A substantial number of passengers in Europe rely on trains for transportation, facilitated by a network of high-speed international trains. However, the coordination of train schedules across multiple networks often poses challenges due to incompatible timings. The scheduling of multiple train networks shares similarities with multi-processor task scheduling and airline scheduling but is distinguished by its cooperative nature rather than a competitive one. Cooperative scheduling necessitates the sharing of private information. This information, 'demand', is commercial sensitive information, since it can reveal demographic information like incomes and tax returns. Privacy-preserving protocols can enable the computation of statistics without revealing this demands (in railway systems) to unauthorized parties. Despite the critical role of privacy in multi-party scheduling, research in this domain remains limited due to domain specific constraints. A model supporting such privacy considerations could significantly help Europe achieve its carbon-neutral goals while improving cross-border services. In this research, we propose a system designed to facilitate joint service scheduling, ensuring confidentiality, integrity, and authenticity. We use partial and fully homomorphic encryption techniques that mimic the outcomes achievable with a trusted third party. We conduct a comparative analysis of online and offline approaches, emphasizing how they achieve confidentiality, collusion-resistance, traceability and non-repudiation. Theoretical and experimental evaluations demonstrate the feasibility of the system for real-world applications by creating schedules for upto four parties. Our solution for scheduling seven slots takes approximately three hours, which is a feasible duration to solve a problem of this size.

Within the field of \emph{cyber threat intelligence} (CTI), healthcare institutions are one of the most targeted organizations by cybercriminals. To mitigate future attacks on their digital infrastructures, healthcare institutions can collaborate and exchange security logs. These logs include data such as IP addresses, malware hashes, and other indicators of compromise. By identifying shared elements across different datasets, threats that are harmful to a greater number of organizations---and thus, pose a more significant risk---can be highlighted to detect common attack patterns. These attack patterns could then provide insight into understanding how cyber criminals operate on a larger scale. However, disclosing locally collected CTI could compromise a hospital's security posture and reputation since it reveals vulnerabilities or attack techniques used by hackers. Furthermore, this cyber threat data is often sensitive. That is why the threat data needs to be shared in a privacy-preserving manner. \emph{Multiparty private set intersection} (MPSI) is a solution that allows parties to find the intersection of all their sets without learning anything of the other inputs. Although, in many cases, the condition that an element be present in all sets is too restrictive. A potential threat is still worth investigating even if it only appears in a portion of sets. Therefore, we focus on \emph{threshold multiparty private set intersection} (T-MPSI). However, not all T-MPSI schemes perform equally depending on the context. Our goal is to determine what makes a T-MPSI protocol effective for application in medical CTI sharing. To do so, we analyze four state-of-the-art T-MPSI protocols in terms of security, theoretical communication and computational complexities, and practical runtime performance.

Financial crime has seen a surge in complexity over the past decades as a result of digitisation. This required better tools for detecting financial crime, creating a cat-and-mouse game between law enforcement and criminals. Anti-money laundering (AML) is the collective term describing these tools, as well as financial regulations designed to combat money laundering. The detection of fraud is done through data analysis. Typically, the financial data is a large set of transactions between accounts. Transactions have a sender, receiver, and an amount that is transferred from sender to receiver. This can be represented by a directed graph where nodes represent accounts and edges transactions between accounts. Methods for finding fraudulent transactions in transactional graphs are widely studied in both literature and industry. This usually assumes a centralised entity with full access to the data. At an international scale, the centralised approach is not suitable. Besides its massive scale posing a challenge, it is hard to imagine a global agreement on what capabilities such an entity should have. At the same time, organisations sharing financial data for analysis raises privacy concerns. We limit our scope to transactional cycles. These typically occur when criminals send money through a series of international bank accounts back to the original account. This lowers the chance of the money being traced back to its illicit source. We propose a privacy-preserving decentralised protocol which takes as input a parameter $\ell$ from any node and outputs all cycles of at most length $\ell$ containing the node. The protocol relies on the decisional Diffie-Hellman assumption, and we show the protocol is secure against a polynomially bounded adversary within our security model. The communication complexity of an instance is $O(n^{\ell})$, multiple instances can be ran in parallel. We implement the protocol and measure its performance on scale free graphs in terms of communication and computational costs. Lastly, we discuss our results and suggestions for future work.

Ensuring the privacy of medical data in a meaningful manner is a complex task. This domain presents a plethora of unique challenges: high stakes, vast differences between possible use cases, long-established methods that limit the number of feasible solutions, and more. Consequently, an effective approach to ensuring the privacy of medical data must be easy to adopt, offer robust privacy guarantees, and minimize the reduction in data utility.

