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.

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.

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.

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.

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.

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.

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.

Web3Recommend is a decentralized Social Recommender System implementation that enables Web3 Platforms on Android to generate recommendations that balance trust and relevance. Generating recommendations in decentralized networks is a non-trivial problem because these networks lack a global perspective due to the absence of a central authority. Further, decentralized networks are prone to Sybil Attacks in which a single malicious user can generate multiple fake or "Sybil" identities. Web3Recommend relies on a novel graph-based content recommendation design inspired by GraphJet, a recommendation system used in Twitter enhanced with MeritRank, a decentralized reputation scheme that provides Sybil-resistance to the system. By adding MeritRank's decay parameters to the vanilla Social Recommender Systems’ personalized SALSA graph algorithm, we can provide theoretical guarantees against Sybil Attacks in the generated recommendations. Similar to GraphJet, we focus on generating real-time recommendations by only acting on recent interactions in the social network, allowing us to cater temporally contextual recommendations while keeping a tight bound on the memory usage in resource-constrained devices, allowing for a seamless user experience. As a proof-of-concept, we integrate our system with MusicDAO, an open-source Web3 music-sharing platform, to generate personalized, real-time recommendations. Thus, we provide the first Sybil-resistant Social Recommender System, allowing real-time recommendations beyond classic user-based collaborative filtering. The system is also rigorously tested with extensive unit and integration tests. Further, our experiments demonstrate the trust-relevance balance of recommendations against multiple adversarial strategies in a test network generated using data from real music platforms.

The European Commission is developing a European Digital Identity (EDI), which will enable a trustworthy digital proof of identity for its citizens. We present a proof of identity in combination with cryptographic evidence of the natural person being authorized to act on behalf of a legal entity. Our study achieves this by connecting users of our system with trusted issuers, the European Blockchain Services Infrastructure (EBSI), and verifiers. Accordingly, we provide a zero-trust architecture for legal entity representation, making trust portable by providing irrefutable proof of a natural person acting as a legal representative of an organization. Our zerotrust architecture aims to change how we represent legal entities and delegate authorizations with powers of attorney (PoA) as a legal primitive, making trust portable and secure. With government assistance, we conducted a pilot deployment of our prototype with live connectivity to the legal source of truth, the Kamer van Koophandel (KVK). In our pilot, a commercial business-only retailer acted as the verifier of the PoA. We shorten legally binding delegation that is cross-border, decentralized, verifiable, and revokable from a week-long process to mere seconds.

Recent years have seen an increasing interest in stablecoins from major corporate and governmental parties. The European Central Bank is investigating the possibility of introducing its own Central Bank Digital Currency. The desired features of such a currency are under discussion. One such feature is offline spending: the ability to use the currency without an internet connection, like cash. This thesis describes a token-based transaction prototype and its implementation on the Kotlin-IPv8 protocol stack. The prototype allows funds to be spent in an offline setting and provides retroactive fraud detection. The prototype is not intended for deployment but instead serves as a trial for building digital currencies on Kotlin-IPv8. The included performance analysis demonstrates that various facets of Kotlin-IPv8 perform suboptimally, of which most notably its UDP data throughput.

Ranking algorithms in traditional search engines are powered by enormous training data sets that are meticulously engineered and curated by a centralized entity. Decentralized peer-to-peer (p2p) networks such as torrenting applications and Web3 protocols deliberately eschew centralized databases and computational architectures when designing services and features. As such, robust search-and-rank algorithms designed for such domains must be engineered specifically for decentralized networks, and must be lightweight enough to operate on consumer-grade personal devices such as a smartphone or laptop computer. We introduce G-Rank, an unsupervised ranking algorithm designed exclusively for decentralized networks. We demonstrate that accurate, relevant ranking results can be achieved in fully decentralized networks without any centralized data aggregation, feature engineering, or model training. Furthermore, we show that such results are obtainable with minimal data preprocessing and computational overhead, and can still return highly relevant results even when a user’s device is disconnected from the network. G-Rank is highly modular in design, is not limited to categorical data, and can be implemented in a variety of domains with minimal modification. The results herein show that unsupervised ranking models designed for decentralized p2p networks are not only viable, but worthy of further research.

The European Union is on course for introducing a European Digital Identity that will be available to all EU citizens and businesses. This will have a huge impact on how citizens and businesses interact online. Big Tech companies currently dictate how digital identities are used. As a result they have amassed huge amounts of private user data. Movements like Self-Sovereign Identity aim to give users control over their online identity. TrustVault is the first data wallet that gives users back control not only of their identity, but all their data. TrustVault allows users to store all their data on their smartphone and control who they share it with. The user has fine-grained access control based on verifiable user attributes. EBSI connects TrustVault to the European Self-Sovereign Identity Framework allowing users to use Verifiable Credentials from public and private institutions in their access control policies. The system is serverless and has no Trusted Third Parties. TrustVault replaces the for-profit infrastructure of Big Tech with a public and transparent platform for innovation.

