Lukas Pietzschmann

This topic addresses the challenges of protecting sensitive information in open data. Even when datasets are anonymized, unintended information leakage can and does still occur. I am particularly interested in quantifying the extent of such leakage and developing privacy-preserving measures to mitigate it. This may also include exploring static code analysis techniques or machine learning approaches to identify potential privacy risks in datasets. If this sounds interesting to you, get in touch with me and we can then further discuss the specific focus and scope of the thesis.

This thesis will take a quantitative look into statistical practices in empirical research. By analyzing the code that is submitted with preprints on the common preprint server PsyArXiv, we will identify common statistical pipelines and practices. This will allow us to gain insights into common patters and potential pitfalls, which can then aid future research in developing better tools and guidelines for statistical analysis.

This thesis explores data protection and information security within psychological research. It evaluates whether current ethical guidelines adequately address digital security risks and if psychology researchers have the necessary knowledge to protect sensitive participant data. By combining a systematic analysis with an empirical survey, the thesis aims to identify gaps between these standards and the researchers' knowledge on how to implement them in their workflows.

Open science repositories continuously aggregate anonymized data, creating cumulative privacy risks over time. These risks are often evaluated using record linkage: the process of identifying and matching records that belong to the same individual across different datasets. While deterministic linkage attacks establish a baseline for these vulnerabilities, they typically underestimate the true risk because they drop matches with slight data inconsistencies. To address this, this thesis or project will introduce probabilistic similarity scoring to handle messy, heterogeneous quasi-identifiers. By evaluating this approach against the PsyArXiv dataset collection, this research aims to provide a more realistic assessment of re-identification risks in real-world open research data.

Flowpipeline is an open-source tool for processing network flow data in order to monitor and analyze traffic. It consists of many different segments that can freely be combined with each other to form a processing pipeline. Currently, Flowpipeline is lacking good testing infrastructure. While some segments are individually tested by unit tests, integration tests for the entire pipeline are missing. The goal of this project is to design and implement a testing strategy for Flowpipeline that allows to automatically verify the correct functionality of the entire pipeline. This also can include automated performance tests that monitor common metrics for commin configurations.

Zero Trust Architectures have recently attracted a lot of interest in the network community. However, access control is often not extending into client devices. In this paper, we propose an extension of Zero Trust Policy Enforcement Points that integrates a device agent to expand the zero trust security model to client devices. We have developed a generalized framework that integrates with multiple compartmentalization technologies, ensuring the isolation of processes and enforcement of network policies while maintaining application and user authentication. This approach minimizes the attack surface of malicious processes, as our Zero Trust Device Agent manages compartment lifecycles based on their behaviour within the network and integrates into the global access control framework, thereby improving the overall security of zero trust architectures.

Context The R programming language has a huge and active community, especially in the area of statistical computing. Its interpreted nature allows for several interesting constructs, like the manipulation of functions at run-time, that hinder the static analysis of R programs. At the same time, there is a lack of existing research regarding how these features, or even the R language as a whole are used in practice. Objective In this paper, we conduct a large-scale, static analysis of more than 50 million lines of real-world R programs and packages to identify their characteristics and the features that are actually used. Moreover, we compare the similarities and differences between the scripts of R users and the implementations of package authors. We provide insights for static analysis tools like the lintr package as well as potential interpreter optimizations and uncover areas for future research. Method We analyze 4 230 R scripts submitted alongside publications and the sources of 19 450 CRAN packages for over 350 000 R files, collecting and summarizing quantitative information for features of interest. Results We find a high frequency of name-based indexing operations, assignments, and loops, but a low frequency for most of R's reflective functions. Furthermore, we find neither testing functions nor many calls to R's foreign function interface (FFI) in the publication submissions. Conclusion R scripts and package sources differ, for example, in their size, the way they include other packages, and their usage of R's reflective capabilities. We provide features that are used frequently and should be prioritized by static analysis tools, like operator assignments, function calls, and certain reflective functions like load.