The unique nature of medical data presents distinct challenges and also opportunities. We consider various types of correlations that significantly impact privacy guarantees. However, these correlations can also be used to train a model for removing anomalies and subsequently enhancing the utility of synthetic medical data.

This thesis proposes a framework compatible with state-of-the-art approaches for differentially private dataset release based on the usage of Generative Adversarial Networks (GANs). Our framework uses a part of the privacy budget to train an unsupervised learning model to detect and remove anomalies. We evaluate the performance of the framework using a variety of machine-learning models and metrics. The final results show an improvement of up 13% compared to approaches not using our framework, under the same privacy budget.

Digital modelling is becoming more prevalent in many applications. The underlying 3D mesh objects are therefore getting increasingly valuable, such that methods for ownership protection are required. Watermarking is a solution to this problem, yet the research combining watermarking and 3D mesh objects is limited. This work aims to improve an existing watermarking method that embeds watermarking bits into a 3D model by selecting features, or the vertices with a larger mean curvature. We propose an improvement where a centre of mass calculation is changed to a centre of volume, aiming to achieve better robustness against mesh simplification attacks on 3D mesh objects by use of this volume centre instead of a mass centre. The proposed method of applying the watermark reaches an accuracy score that is 15.78 percentage points higher on average than the original method, when attacked by mesh simplification. Therefore, this work shows a more robust approach to the method build upon.

In the digital era, XML data is fundamental for various applications, requiring robust methods to ensure data integrity and security. Traditional digital watermarking techniques face challenges due to XML's hierarchical structure. Zero-watermarking, which derives a watermark from inherent data properties without altering the data, offers a promising alternative. This paper enhances zero-watermarking robustness for XML by integrating usability queries to filter functional dependencies (FDs) based on their relevance. This approach maintains watermark integrity even when some data elements are modified, provided the data's usability is preserved. The proposed method increases resilience against targeted attacks altering semantic information while preserving usability. Key contributions include incorporating usability queries into the FD-based zero-watermarking process, generating cover ranges for usability queries, and empirically testing the method's robustness.

Data security has become more important over the last few years as data sharing over the world has become trivial. Data ownership therefore becomes critical as data can be very valuable and vulnerable to theft. Watermarking is a technique that can help data owners prove ownership over their data. In this paper, an approach is presented to watermark data that is gathered over time, such as weather data. With this research, we propose an adaptation of an existing audio watermarking technique developed in [1]. The adapted algorithm embeds a bit stream into a non-medical time series dataset by calculating the Discrete Wavelet Transform coefficients and modifying their magnitudes. The algorithm shows good robustness against a small range of data modification attacks but lacks capability in larger
scaled attacks. In addition, the proposed algorithm does require additional research to use it in a professional setting.

The advancement of Machine Learning (ML) in the last decade has created new business prospects for developers working on ML models. Models that are expensive and time-consuming to design and train can now be outsourced from others to reduce costs using Machine Learning as a service (MLaaS). \textbf{Deep Neural Networks (DNNs)} are particularly expensive to train, therefore many who need a DNN utilize the services of an MLaaS provider. This creates the \textbf{possibility of piracy} of this valuable asset, and the need to prevent piracy to assure a fair market. To address this need, research has been conducted on protecting DNNs using various watermarking techniques. A work by \textit{Li et al.} has proposed null-embedding, a technique that renders the DNN useless if it is subject to a piracy attack. Despite being effective, this method was shown to reduce classification performance when embedding a watermark into the model. This paper suggests modifications to the null-embedding technique that reduce this impact and keep the classification accuracy close to that of a non-watermarked model.

AI and machine learning have been topics of big interest in the last couple of years, with plenty of applications in many domains. To train these models into useful and desirable tools, a large amount of data is necessary. This data is expensive to collect, becoming one of the most valuable commodities of this century. As the value of data increases, protecting this intellectual property becomes more and more relevant. Watermarking is a technique widely used for data protection in media, but the non-media counterpart has not been researched as thoroughly. In this paper, an adaptation of a common watermarking technique, DWT watermarking, is applied on two datasets used for machine learning. This technique is invisible and robust in signal watermarking, but its performance on a numerical dataset has not been previously researched. A previously devised algorithm was used, but it was adjusted to better fit dataset watermarking. To assess the quality of the watermark, the marked data has been subjected to create, remove, update and zero-out attacks. On top of this, multiple machine-learning models have been trained on the marked data. Initial results show that the proposed technique performs well in terms of invisibility, obtaining similar or better accuracies than models trained on the original data, but it is quite sensitive to attacks. Even small modifications, less than 1\% of the data, can break the signature.

