In today’s networks, the frequency of distributed cyber attacks made centrally based SIEM solutions vulnerable to bottlenecks, privacy invasions, and single points of failure. This thesis proposes a decentralized anomaly detection platform for autonomous agents, every server visualized as a node, operating independently without centralized control to identify coordinated attacks with a focus on compliance in the European Blockchain Services Infrastructure (EBSI). We construct an evolving subnet adjacency graph over destination IPs and use Deep Graph Infomax (DGI) to learn highquality node embeddings that capture local traffic patterns as well as global information. The fine-tuned online embeddings are concatenated with raw flow features and fed into a universal LSTM-augmented reinforcement learning policy. The LSTM temporal memory enables both sudden and slow, sneaky attacks. Evaluation metrics ( ROC-AUC, F1, Accuracy, Precision and Recall ) are computed at two granularity levels: per-agent for each node detection performance and system-level using a ”union-of-alerts” decision rule for event-level detection. Experiments on the UNSW-NB15 flow data set demonstrate that our approach improves ROC-AUC from ≈ 0.936 to ≈ 0.95 and F1 from ≈ 0.89 to ≈ 0.913, outperforming the independent PPOLSTM baseline. The system preserves data privacy, only exposing aggregated scores of anomalies. These results suggest that integrating self-supervised graph embeddings with recurrent multiagent RL produces a robust, scalable, and privacy-preserving SIEM solution tailored for the EBSI federated environment. Further studies on window granularity, hyperparameters, and per-subnet policy specialization could potentially further validate design choices and offer a roadmap for deploying decentralized network defense systems.

The digitization of democratic processes promises greater accessibility but presents challenges in terms of security, privacy, and verifiability. Existing electronic voting systems often rely on centralized architectures, creating single points of failure and forcing too much trust in authorities, which contradicts democratic principles. This thesis addresses the challenge of creating a secure, private e-voting system with minimized trust dependencies designed for the most versatile personal device: the smartphone. We introduce SmartphoneDemocracy, a novel e-voting protocol that combines three key technologies: the emerging European Digital Identity (EUDI) Wallet for Sybil-resistant identity verification, Zero-Knowledge Proofs for privacy-preserving validation, and a peer-to-peer blockchain (TrustChain) for a resilient, serverless public bulletin board. Our protocol enables voters to register and cast ballots anonymously and verifiably directly from their smartphones. We provide a detailed protocol design, a security analysis against a defined threat model, and a performance evaluation demonstrating that the computational and network overhead is feasible for medium- to large-scale elections. By developing and prototyping this system, we demonstrate a viable path to empower citizens with a trustworthy, accessible, and user-controlled digital voting experience.

Mobile blockchain transactions require low latency
for practical deployment. This paper presents a
smartphone-native Trustchain system developed as
part of a collaborative research project where five
students implement Trustchain from scratch, each
optimizing a different performance metric: latency,
robustness, storage, throughput, and battery effi-
ciency. This work focuses on latency optimiza-
tion through a novel modular architecture imple-
mented in Rust/Kotlin that enables runtime proto-
col switching—the first such capability in mobile
blockchain implementations.
The evaluation compares two transport layers: a
lightweight UDP implementation and a business-
grade P2P solution (Iroh). Testing on Android de-
vices demonstrates end-to-end round-trip laten-
cies from 11.8 ms under optimal conditions to
240 ms at extreme loads (500 MPS), with UDP
achieving consistent 11.8ms median latency and
Iroh showing 18.2ms for typical payloads. Results
show clear trade-offs between protocol complexity
and performance, providing guidance for selecting
transport mechanisms in different mobile deploy-
ment scenarios. All code is released open-source to
the Tribler project for reproducibility.

