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Khattat, Mostafa (author)
Threshold signatures play a crucial role in the security of blockchain applications. An efficient threshold signature can be applied to enhance the security of wallets and transactions by enforcing multi-device-based authentication, as this requires adversaries to compromise more devices to recover the key. Additionally, threshold signatures can...
master thesis 2024
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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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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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Chen, Congwen (author)
Current backdoor attacks against federated learning (FL) strongly rely on universal triggers or semantic patterns, which can be easily detected and filtered by certain defense mechanisms such as norm clipping, comparing parameter divergences among local updates. In this work, we propose a new stealthy and robust backdoor attack with flexible...
master thesis 2023
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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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Thomas, Jeroen (author)
In a world where more data gets uploaded to the cloud, it is essential that the data gets stored securely. For users to keep search functionality, searchable symmetric encryption has been developed. SSE works by a user sending a token representing a keyword (or a range), after which the server returns the documents that match the keyword (or...
master thesis 2022
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Tian, Yuhang (author)
In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy. In particular, servers use automatic Density-based Spatial Clustering of Applications with Noise (DBSCAN) combined with S2PC to cluster the benign majority without acquiring...
master thesis 2022
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Lambregts, Steven (author)
Searchable Symmetric Encryption (SSE) schemes provide secure search over encrypted databases while allowing admitted information leakages. Generally, the leakages can be categorized into access, search, and volume pattern. In most existing Searchable Encryption (SE) schemes, these leakages are caused by practical designs but are considered an...
master thesis 2022
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Grishkov, Ilya (author)
This paper offers a prototype of a smart-contract-based encryption scheme meant to improve the security of user data being uploaded to the ledger. A new extension to the self-encryption scheme was introduced by integrating identity into the encryption process. Such integration allows to permanently preserve ownership of the original file and...
bachelor thesis 2022
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Gordijn, Daan (author)
Blockchain technologies allow users to securely store and trace their data on a fully decentralized system, and have the potential to make a huge impact on many industries. While traditional, permissionless blockchains such as Bitcoin, Ethereum, and Cardano are very popular, they are currently unable to provide trust and privacy on the network....
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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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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Deshamudre, Rohan (author)
Smart contracts allow for the collaboration and transaction processes between multiple parties/organisations to be automated and conducted in a neutral environment. In many situations these agreements are confidential and running a smart contract that contains private/sensitive information on a public blockchain network which is transparent and...
bachelor thesis 2022
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Nanhekhan, Kevin (author)
Smart contracts play an important role within the blockchain by ensuring that valid transactions are being recorded. However, there are critical concerns regarding the security and privacy of data within these blockchain applications. This research provides information on how the integration of the Trusted Platform Module can achieve more...
bachelor thesis 2022
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Starke, Zeddrich (author)
Block-chain technology is gaining momentum in both industry and academics. With<br/>this momentum there are a lot of potential gains, but also potential risk involved. This papers proposes a solution for security risks, like a man-in-the-middle-attack, of the permissioned block-chain distributed ledger software Hyperledger Fabric. A prototype is...
bachelor thesis 2022
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Chatterjee, Agniv (author)
Blockchain networks are increasingly recognized as a disruptive technology across sectors such as online services, finance, supply chain, administration etc. They are underpinned by smart contracts which provide programmatic instruction for the blockchain to operate. A major obstacle in the widespread adoption of blockchain technology is the...
bachelor thesis 2022
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Joosen, Floor (author)
Privacy and security are important topics in data aggregation, as companies are aggregating large amounts of sensitive data from their users. The aim of this paper is to improve the security and privacy of data aggregation using smart contracts, homomorphic encryption and post-quantum encryption methods on Hyperledger Fabric. After a literature...
bachelor thesis 2022
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Kahawati, Ali (author)
Blockchain networks have acquired ongoing notoriety among associations that need to utilise the security perspectives that blockchain gives. Hyperledger Fabric (HF) is one of the most widely utilised distributed network technologies, most ordinarily applied in situations that require private information to be maintained safely and secretly. Use...
bachelor thesis 2022
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