Federated Learning (FL) is a decentralized machine learning approach that provides a privacy-friendly way of training models by keeping the datasets of participating parties private. Some challenges FL faces are the lack of incentives to encourage participation in the learning process, as well as preventing potential cyber attacks that tamper with the model. Blockchain is an available solution that provides the means to implement incentives to encourage participation and issue penalties to disincentivize malicious behavior. Hence, recent developments introduced blockchain-enabled FL (BCFL) designs for various applications. However, one obstacle that slows down the widespread adoption of this technology is the high latency of blockchain networks due to its laborious consensus protocols. In this paper, we propose StateFL, a revised BCFL architecture that uses state channels (a blockchain scaling solution) in order to ease the load on the blockchain by reducing the number of on-chain transactions, improving the system’s latency, and minimizing transaction fees as a result. State channels are governed by smart contracts and enable two parties to exchange information and assets off-chain unless disputes occur. Either channel party can dispute the state of the channel if suspicious behavior is observed. In that case, the dispute is settled on the blockchain. We evaluate StateFL in a series of experiments to establish latency improvements, identify bottlenecks, and quantify the impact of disputes on channel latency and transaction fees. The findings show that the higher the number of FL rounds, the more StateFL outperforms the baseline BCFL, with the exception of a very low number of rounds. In realistic FL scenarios, the rounds are in the order of hundreds making StateFL a solid contender even if disputes do occasionally occur. The bottleneck of StateFL is the channel setup and closure which require extensive interaction with the blockchain.

In modern society, data is increasingly important for people's daily lives and commercial activities to benefit organization, management and analysis. Proper data utilization involves different stages, and the data life cycle is introduced to describe the procedures as generation, collection, processing, storage, management, analysis, visualization and interpretation. Along the data life cycle, data privacy is highly concerned since possible breaches can lead to the abuse of personal information and financial loss. In this thesis, we advance data privacy protection in data processing, data management, and data analysis correlated with the Spark Living Lab project in the domain of supply chains and machine learning. To enhance privacy protection, we propose solutions based on differential privacy and cryptographic protocols since they provide strong and provable security and privacy guarantees. Moreover, we integrate differential privacy and cryptographic protocols to achieve strong privacy guarantees efficiently and practically for data processing, management, and analysis.

In data processing, we focus on data anonymization and location data perturbation in supply chains. Data de-identification is essential to comply with privacy laws, such as GDPR, before possible sharing or analysis. We propose an anonymization algorithm by combining differential privacy and k-anonymity to achieve stronger privacy guarantees or better data utility than using them alone. Meanwhile, we consider trajectory hiding under possible attacks and real maps, which propose a more practical solution to share trajectory data under privacy protection.

In data management, we address secure data sharing with cryptographic protocols. Data sharing is vital in data management to advance collaboration and knowledge. However, possible data breaches and malicious inputs can lead to potential financial loss and identity theft. In this thesis, we propose a framework for sharing logistic data in a privacy-preserving way using blockchain and cryptographic protocols. Differential privacy is applied to anonymize data, while cryptographic protocols enhance privacy during data transmission.

In data analysis, we pay attention to privacy-preserving machine learning. Machine learning models are usually trained on large datasets which may contain sensitive personal information. It is important to consider privacy protection during the training and utilization of models. We use differential privacy and secure multi-party computation techniques to design a framework for collaborative learning among multiple parties against inference attacks. Also, we utilize zero-knowledge proof to validate model integrity without leaking the model.@en

