黑料传送门

Recording traces of queries sent to a database system allows to replay these traces later as an evaluation workload. Furthermore, traces can be altered and modified in order to enhance the trace-based benchmarking capabilities. The aim of this thesis is to give an overview of the state of the art of trace-based DBMS benchmarks and to explore which tools are currently available for popular open source DBMSs. Furthermore, the thesis should explore the feasibility of advanced capabilities such as altering, tailoring, and scaling traces as well as the use of machine learning techniques to augment recorded traces. This thesis project is provided in collaboration with benchANT GmbH.

Many database management systems have been heavily extended recently with vector stores and MCP support to enable agentic access. However, the performance characteristics of these new access types have not yet been studied to a sufficient extent. This includes query patterns, transactional behaviors, and data access patterns. The aim of this thesis is to address this gap, compare agentic workloads with traditional database benchmarks (e.g., TPC-C), and derive and appropriate benchmark to evaluate database systems for such agentic usage. This thesis project is provided in collaboration with benchANT GmbH.

The landscape of web technologies and architectures is constantly evolving to meet modern performance and structural demands. Understanding and innovating within these paradigms is key to building next-generation scalable web applications. If you are interested in this area and seek a potential topic, please contact me for further discussion and drafting.

Effective privacy protection in modern software systems requires bridging the gap between strong technical guarantees and actual user adoption. Developing robust Privacy-Enhancing Technologies (PETs) and leveraging Trusted Computing paradigms, such as Trusted Execution Environments (TEEs), are critical challenges for building secure, user-centric systems. If you are interested in this area and seek a potential topic, please contact me for further discussion and drafting.

Large Language Models (LLMs) are transforming many aspects of society and IT systems. Large pre-trained models demonstrate impressive problem understanding and solving capabilities and generalize to domain-specific tasks through in-context learning without further task-specific fine-tuning. This has led to a fast-evolving area of research that focuses on using LLMs to automate complex workflows like software development, system configuration and security incident response. Despite significant progress in this field, the aspects of privacy, security and reliability of such LLM-based systems often remain underexplored. Especially in high-stakes domains like network access control and healthcare or when dealing with sensitive data, reliability, security and privacy of such systems is crucial. We offer various bachelor and master thesis projects in this area of research. Specific thesis topics can be individually discussed and designed with the supervisor.

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 topic addresses the challenges of securing modern network environments through Zero Trust Network Access. Even when strong perimeter defenses are in place, the lack of true end-to-end network traffic separation still leaves systems vulnerable to lateral movement. I am interested in developing architectures that enforce strict network isolation and verification. This may also include exploring unified policy frameworks to seamlessly synchronize security rules across both the core network infrastructure and the client endpoints. If you wish to explore this topic further, I invite you to contact me and we can then further discuss the specific focus of the thesis.

State-machine replication is a concept to achieve fault tolerance. Multiple replicas execute each the same requests on behalf of clients. At the institute we have an own replication framework called SMRteez. As the setup of multiple replicas is complex and error prone, there were initial attempts for a central component that can start, stop and manage replicas from a GUI-based program. The goal of this work is a redesign of this component so that security concerns (e.g., availability of private keys) are considered and the operations are base on a REST interface that can either be operated with a command-line tool or by a web-based single-page application, i.e. a browser GUI. Exact coverage has to be discussed based on the amount of available credit points.

State-machine replication is a concept to achieve fault tolerance. There are several frameworks to support SMR-based applications. Etcd is an application implementing a so-called coordination service. It is internally build with SMR technology, but does not use an underlying framework. The task of this work is to reimplement etcd with the BFT-SMaRt/SMRteez framework. The goal is to demonstrate that the framework can handle such applications. In case of a Bachelor's thesis, in case of remaining time also in case of project work, the performance shall be compared to the original etcd implementation.

Zero-Knowledge Proofs (ZKPs) are a highly useful building block for engineering privacy-enhancing systems, yet their practical application and implementation remain notoriously complex. Recently, several mature, well-maintained software libraries have emerged to abstract this complexity and streamline the integration of ZKPs. This thesis aims to conduct a multi-faceted comparative analysis of three prominent, generalized zkSNARK frameworks: arkworks (Rust), gnark (Go), and snarkjs/circom (JavaScript/WASM). The core practical component of this thesis will be the implementation of an anonymous credential scheme across all three frameworks, using a common baseline implementation as a reference. These implementations will provide the empirical basis on which to evaluate the frameworks across qualitative dimensions, such as developer experience and quality of documentation. In addition to this qualitative analysis, the resulting implementations should undergo a rigorous performance evaluation, which will be analyzed and discussed in consideration of existing public benchmarks.