TrustChain is a scalable, lightweight blockchain architecture that avoids global consensus by maintaining a personal chain of co-signed interactions for each peer, forming a directed acyclic graph. While its structure makes it promising for mobile and resource constrained environments, its behavior under real world conditions, such as the time it takes to restore connectivity, remains underexplored. This paper presents an implementation of TrustChain in Rust for Android mobile devices and evaluates its robustness under network disconnections across two protocols: UDP and Iroh over QUIC. Robustness is defined as the time elapsed between network connectivity restoration and the successful exchange of the first valid TrustChain message between peers. Experiments involved controlled Wi-Fi interruptions, measuring the reconnection time for each protocol. The findings reveal trade-offs between protocol simplicity and recovery performance. UDP demonstrated consistent low-latency reconnection times between 4 and 6 seconds, averaging 5 seconds, due to its stateless nature and lack of connection recovery overhead. In contrast, Iroh reconnection times ranged from 4.5 to 11.5 seconds, with most values between 6 and 8 seconds, due to QUIC's timeout strategy, DNS-based peer discovery, and relay reconnection overhead. The results provide insights for deploying decentralized systems in mobile contexts and highlight open challenges in peer reconnection for lightweight blockchain protocols. This study also proposes improvements and directions for future work, including multi-peer evaluation, measuring different Wi-Fi congestion levels, or experimentation with other network protocol stacks.

Blockchain technologies offer decentralized, secure, and transparent transaction sys tems but face significant scalability challenges, especially in mobile and peer-to-peer environments. Directed Acyclic Graph (DAG)-based protocols, such as Trustchain, present a promising alternative by allowing each agent to maintain a personal chain of relevant transactions, thereby improving throughput and reducing resource consump tion. Despite its theoretical potential, Trustchain has not yet been effectively imple mented for smartphones. To evaluate performance, we provide an implementation over two communication protocols, focusing on throughput. We found that our QUIC-based implementation displays an over three times throughput improvement when keeping a QUIC connection open, and can confidently store at least 28 536-byte blocks per sec ond. However, our UDP-based version outperforms our implementation, confidently storing at least 500 668-byte blocks per second.

Blockchain is already a widely adopted solution which can achieve decentralized storage in trustless settings. However, it is infamous for its high energy demands, making it difficult to operate on mobile phones with limited battery life. Among many design decisions in a blockchain implementation, is the choice of the communication protocol used for messaging between the participants. This work systematically evaluates the energy efficiency of custom blockchain implementations: over UDP and over QUIC with Iroh Rust crate for Android devices. The results demonstrate a higher energy demand by the QUIC-based implementation, with up to 34\% faster battery discharge on average compared to the UDP approach. Investigations show different energy usage per CPU and Wi-Fi components, and network traffic profile during the application's operation. These findings highlight the trade-offs related to energy efficiency in designing a blockchain-based system.

Smartphones offer limited storage and memory, con- straints that conventional blockchains struggle to meet, yet they are also the devices where user- owned transaction chains promise the most value. We present the first empirical study of TrustChain, a DAG-based per-peer ledger running entirely on mobile hardware. A Rust implementation and open- source benchmark evaluate how flush-interval batch- ing (k) and lossless compression affect a single node scaling from 103 to 106 blocks.
On a Galaxy S8 and Pixel-6 emulator, RAM stays below 600 MB and compressed disk use below 0.5 GB at one million blocks (128 B payload). Insert la- tency remains interactive (< 8 ms) with disk:100 flushing; moderate batching (k ≈ 500) cuts CPU load by roughly 45 % without harming durabil- ity. Lightweight compression (LZ4-1, Zstd-1) trims space by 20 to 30 % at a sub-10 ms cost, with diminishing returns at higher levels. End-to-end tests show storage is never the bottleneck, raw UDP achieving a 7 ms median RTT.
Taken together, the implementation and measure- ment dataset provide a concrete reference for deploy- ing DAG-based chains on smartphones and highlight opportunities for advancing mobile blockchain tech- nology.

