Searched for: subject%3A%22multi%255C-party%255C+computation%22
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Maćkowiak, Pawel (author)
The inability to check how our Internet traffic is being handled and routed poses all kinds of security and privacy risks. Yet, for the typical end-user, the Internet indeed is such a black box. This thesis, adheres to the call for an Internet that is more transparent, and as a step forward proposes a mechanism that carefully balances the desire...
master thesis 2023
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Agahari, W. (author)
Data sharing through data marketplaces, which rely on a Trusted Third Party (TTP), can benefit businesses and society. However, many companies and consumers are increasingly reluctant to share data due to mounting concerns over data control and privacy. Emerging privacy-enhancing technologies (PETs) like Multi-Party Computation (MPC), which...
doctoral thesis 2023
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Mălan, Abel (author)
Effective large-scale process optimization in manufacturing industries requires close cooperation between different parties of human experts who encode their knowledge of related domains as Bayesian network models. For example, parties in the steel industry must collaboratively use their Bayesian networks on process parameters at the maker,...
master thesis 2023
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Shankar, Aditya (author)
Vertical federated learning’s (VFL) immense potential for time series forecasting in industrial applications such as predictive maintenance and machine control remains untapped. Critical challenges to be addressed in the manufacturing industry include small and noisy datasets, model explainability, and stringent privacy requirements for training...
master thesis 2023
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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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Agahari, W. (author), de Reuver, Mark (author)
Consumers are increasingly reluctant to share their personal data with businesses due to mounting concerns over privacy and control. Emerging privacy-enhancing technologies like multi-party computation (MPC), which allows generating insights while consumers retain data control, are challenging the current understanding of why consumers share...
conference paper 2022
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Agahari, W. (author), Ofe, H.A. (author), de Reuver, Mark (author)
Firms are often reluctant to share data because of mistrust, concerns over control, and other risks. Multi-party computation (MPC) is a new technique to compute meaningful insights without having to transfer data. This paper investigates if MPC affects known antecedents for data sharing decisions: control, trust, and risks. Through 23...
journal article 2022
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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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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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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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Romanov, Danila (author)
Despite evidence that collaborating in the supply chain can reduce inefficiency and result in mutual gain, parties do not wish to collaborate if they have to share their private proprietary information. The main reason for their privacy concern is that the party does not want to lose their competitive advantage by giving away company secrets....
bachelor thesis 2021
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van Tetering, Daphne (author)
The convenient service offered by credit cards and the technological advances in e-commerce have caused the number of online payment transactions to increase daily. With this rising number, the opportunity for fraudsters to obtain cardholder details via online credit card fraud has also increased. As a result, according to the European Central...
master thesis 2021
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Vos, Jelle (author)
In our increasingly digital society, we are making a growing amount of data available to computers, networks and third parties. As a consequence, our sensitive data is in danger of getting exposed. The field of multi-party computation attempts to mitigate this by studying protocols that enable parties to perform their operations digitally,...
master thesis 2021
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Ugwuoke, C.I. (author)
The genome is the blueprint of life and has a detailed genotype and phenotype description of any organism. This in itself attributes sensitivity to genetic data, be it in the biological or electronic format. The possibility of sequencing the genome has opened doors to further probing of the data in its electronic form. Post sequencing of the...
doctoral thesis 2021
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van der Ende, Dieuwke (author)
Decision-tree evaluation is a widely-used classification approach known for its simplicity and effectiveness. Decision-tree models are shown to be helpful in classifying instances of fraud, malware, or diseases and can be used to make dynamic, flexible access decisions within an access-control system. These applications often require sensitive...
master thesis 2021
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Agahari, W. (author), Dolci, R. (author), de Reuver, Mark (author)
Privacy-preserving technologies could allow data marketplaces to deliver technical assurances to companies on data privacy and control. However, how such technologies change the business model of data marketplaces is not fully understood. This paper aims to bridge this gap by focusing on multi-party computation (MPC) as a cryptographic...
conference paper 2021
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Lam, Julia (author)
EU banks have a dual obligation to generate and protect assets as a due diligence for their customers, their business longevity, and in accordance with regulatory bodies. The effective use of data presents opportunities for banks to meet their obligations in improving financial risk and also ensuring business growth and continuity in their...
master thesis 2020
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Dolci, Riccardo (author)
Practical problem: In today's digitally transformed and connected world, data has become a critical strategic corporate resource. In this context, data marketplaces are becoming more popular since they enable wider accessibility and more efficient interaction among companies. Despite this, there are several barriers in sharing data through this...
master thesis 2020
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Kumar, Jeevan (author)
The emergence of the Data Marketplaces is the latest iteration in the phenomenon of data-driven transformation of the world. Data marketplaces have emerged as a new form of data-driven business models which enable trading of data between the data owners/providers and data consumers by providing the necessary technological and non-technological...
master thesis 2019
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Faujdar, Vidyottama (author)
Research ProblemIn today’s competitive and fast-paced nature of conducting business in the semiconductor industry, the discipline of revenue management (RM) is often mentioned. Through a dynamic pricing capability, RM enables firms to maximize profits by capitalizing on missed revenues. Moreover, RM enables customers to receive products on short...
master thesis 2019
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