Automatically deriving 3D representations of buildings is a challenging problem which is at the base of a wide range of applications. The DE-RISC project aims to generate a 3D model of the entire city of Rotterdam in The Netherlands, enabling many of these applications. Generating a 3D model of a building can be done in a variety of ways, of which only few are robust, scalable and generalizable. Recognition and reconstruction of architectural floor plans is such a scalable method long researched in literature.
Although these methods generalized poorly initially, recent breakthroughs in computer vision have allowed for the application of deep learning based approaches. Recent floor plan processing methods have shown promising results on single-unit floor plans. Single-unit floor plans are floor plans of single apartments of relatively low complexity. In contrast, multi-unit floor plans describe entire buildings, and are thus significantly larger and of higher complexity. Applying single-unit floor plan processing methods to multi-unit floor plans is not trivial, and results in insufficient accuracy. These methods can therefore not be applied to an entire city, limiting the scalability and generalizability.
This thesis proposes a novel multi-scale floor plan recognition and reconstruction method designed to transform floor plans of arbitrary size into their 3D representations. As no multi-unit floor plan datasets exists, a novel floor plan dataset MURF is presented based on multi-unit floor plans from buildings in Rotterdam. MURF considers seven boundary and opening semantic classes, each with distinct physical properties. The recognition part of the method relies on an FCN employing multi-scale skip-connections, an attention mechanism, and a multi-task training objective to reinforce the learning of multi-scale features. The reconstruction part refines predictions from the recognition step by applying post-processing, vectorization, and visualization in Blender.
The proposed method is compared to floor plan processing models from literature and general stateof-the-art segmentation models by a quantitative and qualitative evaluation. Experimental results show that the proposed method is significantly outperforms existing floor plan processing methods, and performs best out of general segmentation models. A case study on the EMC in Rotterdam demonstrates the generalizability of the proposed method.

A movement for a more transparent and decentralized Internet is globally attracting more attention. People are becoming more privacy-aware of their online identities and data. The Internet is constantly evolving. Web2 focused on companies that provide services in exchange for personal user data. Web3 commits to user-centricity using decentralization and zero-server architectures. The current digital society demands a global change to empower citizens and take back control. Citizens are locked into big- tech for personal data storage and their for-profit digital identity. Protection of data has proven to be essential, especially due to increased home Internet traffic during the COVID pandemic. Citizens do not possess their own travel documents. The Euro- pean Commission aims to transition this governmental property towards self-sovereign identity, introducing many new opportunities. Citizens are locked into banks with non- portable IBAN accounts and unsustainable legacy banking infrastructures. Migration to all-digital low-fraud infrastructures and healthier competitive ecosystems is essential.
The overall challenge is to return the power to citizens and users again. The transi- tion to a more decentralized Internet is the first crucial step in the realization of user- centricity. This thesis presents the first exploratory study that integrates governmental- issued travel documents into a (decentralized) societal infrastructure. These self-sovereign identities form the authentic base to a private and secure transfer of money and data, and can effectively provide trust in authenticity that is currently missing in online con- versations. A fully operational zero-server infrastructure that incorporates all our re- quirements has been developed for Android using the P2P network overlay IPv8 (Tribler, 2021), and a personalized blockchain called TrustChain (Otte et al., 2020). It contributes to a reformed tech and financial sector that is more efficient and effective in serving the wider economy, and more resistant to bad behavior of all kinds. Creating such an in- frastructure that is decentralized and anti-fragile is deemed crucial for the future.

CRDTs are data structures that allow conflict free replication and modification. In theory, CRDTs seem like a natural fit for open P2P networks, however there are obstacles to overcome. Firstly, many proposed CRDTs are grow-only because (i) CRDTs may track deletions in permanent tombstone values or (ii) they may gather permanent information on every peer in the system. As such, CRDTs are not well adapted to open P2P networks. Many peers may come and go over time and persistent accurate information about all peers will grow too large eventually. Secondly, some types of CRDT (mainly op-based CmRDT) require causal message delivery which is hard in open P2P networks. Thirdly, CRDTs are typically built with the assumption that all peers need all data in a replica and thus all data is fully replicated, even though a client may only be interested in a small subset.

To address these issues a new state-based CvRDT is proposed: BloomCRDT, which is a variation on the OR-set that replaces its 푅 grow-only set with Bloom filters. It does not need knowledge of other peers or their state and avoids tombstones. This makes BloomCRDT well suited for use in open P2P networks. The grow-only aspect is vastly reduced compared to the standard OR-set. However, the BloomCRDT itself is indivisible and can grow to be too large if it stores many elements. This limit is not high enough to accommodate demanding applications. Scalability can be dramatically improved by combining multiple BloomCRDTs to form a Conflict Free R-Tree (CFRT). Each node of the R-Tree is represented by a BloomCRDT. Concurrent modifications are allowed and, due to the characteristics of the R-tree, this results in a consistent data structure with overlapping ranges. Since an R-Tree’s efficiency is reduced when ranges overlap, a periodic optimization algorithm can be used to eliminate the overlapping ranges.