We present a passport-level trust token for Europe. In an era of escalating cyber threats fueled by global competition in economic, military, and technological domains, traditional security models are proving inadequate. The rise of advanced attacks exploiting zero-day vulnerabilities, supply chain infiltration, and system interdependencies underscores the need for a paradigm shift in cybersecurity. Zero Trust Architecture (ZTA) emerges as a transformative framework that replaces implicit trust with continuous verification of identity and granular access control. This thesis introduces TrustZero, a scalable layer of zero trust security built around a universal ”trust token”- a non revocable self-sovereign identity with cryptographic signatures to enable robust, mathematically grounded trust attestations. By integrating ZTA principles with cryptography, TrustZero establishes a secure web-of-trust framework adaptable to legacy systems and inter-organizational communication.

Webs represents a n ambitious attempt to democratize access to financial instruments and reimagine economic coordination. At its core, Web3 envisions a public infrastructure where users actively contribute to and share control of the systems they depend upon. Yet the gap between this vision and today's implementations reveals deep architectural flaws.

This thesis revisits Web3's foundations from first principles. Our investigation reveals that persistent vulnerabilities like transaction manipulation, Sybil attacks, and selfish behavior stem from misguided assumptions about participant behavior and misplaced focus on consensus alone.

Trustworthy Web3 foundations must account for the fact that protocols can be forked, validators run different software versions, and participants operate under diverse incentives. These realities demand accountability and incentive alignment mechanisms beyond traditional security models.

We present a decentralised alternative to the winner-takes-everything dynamics of social media platforms.

For 25 years there have been continuous attempts to decentralise file sharing, music streaming, video conferencing, and social media. 

None of these hundreds of projects to re-decentralise the Internet have reached the uptake level of YouTube and TikTok. They are rarely easy to use.

We present DeToks, a fully decentralised alternative to Youtube and Tiktok. DeToks is not dependant on any central server or cloud. DeToks is specifically designed to be as decentralised and attack-resilient as Bitcoin and Bittorrent.

Our core contribution is effortless 5G Network Address Translators (NATs) puncturing.

Direct phone-to-phone communication is not available on today's smartphones. DeToks solves this problem.

NATs and carrier-grade NATs block direct communication between smartphones. 

We procured 30+ SIMs card on European 4G/5G mobile networks and measured the carrier-grade NATs behavior. We determined the NAT types (full cone,restricted,symmetric) and their time-out settings. By leveraging provider-aware (Vodafone,Orange,Telia, etc.) NAT puncturing strategies we create direct UDP-based phone-to-phone connectivity. 

We utilise parallelism by opening at least 500 Internet datagram sockets on two devices. By relying on provider-aware IPv4 range allocations, provider-aware port prediction heuristics, high bandwidth probing, and the birthday paradox we can successfully bypass even symmetric NATs. Our communication method achieves peer-to-peer 5G connectivity at the cost of merely some initial delay and bandwidth, without any assistance from third party servers or clouds.

Detoks validates our 5G puncturing work. We demonstrate the feasibility of fully decentralized social media platforms on consumer mobile devices.

Traditional search engines rely on centralized databases and powerful servers to process and retrieve information. Developing alternatives to key-value search engine databases in distributed computing environments is a significant challenge, particularly when dealing with limited computational resources. This study explores the use of large language models (LLMs) to address this problem. We focus on environments with constrained computing power, such as mobile devices, to investigate the feasibility of using LLMs as a localized search solution. Through experiments with the state-of-the-art LLMs BERT and T5, we demonstrate their ability to memorize and retrieve unstructured data, specifically YouTube video IDs, based on partial information derived from video titles or tags. Our results show that the explored models can achieve 100\% precision and recall when retrieving 48266 video IDs. The findings suggest that LLMs have the potential to effectively function as a search engine database, offering semantic search capabilities while operating within the constraints of limited computational resources.

