact that information flow control ensures strong confidentiality, such a model is too restrictive for many real-world applications. Declassification offers one way of releasing sensitive information in a controlled manner. This paper introduces Faceted JS, a modified JavaScript language that supports basic JavaScript features as well as faceted executions. To demonstrate the proper way to release sensitive data, a declassification mechanism is implemented, based on the concept of the object capability model [12] and policy-agnostic programming [4]. Finally, we cover the aspect of implementation and offer some practical examples

Constructing and Validating Feature Models Using Relational, Document, and Graph Databases

Building a software product line (SPL) is a systematic strategy for reusing software within a family of related systems from some application domain. To define an SPL, a domain analyst must identify the common and variable aspects of a family of systems and capture them for later use in construction of specific products. To do so, Feature-Oriented Domain Analysis (FODA) introduced the feature model as an abstraction to represent the common and variable aspects, using a feature diagram to depict the model visually. However, this abstraction is often difficult for developers to use because most tools rely on specialized theories, notations, or technologies

The Code Mini-Map Visualisation: Encoding Conceptual Structures Within Source Code

Modern source code editors typically include a code mini-map visualisation, which provides programmers with an overview of the currently open source code document. This paper proposes to add a layering mechanism to the code mini- map visualisation in order to provide programmers with visual answers to questions related to conceptual structures that are not manifested directly in the code. Details regarding the design and implementation of this scope information layer, which displays additional encodings that correspond to the scope chain and information related to the scope chain within a source code document, is presented. The scope information layer can be used by programmers to answer questions such as: to which scope does a specific variable belong, and in which scope is the cursor of the source code editor currently located in. Additionally, this paper presents a study that evaluates the effectiveness of adding the scope information layer to a code mini-map visualisation in order to help programmers understand scope relationships within source code. The results of the study show that the incorporating additional layers of information onto the code mini-map visualisation can have a positive effect on code understanding

The Code Mini-Map Visualisation - Encoding Conceptual Structures Within Source Code

Modern source code editors typically include a code mini-map visualisation, which provides programmers with an overview of the currently open source code document. This paper proposes to add a layering mechanism to the code mini-map visualisation in order to provide programmers with visual answers to questions related to conceptual structures that are not manifested directly in the code

From Big Data to Big Displays: High-Performance Visualization at Blue Brain

Blue Brain has pushed high-performance visualization (HPV) to complement its HPC strategy since its inception in 2007. In 2011, this strategy has been accelerated to develop innovative visualization solutions through increased funding and strategic partnerships with other research institutions. We present the key elements of this HPV ecosystem, which integrates C++ visualization applications with novel collaborative display systems. We motivate how our strategy of transforming visualization engines into services enables a variety of use cases, not only for the integration with high-fidelity displays, but also to build service oriented architectures, to link into web applications and to provide remote services to Python applications.Comment: ISC 2017 Visualization at Scale worksho

Visualization of heterogeneous data

Abstract — Both the Resource Description Framework (RDF), used in the semantic web, and Maya Viz u-forms represent data as a graph of objects connected by labeled edges. Existing systems for flexible visualization of this kind of data require manual specification of the possible visualization roles for each data attribute. When the schema is large and unfamiliar, this requirement inhibits exploratory visualization by requiring a costly up-front data integration step. To eliminate this step, we propose an automatic technique for mapping data attributes to visualization attributes. We formulate this as a schema matching problem, finding appropriate paths in the data model for each required visualization attribute in a visualization template. Index Terms—Data integration, RDF, attribute inference.