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Optimizing Database Joins: Cost Models and Benchmarking for CPU and GPU Systems

Matušovič, Marko (author)

Optimizing SQL query execution through effective cost models is a critical challenge in database management systems (DBMS). This thesis introduces a modular benchmarking system for cost models, with a pluggable architecture for both cost models and execution engines, enabling comprehensive benchmarking across various scenarios. Accompanied by a...

master thesis 2024

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Optimised Private Set Intersection for Vertical Federated Tree Models

Li, Martin (author)

In recent years, the rapid advancements in big data, machine learning, and artificial intelligence have led to a corresponding rise in privacy concerns. One of the solutions to address these concerns is federated learning. In this thesis, we will look at the setting of vertical federated learning based on tree models. We have built a system that...

master thesis 2024

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Integrating Blockchain Technology with Project Management System in the AEC Sector

YU, HAIHAN (author)

Since its emergence in 2008, blockchain technology has significantly expanded its scope, impacting various industries beyond its initial cryptocurrency applications. Its potential to enhance established practices is increasingly recognized, yet its application in the Architecture, Engineering, and Construction (AEC) industry has been relatively...

master thesis 2024

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Searchable Symmetric Encryption Attacks: More power with more knowledge

Ho, Björn (author)

A searchable symmetric encryption (SSE) scheme allows a user to securely perform a keyword search on an encrypted database. This search capability is useful but comes with the price of unintentional information leakage. An attacker abuses leakage to steal confidential information by launching SSE attacks. In this work, our goal is to design a...

master thesis 2023

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Unmasking the Power of Trigger Intensity in Federated Learning: Exploring Trigger Intensities in Backdoor Attacks

Shokri Kalisa, Armin (author)

Federated learning allows a multitude of contributors to collaboratively build a deep learning model, all while keeping their individual training data private from one another. However, it is not immune to security flaws such as backdoor attacks in which malevolent adversaries manipulate the global model to trigger specific behaviors. In this...

master thesis 2023

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TabVFL: Improving Latent Representation Learning in Vertical Federated Learning

Rashad, Mohamed (author)

Autoencoders are popular neural networks that are able to compress high dimensional data to extract relevant latent information. TabNet is a state-of-the-art neural network model designed for tabular data that utilizes an autoencoder architecture for training. Vertical Federated Learning (VFL) is an emerging distributed machine learning paradigm...

master thesis 2023

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Online state migration in modern stream processing engines

Veneti, Theodoros (author)

Stream Processing Engines (SPEs) are called upon to help solve problems around big and volatile data, while satisfying the needs for near real-time processing. In order for such systems to be considered effective solutions to such problems at scale, efficient elasticity and non dataflow-disturbing reconfiguration operations within are a...

master thesis 2023

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Unlocking the Potential of Document Recovery in Injection Attacks against SSE: Inject Less, Recover More

Zhang, Manning (author)

Searchable symmetric encryption (SSE) is an encryption scheme that allows a single user to perform searches over an encrypted dataset. The advent of dynamic SSE has further enhanced this scheme by enabling updates to the encrypted dataset, such as insertions and deletions. In dynamic SSE, attackers have employed file injection attacks, initially...

master thesis 2023

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Multi-server Asynchronous Federated Learning

Zuo, Yuncong (author)

In federated learning systems, a server maintains a global model trained by a set of clients based on their local datasets. Conventional synchronous FL systems are very sensitive to system heterogeneity since the server needs to wait for the slowest clients in each round. Asynchronous fl partially addresses this bottleneck by dealing with...

master thesis 2023

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Impact of replacing TCP by QUIC in Tor on website fingerprinting resistance

Trap, Cyril (author)

Privacy is a human right, yet, people’s behavior on the web is constantly tracked. Tor, an anonymity network, is an effective defence against tracking. However, Tor’s multiplexing of logically independent data streams into a single TCP connection causes issues. Tor with QUIC has been implemented as an alternative with better performance but it...

master thesis 2023

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Blockchain-Based Verifiable and Privacy-Preserving Machine Learning Inference

Samardžić, Mariana (author)

