http://dl.gi.de/handle/20.500.12116/39742 2026-07-22T22:10:51Z http://dl.gi.de/handle/20.500.12116/39754 Enabling data-centric AI through data quality management and data literacy Abedjan, Ziawasch Data is being produced at an intractable pace. At the same time, there is an insatiable interest in using such data for use cases that span all imaginable domains, including health, climate, business, and gaming. Beyond the novel socio-technical challenges that surround data-driven innovations, there are still open data processing challenges that impede the usability of data-driven techniques. It is commonly acknowledged that overcoming heterogeneity of data with regard to syntax and semantics to combine various sources for a common goal is a major bottleneck. Furthermore, the quality of such data is always under question as the data science pipelines today are highly ad-hoc and without the necessary care for provenance. Finally, quality criteria that go beyond the syntactical and semantic correctness of individual values but also incorporate population-level constraints, such as equal parity and opportunity with regard to protected groups, play a more and more important role in this process. Traditional research on data integration was focused on post-merger integration of companies, where customer or product databases had to be integrated. While this is often hard enough, today the challenges aggravate because of the fact that more stakeholders are using data analytics tools to derive domain-specific insights. I call this phenomenon the democratization of data science, a process, which is both challenging and necessary. Novel systems need to be user-friendly in a way that not only trained database admins can handle them but also less computer science savvy stakeholders. Thus, our research focuses on scalable example-driven techniques for data preparation and curation. Furthermore, we believe that it is important to educate the breadth of society on implications of a data-driven world and actively promote the concept of data literacy as a fundamental competence. 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/39749 Exploring syntactical features for anomaly detection in application logs Copstein, Rafael; Karlsen, Egil; Schwartzentruber, Jeff; Zincir-Heywood, Nur; Heywood, Malcolm In this research, we analyze the effect of lightweight syntactical feature extraction techniques from the field of information retrieval for log abstraction in information security. To this end, we evaluate three feature extraction techniques and three clustering algorithms on four different security datasets for anomaly detection. Results demonstrate that these techniques have a role to play for log abstraction in the form of extracting syntactic features which improves the identification of anomalous minority classes, specifically in homogeneous security datasets. 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/39752 Towards practical privacy-preserving protocols Demmler, Daniel Protecting users’ privacy in digital systems becomes more complex and challenging over time, as the amount of stored and exchanged data grows steadily and systems become increasingly involved and connected. Two techniques that try to approach this issue are the privacy-preserving protocols secure multi-party computation (MPC) and private information retrieval (PIR), which aim to enable practical computation while simultaneously keeping sensitive data private. In the dissertation [Daniel Demmler. “Towards Practical Privacy-Preserving Protocols”. Diss. Darmstadt: Technische Universität, 2018. url: http://tuprints.ulb.tu-darmstadt.de/8605/], summarized in this article, we present results showing how real-world applications can be executed in a privacy-preserving way. This is not only desired by users of such applications, but since 2018 also based on a strong legal foundation with the GDPR in the European Union, that enforces privacy protection of user data by design. 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/39747 Guest editorial: Information security methodology and replication studies Wendzel, Steffen; Caviglione, Luca; Mileva, Aleksandra; Lalande, Jean-Francois; Mazurczyk, Wojciech This special issue presents five articles that address the topic of replicability and scientific methodology in information security research, featuring two extended articles from the 2021 International Workshop on Information Security Methodology and Replication Studies (IWSMR). This special issue also comprises two distinguished dissertations. 2022-01-01T00:00:00Z