A proof-of-concept implementation was made of BloomCRDT and CFRT using Python and PyIpv8. Experiments where performed using the Gumby experimental framework and the DAS5 cluster. The experiments show that BloomCRDT performs as designed: it keeps a smaller state than other solutions while being invariant to the number of identities that have interacted with a BloomCRDT instance. The CFRT is shown to be able to balance key/value entries over multiple BloomCRDT instances using favorable messaging metrics. Moreover the CFRT, and by extension BloomCRDT, tolerate network faults and can work with up to 90% message loss. A final experiment uses real world data to compare the CFRT to Tribler’s channels implementation, and shows that even at scale a CFRT can outperform the current channels implementation by orders of magnitude in terms of user responsiveness.

This research has been performed in pursuit of the MSc Computer Science at Delft University of Technology in collaboration with the Dutch National Office for Identity Data (RvIG), part of the Dutch Ministry of the Interior and Kingdom Relations. Self-Sovereign Identity (SSI) is a relatively new concept part of a movement aspiring to create a universal identity layer for the Internet. SSI aims to put the citizen at the centre of their data, making them the sovereign over their digital presence. Wherein the current ecosystem personal information is stored in centralised or federated settings, SSI delegates this responsibility entirely to the user. Functioning SSI schemes have been proposed and deployed, even with governmental support. However, we identify that the key issue that remains to be solved is revocation: the invalidation of credentials. Proposed revocation mechanisms typically rely on centralised infrastructure for revocations, defying the principles of SSI itself and, furthermore, lack offline verification capabilities. This research addresses these issues and proposes the first fully distributed revocation mechanism in SSI, using a gossip-based propagation algorithm. Our revocation mechanism requires no centralised infrastructure or strict network requirements and enables offline verification of credentials in case of disaster. Propagation is handled by honest clients, requires no direct communication with authorities and is shown to be robust in case of unreliable communication links. Furthermore, revocation acceptance is at the discretion of individual clients, making our mechanism fully adhere to the principles of Self-Sovereignty. This revocation and verification structure is part of our Industry-Grade Self-Sovereign Identity (IG-SSI) architecture. IG-SSI is a purely academic fully distributed SSI scheme with intrinsic equality across the network. Furthermore, communication is facilitated peer-to-peer, requiring no specialised infrastructure. The architecture allows for the signing, verification and presentation of credentials using Zero-Knowledge Proofs. We believe that the characteristics of our system provide it with use for decades to come, hence, we deem it to be industry-grade. Our simulation portrays that a network comprised of 10,000 clients gossips 1 million revocations within 25 seconds. Feasibility on smartphones is shown through a government-backed real-life trial. Based on our results, we claim that IG-SSI is a viable candidate for facilitating the needs for a digital identity of the European Union.

After the release of Bitcoin in 2008 many advances have been made in the crypto space. The feature-set, scalability, transaction cost, and efficiency have all improved. Yet, as the development continued, the goal of Bitcoin or any other cryptocurrency as a digital payment form started to take a backseat to their utility as a store of value and investment vehicle. Additionally, cryptocurrencies are missing the crutial features of scalability in transactions per second and basic price stability. This calls into question whether fully decentralized currencies can ever serve as a global payment system.

Meanwhile, the world of payments outside the crypto-sphere is going through a transformation. With the use of physical money declining in favor of privately banked money, payment systems are going digital. Yet, banks are being found unreliable, unavailable to many people across the world, and unable to match the convenience and feature set of new payment providers. With decentralized cryptocurrencies not sufficing as payment solutions, centrally controlled currencies like Tether and Diem (previously Libra) are trying to fill the exact hole in the market left by banks. Consequently, the global digital payment system may eventually belong to the first bidder, be they decentralized, run by governments, or by private, profit driven corporations.

We present EuroToken, a design for a CBDC that is focused on solving the open issues in cryptocurrencies from the perspective of a trustworthy central bank. By centralising some infrastructure required to run a cryptographically secure currency, we achieve a network that can scale to arbitrary size, handle offline transactions, and remain price-stable and guaranteed in value by the Central Bank.

The EuroToken system is a Peer-to-Peer blockchain system that stores every wallets' history on the users personal device. This allows basic transactions without an online connection. Double-spend prevention is handled by a set of centralised nodes that periodically verify the transactions of a given user and condenses their chain into a single "checkpoint" block for scalability. In order to create price stability, we attach EuroToken to the IBAN system and enable a 1:1 exchange in either direction.

We present a working implementation of EuroToken including a wallet and an IBAN / EuroToken exchange. We also show how our system can grow to an arbitrarily large scale without sacrificing efficiency.