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de With, Wim (author)
Recommender systems usually base their predictions on user-item interaction, a technique known as collaborative filtering. Vendors that utilize collaborative filtering generally exclusively use their own user-item interactions, but the accuracy of the recommendations may improve if several vendors share their data. Since user-item interaction...
master thesis 2022
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Ivaşcu, Darius (author)
The Software Defined Network (SDN) is a relatively new paradigm that aims to tackle the lack of centralization in the existing network by separating the control centre from the programming data plane. The controller keeps an overview of the structure of the whole network, which makes it vulnerable to possible topology poisoning attacks. Topology...
bachelor thesis 2022
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de Los Santos Subirats, Alex (author)
Software-Defined Networks (SDNs) are a promising new network design paradigm that allows for better control of the network. But as with any new software implementation, there are new security concerns that arise. In the past there have been various papers covering specific side-channel attacks on SDNs; most of them consist of either using time...
bachelor thesis 2022
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Durand, Dylan (author)
Software-Defined Networks are an exciting network paradigm that brings many advantages to its users. However, its architecture also makes it vulnerable to attacks. Distributed Denial of Service attacks are one of those attacks that can<br/>exploit the weaknesses of an SDN. This paper explains the weaknesses that can be exploited and the...
bachelor thesis 2022
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Schram, Gregor (author)
Machine learning has been applied to almost all fields of computer science over the past decades. The introduction of GANs allowed for new possibilities in fields of medical research and text prediction. However, these new fields work with ever more privacy-sensitive data. In order to maintain user privacy, a combination of federated learning,...
bachelor thesis 2022
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Pejić, Ignjat (author)
A Generative Adversarial Network (GAN) is a deep-learning generative model in the field of Ma- chine Learning (ML) that involves training two Neural Networks (NN) using a sizable data set. In certain fields, such as medicine, the data involved in training may be hospital patient records that are stored across different hospitals. The classic cen...
bachelor thesis 2022
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Amalan, Akash (author)
Federated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled GANs to benefit from the rich distributed training data while preserving privacy However,in a non-iid setting, current federated GAN architectures are unstable, struggling to learn the distinct features and vulnerable to mode...
bachelor thesis 2022
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Jehee, Wouter (author)
Federated learning (FL), although a major privacy improvement over centralized learning, is still vulnerable to privacy leaks. The research presented in this paper provides an analysis of the threats to FL Generative Adversarial Networks. Furthermore, an implementation is provided to better protect the data of the participants with Trusted...
bachelor thesis 2022
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Florea, Vlad (author)
Software Defined Networking (SDN) is a new paradigm that allows for greater reliability and more efficient management compared to traditional networks. However, SDN security is a developing field, and research towards fixing significant security vulnerabilities is still ongoing. One major threat to SDN security are attacks that seek to exploit...
bachelor thesis 2022
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Swaminathan, Sudharshan (author)
Side-channel attacks (SCA) focus on vulnerabilities caused by insecure implementations and exploit them to deduce useful information about the data being processed or the data itself through leakages obtained from the device. There have been many studies exploiting these side-channel leakages, and most of the state-of-the-art attacks have been...
master thesis 2021
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Korpas Kamarianos, Alexandros (author)
Over the last decades, side-channel attacks (SCAs) have been proven as a substantial weakness of cryptographic devices, while the recent growth of deep learning (DL) dramatically improved the performance of SCA. More and more researches present ways to build lightweight deep neural network (DNN) models that can retrieve the secret encryption key...
master thesis 2021
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Wiemers, Marina (author)
With recent advances in performance and complexity, multi-party computation, a privacy-preserving technology which allows for joint processing of hidden input data, has lately been found to be applicable in a number of use cases. Despite existing implementations for secure data aggregation, substantial adoptions of the technology remain limited...
bachelor thesis 2021
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Latyšov, Sever (author)
Around the world millions of people get injured due to traffic accidents. Autonomous vehicles are expected to significantly reduce these numbers. To increase safety, autonomous vehicle communication can be used. Current vehicle communication networks called VANETs have security and privacy protection problems and the vehicle industry is...
bachelor thesis 2021
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Kolar, Brontë (author)
Social determinants such as a person’s race, level of education, and income can be responsible for their health outcomes. Consequently, we see that discrimination along the social spectrum results in health disparities. In an effort to close the gaps in healthcare systems, these determinants have been heavily researched. Open questions remain...
bachelor thesis 2021
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Maas, Wouter (author)
Globally, citizens’ assemblies have been gaining momentum as a way to counter dissatisfaction in democracies. Central to the citizens’ assembly is sortition, the process of randomly selecting political representatives given certain demographic criteria. In order to have an assembly representative of the population, personal data is necessary to...
bachelor thesis 2021
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El Coudi El Amrani, Nafie (author)
Reentrancy attacks target smart contracts of Decentralized Finance systems that contain coding errors caused by developers. This type of attacks caused, in the past 5 years, the loss of over 400 million USD. Several countermeasures were developed that use patterns to detect reentrancy attacks on smart contracts before deployment on the Ethereum...
bachelor thesis 2021
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Tatabitovska, Ana (author)
Front-running is the illegal practice of obtaining information unavailable to the general public with regards to upcoming transactions and performing actions based on this knowledge as to gain profit. This type of attack has been an issue since the introduction of the first stock market and it is not a surprise it has spread into blockchain...
bachelor thesis 2021
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Yüksel, Akif (author)
Kyber is a Decentralized Finance (DeFi) system which runs on the Ethereum blockchain. DeFi aims to remove centralized intermediaries such as Market Makers. An Automated Market Maker (AMM), implemented in a smart contract, is a decentralized version of these. Kyber's Dynamic Market Maker (DMM) is a next-generation AMM which solves two issues:...
bachelor thesis 2021
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Dekker, Hans (author)
Oracles are mechanisms that provide blockchain networks with data that only exists outside of the network, such as asset prices. Decentralized Finance (DeFi) protocols use this data, and therefore their usability depends on the reliability of oracles. One such oracle system, widely used by DeFi protocols for pricing feeds, is Chainlink. The...
bachelor thesis 2021
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Cirikka, Selim (author)
Protocols for Loanable Funds (PLFs) are lending protocols that exist in the decentralized finance (DeFi) ecosystem. They provide users the opportunity of lending and borrowing of cryptocurrencies. The economic model used to ensure liquidity in these protocols are variable parameters and incentives to reach an optimal equilibrium and...
bachelor thesis 2021
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