Security mechanisms in Vehicle-to-Everything (V2X) environments, such as misbehavior detection systems, generate outputs that indicate potential malicious behavior but do not directly provide a unified and interpretable trust representation. This thesis investigates methods for quantifying such outputs into subjective logic opinions that can be used by trust assessment frameworks. The focus lies on a machine learning鈥揵ased approach that learns the mapping from security mechanism outputs to belief, disbelief, and uncertainty values. The proposed method will be compared against existing quantification techniques to evaluate improvements in accuracy, robustness, and interpretability. The evaluation will be conducted using realistic V2X datasets and scenarios.

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.

State-machine replication is a concept to achieve fault tolerance. Multiple replicas execute each the same requests on behalf of clients. At the institute we have an own replication framework called SMRteez. It is equipped with a mechanism to deterministically measure some metrics in each replica so that adaptation concepts can be built on top. This work is about measuring the length of the underlying consensus protocol and dynamically adjust the local timeout when a replica is considered to be faulty. This not only allows for faster crash detection but also for faster misbehavior detection in Byzantine replicas.

This thesis aims to investigate the reliability and trustworthiness of traffic sign classifiers when subjected to occlusions. Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, this research will focus on annotating the dataset with various levels and types of occlusions to evaluate if the predictions are still trustworthy. The primary objective is to assess the performance degradation of the classifier under different occlusion scenarios and to develop strategies to enhance its robustness. This study is crucial for improving the safety and reliability of autonomous driving systems where traffic signs might be partially obscured.

This thesis will investigate methods for detecting natural adversarial examples against ImageNet classifiers using classic computer vision techniques. Adversarial examples are inputs to machine learning models that are designed to cause the model to make a mistake. This project will utilize the Harder ImageNet Test Set (https://arxiv.org/abs/1907.07174) as an dataset for Natural Adversarial Examples. The primary objective is to explore and compare the effectiveness of traditional computer vision methods, such as histograms and SIFT (Scale-Invariant Feature Transform), in identifying these adversarial examples. The outcome of this research will enhance our understanding of model vulnerabilities and contribute to developing more robust machine learning systems.

This thesis will explore the effectiveness of different optimization strategies in the generation of adversarial patches. Adversarial patches are small, intentionally designed perturbations that can cause machine learning models, particularly in computer vision, to misclassify inputs. The primary objective of this research is to compare various optimization techniques, such as gradient-based methods, evolutionary algorithms, and reinforcement learning, to determine which methods are most effective and efficient in creating these patches. The outcome of this research could significantly enhance our understanding of model vulnerabilities and contribute to the development of more robust machine learning systems.

This thesis aims to investigate the reliability and trustworthiness of traffic sign classifiers when subjected to occlusions. Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, this research will focus on annotating the dataset with various levels and types of occlusions to evaluate if the predictions are still trustworthy. The primary objective is to assess the performance degradation of the classifier under different occlusion scenarios and to develop strategies to enhance its robustness. This study is crucial for improving the safety and reliability of autonomous driving systems where traffic signs might be partially obscured.

This thesis will investigate methods for detecting natural adversarial examples against ImageNet classifiers using classic computer vision techniques. Adversarial examples are inputs to machine learning models that are designed to cause the model to make a mistake. This project will utilize the Harder ImageNet Test Set (https://arxiv.org/abs/1907.07174) as an dataset for Natural Adversarial Examples. The primary objective is to explore and compare the effectiveness of traditional computer vision methods, such as histograms and SIFT (Scale-Invariant Feature Transform), in identifying these adversarial examples. The outcome of this research will enhance our understanding of model vulnerabilities and contribute to developing more robust machine learning systems.

An autonomous vehicle is equipped with a variety of sensors that produce large quantites of data which the vehicle uses to run a lot of different safety-critical functions, such as Cooperative Adaptive Cruise Control or Park Assist. In this thesis, we focus on the trust between the vehicle computer and other in-vehicle components that it relies upon to provide non-compromised data as input to different safety-critical functions. The goal of the thesis is to build a tool that will automate building of in-vehicular trust models based on a system model of a vehicle. A system model of a simplified vehicle will first need to be created by using the System Modeling Language (SysML). This model will serve as an input to the automation tool that needs to output a trust model in a pre-defined form. The methodology for building such trust models will be provided.

This thesis will explore the effectiveness of different optimization strategies in the generation of adversarial patches. Adversarial patches are small, intentionally designed perturbations that can cause machine learning models, particularly in computer vision, to misclassify inputs. The primary objective of this research is to compare various optimization techniques, such as gradient-based methods, evolutionary algorithms, and reinforcement learning, to determine which methods are most effective and efficient in creating these patches. The outcome of this research could significantly enhance our understanding of model vulnerabilities and contribute to the development of more robust machine learning systems.