Current digital payment solutions are fragile and offer less privacy than traditional cash. Their critical dependency on an online service used to perform and validate transactions makes them void if this service is unreachable. Moreover, no transaction can be executed during server malfunctions or power outages. Due to climate change, the likelihood of extreme weather increases. As extreme weather is a major cause of power outages, the frequency of power outages is expected to increase. The lack of privacy is an inherent result of their account-based design or the use of a public ledger. The critical dependency and lack of privacy can be resolved with a Central Bank Digital Currency that can be used offline. This thesis proposes a design and a first implementation for an offline-first digital euro. The protocol offers complete privacy during transactions using zero-knowledge proofs. Furthermore, transactions can be executed offline without third parties and retroactive double-spending detection is facilitated. To protect the users’ privacy, but also guard against money laundering, we have added the following privacy-guarding mechanism. The bank and trusted third parties for law enforcement must collaborate to decrypt transactions, revealing the digital pseudonym used in the transaction. Importantly, the transaction can be decrypted without decrypting prior transactions attached to the
digital euro. The protocol has a working initial implementation showcasing its usability and demonstrating functionality.

Blockchain-based payment systems typically assume a synchronous communication network and a limited workload to confirm transactions within a bounded timeframe. These assumptions make such systems less effective in scenarios where reliable network access is not guaranteed.
Offline payment systems are designed to allow users to register offline transactions and ensure their execution once network access is restored. However, these methods often rely on specialized hardware to remain secure and tamper-proof, preventing double-spending and invalid transactions.

In this work, we introduce a novel offline payment system that does not rely on trusted hardware. Our system enables users to determine transaction fees and accept offline payments based on the trustworthiness of their peers. Users can decide whether to accept offline payments from each other based on the information our system provides.

Our system employs a smart contract to manage transactions on a blockchain for its online component. We implemented the online part of our system as an Ethereum Solidity smart contract and deployed it on the Sepolia testnet. Our performance evaluation demonstrates our system’s practicality, handling 68 Transactions Per Second (TPS) with a confirmation latency of 12 seconds on the Sepolia testnet. The system’s scalability can improve with advanced layer-1 or layer-2 blockchain solutions, enhancing throughput, decreasing latency, and reducing fees.

Decentralized learning (DL) leverages edge devices for collaborative model training while enhancing data privacy, as training data never leaves the device. In this paper, we present "BeyondFederated: Truly Decentralised Edge AI," a novel approach to overcoming the limitations of traditional Federated Learning (FL) by eliminating reliance on centralized servers. Traditional FL, despite its decentralization benefits, still poses significant privacy and security risks due to its central control points. Our system leverages edge AI with TensorFlow Lite and a peer-to-peer gossip network to ensure fully decentralized learning and data processing. We developed a proof-of-concept to demonstrate a decentralized alternative to Spotify, incorporating Web3 YouTube playback, and enabling efficient and accurate Scalable Nearest Neighbor (ScaNN) searches using vector embeddings incorporated into an actual TensorFlow model running on edge devices. Through various experiments, we evaluated the system's performance in real-time search capabilities, embedding model comparisons, and new data insertion handling. The results confirm that BeyondFederated maintains high efficiency, scalability, and privacy. This study underscores the potential for truly decentralized machine learning and sets the stage for more robust and user-centric decentralized AI applications.

The digital world is evolving toward representing - and serving the interconnection of - natural persons. Instead of depending on the intrastructure of Big Tech companies and governments, users can cooperate and use their hardware to form public infrastructure. Instead of existing by virtue of a reference in some institution's database, users can interact based on a digital representation of their own choosing. It is no longer sufficient to depend on users to act out of system-imposed altruism. A new digital world is emerging that aims to provide systems that respect the rights of users to control their own digital representation. The complete control over one's own representation and all the data that belongs to it is what we know as Self-Sovereignty.

Solutions for digital Self-Sovereignty are wildly sought after, though their solution space remains woefully underexplored. Numerous global entities, e.g., the European Union, have stated their support for Self-Sovereign systems. However, many old problems of peer-to-peer systems that have gone ignored for decennia resurge with the need for Self-Sovereignty. For example, interconnections in peer-to-peer networks are vulnerable to attacks using fake identities and attackers can manipulate peers by depriving them of data. As most deployed peer-to-peer solutions have very little incentive for disruption by attackers, we have seen very few attacks. However, cryptocurrencies have shown that these attacks do surface when there is sizable monetary gain for attackers. In order to secure our future digital society, we must define and study these systems for Self-Sovereignty.

