Publications

Article

10.3390/fi14010028

Department of Computer Systems

In wireless ad hoc networks, security and communication challenges are frequently addressed by deploying a trust mechanism. A number of approaches for evaluating trust of ad hoc network nodes have been proposed, including the one that uses neural networks. We proposed to use packet delivery ratios as input to the neural network. In this article, we present a new method, called TARA (Trust-Aware Reactive Ad Hoc routing), to incorporate node trusts into reactive ad hoc routing protocols. The novelty of the TARA method is that it does not require changes to the routing protocol itself. Instead, it influences the routing choice from outside by delaying the route request messages of untrusted nodes. The performance of the method was evaluated on the use case of sensor nodes sending data to a sink node. The experiments showed that the method improves the packet delivery ratio in the network by about 70%. Performance analysis of the TARA method provided recommendations for its application in a particular ad hoc network.

Article

10.1007/s42979-022-01222-w

Department of Information Security

In this paper we propose a set of algorithms that can automatically detect the use of AES and automatically recover both the encryption key and the plaintext, assuming that we can control the code flow of the encrypting program, e.g., when an application is performing encryption without the user’s permission. The first algorithm makes use of the fact that we can monitor accesses to the AES S-Box and deduce the desired data from these accesses; the approach is suitable to software-based AES implementations, both naïve and optimized. To demonstrate the feasibility of this approach we designed a tool which implements the algorithm for Microsoft Windows running on the Intel x86 architecture. The tool has been successfully tested against a set of applications using different cryptographic libraries and common user applications. We also discuss the options of recovering the same data when hardware-assisted AES implementations on Intel-compatible architectures are used.

Article

10.1145/3485527

Programming Research Lab

As a scientific programming language, Julia strives for performance but also provides high-level productivity features. To avoid performance pathologies, Julia users are expected to adhere to a coding discipline that enables so-called type stability. Informally, a function is type stable if the type of the output depends only on the types of the inputs, not their values. This paper provides a formal definition of type stability as well as a stronger property of type groundedness, shows that groundedness enables compiler optimizations, and proves the compiler correct. We also perform a corpus analysis to uncover how these type-related properties manifest in practice.

Article

10.1016/j.eswa.2023.121164

Department of Applied Mathematics

Relevance-based ranking is a popular ingredient in recommenders, but it frequently struggles to meet fairness criteria because social and cultural norms may favor some item groups over others. For instance, some items might receive lower ratings due to some sort of bias (e.g. gender bias). A fair ranking should balance the exposure of items from advantaged and disadvantaged groups. To this end, we propose a novel post-processing framework to produce fair, exposure-aware recommendations. Our approach is based on an integer linear programming model maximizing the expected utility while satisfying a minimum exposure constraint. The model has fewer variables than previous work and thus can be deployed to larger datasets and allows the organization to define a minimum level of exposure for groups of items. We conduct an extensive empirical evaluation indicating that our new framework can increase the exposure of items from disadvantaged groups at a small cost of recommendation accuracy

Article

10.1162/dint_a_00161

Department of Software Engineering

While the FAIR Principles do not specify a technical solution for ‘FAIRness’, it was clear from the outset of the FAIR initiative that it would be useful to have commodity software and tooling that would simplify the creation of FAIR-compliant resources. The FAIR Data Point is a metadata repository that follows the DCAT(2) schema, and utilizes the Linked Data Platform to manage the hierarchical metadata layers as LDP Containers. There has been a recent flurry of development activity around the FAIR Data Point that has significantly improved its power and ease-of-use. Here we describe five specific tools—an installer, a loader, two Web-based interfaces, and an indexer—aimed at maximizing the uptake and utility of the FAIR Data Point.