The Machine Learning (ML) technology has taken the world by storm since it equipped the machines with previously unimaginable decision-making capabilities. However, building powerful ML models is not an easy task, but the demand for their utilization in different industries and areas of expertise is high. This was recognized by entities that...

master thesis 2023

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Combining SAT solvers with heuristic ideas for solving RCPSP with logical constraints: An exploration of variable ordering heuristics impact on solving RCPSP-log

Tudor, Iarina (author)

This paper provides a novel method of solving the resource-constrained project scheduling problem (RCPSP) with logical constraints (RCPSP-log) using satisfiability (SAT) solving and integrating variable selection heuristics. The extension provides two additional precedences: OR constraints and bidirectional (BI) relations, making it possible to...

bachelor thesis 2023

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Augmenting VSIDS heuristic for the RCPSP/t by initializing activity values using domain-specific information

Lenssen, Tijs (author)

The Variable State Independent Decaying Sum (VSIDS) heuristic is one of the most effective variable selection heuristics for Conflict-Driven Clause-Learning (CDCL) SAT solvers. It works by keeping track of the activity values for each variable, which get bumped and decayed based on conflict analysis. These activity values usually start out...

bachelor thesis 2023

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Why Midas would be a terrible secretary: Using a greedy approach to enhance SAT for the Preemptive Resource-Constrained project scheduling problem with set up time

Hellouin de Ménibus, George (author)

This paper presents a new greedy heuristic to extend SAT Solvers when solving the Preemptive resource-constrained project scheduling problem (PRCPSP-ST). The heuristic uses domain-specific knowledge to generate a fixed order of variable selection. We also extend previous work into encoding PRCPSP-ST by providing an alternative upper bound. The...

bachelor thesis 2023

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Combinatorial optimization for job sequencing with one common and multiple secondary resources by using a SAT solver augmented with a domain-specific heuristic

Pugatšov, Artjom (author)

This paper solves job sequencing with one common and multiple secondary resources (JSOCMSR) problem by encoding it as a Boolean satisfiability (SAT) problem and applying domain-specific heuristics to improve the SAT solver’s performance. JSOCMSR problem is an NP-hard scheduling problem where each job utilizes two resources: a shared resource and...

bachelor thesis 2023

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Integration of variable selection heuristics into a MaxSAT solver for solving the multi-mode resource-constrained project scheduling problem

Tsvetkov, Denis (author)

The multi-mode resource-constrained project scheduling problem (MRCPSP) is an extension of the resource-constrained project scheduling problem (RCPSP), which allows activities to be executed in multiple modes. The state-of-the-art solutions for solving this NP-Hard problem are dedicated algorithms and (meta-)heuristics. However, this paper...

bachelor thesis 2023

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Using Personalized Federated Learning to train Diffusion Models

HADJIGEORGIOU, MARIOS (author)

Federated Learning (FL) is widely favoured in the training of machine learning models due to its privacy-preserving and data diversity benefits. In this research paper, we investigate an extension of FL referred to as Personalized Federated Learning (PFL) for the purpose of training diffusion models. We explore the personalization technique of...

bachelor thesis 2023

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Bug Detection in Distributed Systems with Platform-independent Fault Injection: A Case Study at Adyen

Dekker, Nick (author)

Fault injection has been a long-standing technique for testing software. Injecting faults into a system, either in production or development environments, offers unique opportunities to discover bugs that are difficult to reproduce using conventional testing methods. However, it is widely considered to have a high implementation threshold. Due...

master thesis 2023

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An experimental evaluation of auto-scaling techniques for distributed stream processing systems

Kanis, Job (author)

The introduction of cloud hosting has made it possible to elastically provision distributed stream processing systems (SPEs). By dynamically scaling the different operators of the system, resource consumption can be minimised while meeting the system service-level objectives. In the literature, many different auto-scaling techniques are proposed...

master thesis 2023

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Attacks on Searchable Symmetric Encryption Systems: Revisiting Similar-data and File Injection Attacks

Ilbaş, Hakan (author)

The amount of data individuals create keeps increasing every year to the point that the data cannot be stored on a single device anymore. Cloud storage provides a solution for this problem, but not everybody wants the cloud storage service providers to peek at their data and they thus encrypt their data before storing it on the service provider...

master thesis 2022