In this thesis we take the first steps toward defining the systems that can power a Self-Sovereign "Web3" ecosystem. In particular, we explore systems that apply Self-Sovereignty for identity, for public infrastructure, and for the execution of shared code. We describe four prototype mechanisms to form a guide for future work and to derive their general properties. Each mechanism is evaluated as realistically as possible. Thereby, this thesis mostly fulfills an exploratory role to guide the further evolution of our digital world.

The introduction of Blockchain technology has led to the idea of Decentralized Autonomous Organizations.
(DAOs). DAOs enable decentralized and transparent collective wealth, allowing groups of individuals to pool their funds for collective management. DAOs rely on complex smart contracts to function. Despite Bitcoin being the most popular Blockchain, its simple smart contracts make DAO unfeasible. This thesis presents a DAO structure that addresses the challenge of DAOs on Bitcoin. The system utilizes threshold signatures to allow anyone to create Bitcoin wallets jointly controlled by a group. The system is implemented as an Android application and relies on no central party, allowing the system to be used by anyone worldwide. Our experiments show that our system is practical for real-world use for groups of 50 members. Joining an organization in the system and creating a transaction to spend funds can be done in under a minute. Further research is required to determine how far the system can scale while being practical.

Colorectal cancer is a widespread disease that significantly impacts the health of individuals worldwide. Understanding the needs and concerns of those affected by this disease is crucial for improving patient outcomes and enhancing the quality of care. Patient web forums have emerged as valuable platforms for individuals to openly share their experiences and thoughts related to colorectal cancer, providing unique insights into the social, physical and emotional aspects of their patient journey. These forums offer a more comprehensive and authentic portrayal of patient experiences compared to traditional patient data collection methods, such as questionnaires and interviews, which may not capture the full scope of patients experiences in the colorectal cancer carepath.

However, analyzing the vast amount of unstructured data within these patient web forums presents a significant challenge. Traditional manual analysis by human experts is time-consuming, labor-intensive, and limited in scalability, making it impractical to analyze the sheer volume of patient-generated content. This is where the application of natural language processing (NLP) techniques becomes crucial. NLP enables the automated processing and analysis of textual data, allowing for efficient extraction and interpretation of the large amounts of patient forum posts.

Nevertheless, relying solely on machine intelligence, such as topic modeling and natural language generation, for interpreting patient forum data carries inherent risks, including the potential for disseminating misleading information. While these machine-driven techniques offer efficient and scalable ways to analyze and generate insights from the large amount of diverse and unstructured patient forums, they may lack the necessary contextual understanding and domain expertise to ensure the accuracy, relevance, and ethical implications for interpreting colorectal cancer patient experiences.

To close this gap between human experts and machine intelligence, this thesis explores the potential of hybrid intelligence (HI) for topic interpretation in colorectal cancer research. The main research question is: ``How can topic modeling, GPT-3.5 language generation and human expertise be combined to explore the interpretation of patient web forums in colorectal cancer (CRC) research?"

To address the research question, three human studies were conducted. The first study employed NMF topic modeling to compare topic interpretations created independently by medical workers and GPT-3.5. This comparative analysis discovered unique observations that differentiate human-written and AI-generated interpretations on online patient stories. In the second study, it was investigated how medical researchers collaborate with GPT-3.5 to develop hybrid interpretations on patient experience topics generated by the BERTopic model. A Flask web application served as the interactive platform for combining their knowledge with the AI model. Finally, the third study made professional human evaluators assess the topic relevance of the interpretations generated by medical researchers and GPT-3.5 to determine whether the combination of GPT-3.5 and human expertise leads to improved topic interpretations compared to individual interpretations.

The proposed solution to the research problem is to explore a hybrid workflow that compares, combines and validates GPT-3.5 language generation and human expertise, aiming for enhanced interpretations of topics extracted from colorectal cancer patient forums. The three studies provide opportunities for researchers and medical professionals to integrate machine intelligence from topic models and GPT-3.5 in their field of work. The hybrid workflow has conclusively demonstrated that human experts were successfully able to compare and enhance the relevance of human and GPT-3.5 interpretations of colorectal cancer patient experience topics. This allowed human experts to efficiently reach a more comprehensive understanding of patient forum data, which is essential for improving patient health in colorectal cancer research.

High Performance Computing (HPC) facilities play a crucial role in acceler- ating Electronic Design Automation (EDA) procedures at NXP Semiconduct- ors. The increasing number of job requests and workloads has led to a surge in memory demand, which is a costly resource. To address this, we leverage memory swap space on disk as a more affordable extension of main memory. By employing a job submit-time parameter called memory reservation, we can increase the cluster jobs’ parallelism, and control the memory swap space usage. The primary goal of this thesis is to find the optimal cost and performance by optimizing the memory reservation parameter. To achieve this goal, we created an effective parameter optimizer that employs a one-way search method to determine the best memory reservation, focusing on scenarios with a large homogeneous job set running on an overloaded system. Our contributions include improving the memory utilization efficiency in HPC by utilizing swap space. Previous work used Machine Learning models to predict maximum memory usage and reduce over-reservation; and our work further reduces memory usage per job. The results demonstrate that the parameter optimizer effectively minimizes the cost and performance at the same time, leading to up to 43% cost saving for a job among our test cases.

Blockchain technology has allowed for the emergence of a new type of organization, the Decentralized Autonomous Organization (DAO). They have gained significant traction in recent years, reaching market capitalizations of up to 60 billion USD in 2021. These organizations coordinate economic activity by an unbounded group of people within an adversarial environment. However, despite their potential, currently deployed DAOs face notable challenges related to centralization in governance and infrastructure. This work addresses these limitations by proposing a novel architecture for a fully decentralized DAO with no compromises. We introduce a scalable governance protocol utilizing multi-signature schemes to manage shared assets effectively. To demonstrate the feasibility of our approach, we implement, deploy, and evaluate a real-world DAO called Music DAO. Music DAO serves as a compelling use case, enabling listeners to collectively invest in and listen to their favorite artists. This research represents a significant advancement in the field of decentralized organizations, with the potential to revolutionize the way people collaborate and organize themselves.

Federated learning is a privacy-enforcing machine learning technology but suffers from limited scalability. This limitation mostly originates from the internet connection and memory capacity of the central parameter server, and the complexity of the model aggregation function. Decentralized learning has recently been emerging as a promising alternative to federated learning. This novel technology eliminates the need for a central parameter server by decentralizing the model aggregation across all participating nodes. Numerous studies have been conducted on improving the resilience of federated learning against poisoning and Sybil attacks, whereas the resilience of decentralized learning remains largely unstudied. This research gap serves as the main motivator for this study, in which our objective is to improve the Sybil poisoning resilience of decentralized learning.

We present SybilWall, an innovative algorithm focused on increasing the resilience of decentralized learning against targeted Sybil poisoning attacks. By combining a Sybil-resistant aggregation function based on similarity between Sybils with a novel probabilistic gossiping mechanism, we establish a new benchmark for scalable, Sybil-resilient decentralized learning.

A comprehensive empirical evaluation demonstrated that SybilWall outperforms existing state-of-the-art solutions designed for federated learning scenarios and is the only algorithm to obtain consistent accuracy over a range of adversarial attack scenarios. We also found SybilWall to diminish the utility of creating many Sybils, as our evaluations demonstrate a higher success rate among adversaries employing fewer Sybils. Finally, we suggest a number of possible improvements to SybilWall and highlight promising future research directions.