Recent developments in the capability and availability of small internet of things devices has meant that networked medical devices, like networked implants and wearable monitors, have become more widespread. This data is invaluable for solving pressing global healthcare concerns, like eectively monitoring and treating heart patients. The European Union has announced plans to create an international collaborative network for sharing medical data. However, such a system will have to overcome some major unsolved issues regarding security and privacy. Citizens surveys have stressed the im-portance of privacy protection and transparency in recipients. Governments have appointed administrative bodies tasked with supervising the processing of personal data, or assuring healthcare quality. However, medical health-care providers have signalled concern with unrestricted governmental access to patient data. In this thesis, we propose a system for auditable medical data sharing compatible with privacy-preserving technologies. We demon-strate a method to securely generate encryption keys which are recoverable using an audit key.
We combine this with distributed key generation to cre-ate a board of trusted members, with each a share of the audit key. Board members can work together to collaboratively audit communication between healthcare providers and medical researchers. We demonstrate that the key generation is secure and ecient. We show that auditability is guaranteed under the assumptions that at least one of the communicating parties is hon-est. Our system bridges the gap between privacy-preserving medical data analysis and governing capabilities by assuring auditability without handing this power over to a single party. In real world scenarios, this system can be used to create international level of data sharing, as is explored for the European Health Data Space. The data inspection can be combined with already existing legislative power to detect fraudulent behavior and perform physical audits when required. The system can be extended to facilitate reproducible medical research.

Organizations use cloud service providers for outsourcing their data, since this includes advantages such as: scalability, security and no need for in house experts. Therefore, outsourcing data to cloud providers results in reduced costs.
The disadvantage of outsourcing data to a cloud provider, is that organizations are not in control of their own data.
When organizations are not in control of their data, they are subject to privacy risks.
Privacy risks should be avoided, especially when sensitive data such as medical or financial records are involved.
Therefore, organizations protect their data by only outsourcing encrypted data to cloud providers.
However, data analysis on encrypted data is significantly reduced due to computational and communicational overhead.

A commonly used data analysis method, such as k-Nearest Neighbour Search (k-
NNS), is useful when for finding similar records in a database for a given query.
Previous research shows success using k-NNS methods while preserving privacy, by using fully homomorphic encryption.
However, previous solutions required the client to be online and help in the protocol, or make use of non-colluding servers.

Therefore, we introduce our k-NNS protocol, which outsources all the work to the cloud server and the client is not involved in the computation.
Our k-NNS protocol shows success on data sets used to test k-NNS applications, however is significantly slower than solutions which involve the client or non-colluding servers.

Cloud computing and storage solutions have been credited with increasing competitiveness through cost savings, greater flexibility, elasticity and optimal resource allocation. However, the data outsourced to the cloud may contain private information that must be protected from misuse. In such cases, the data can only be outsourced after encryption. Graphs are widely used to model and represent data in various applications, including geographic information systems. Yet performing the shortest path algorithm over such encrypted outsourced graphs represents a challenge. Current approaches rely on trusted third parties, employ weaker security measures, or treat the cloud as a storage medium rather than addressing the algorithmic complexities directly. This makes the adoption of such protocols difficult in practice.

In this work, we develop two privacy-preserving navigation protocols to compute the shortest path over encrypted outsourced graphs, under different security and efficiency guarantees. The first protocol enables one to retrieve the shortest path in an oblivious manner, that is, without the cloud learning any information about the outsourced graph or the returned path. The second protocol assumes that the topology of the outsourced graph is public knowledge, and obtains retrieval times orders of magnitude faster. The evaluation results on a proof-of-concept implementation indicate that the condition number of the node-incidence matrix of the graph being outsourced serves as a determining factor for the number of iterations. Dense graphs exhibit a low condition number. In such cases, the high number of iterations required for convergence, quadratic time complexity with respect to the number of edges in the graph, and the inherently slow homomorphic operations results in impractical retrieval times. Conversely, sparse graphs allow for trivial resolution of shortest paths within a single iteration.

The media watermarking technique domain has had the last 30 years to develop itself. The non-media side, however, is a way newer sub-domain. [1] The data-gathering process for machine learning algorithms is a tedious and time consuming task. This becomes worse as the scale of these algorithms increases. Thus, protecting the datasets against illegal use or sale and proving they are intellectual property is useful. In this paper, we answer the question: How can image watermarking techniques be applied to classification algorithm datasets, without degrading the dataset's quality? Algorithms that use the Singular Value Decomposition (SVD) of the data are often the basis of other matrix decomposition based Image watermarking techniques. Thus if an SVD based algorithm can be applied to a machine learning dataset then the other matrix decomposition based algorithms can also be applied. This implies that a large part of the much older media targeted watermarking techniques can be applied to the non-media datasets. In this paper we apply the watermarking technique described in [2] to a machine learning dataset. This watermark provides decent imperceptibility and robustness against update, zero-out and insertion attacks but it's held back by its lackluster robustness against deletion attacks. That said, we proved that when an image watermark is found that is impervious against deletion attacks, it can be applied to the machine learning datasets.

Although digital watermarking has been a well-researched topic for the past decades and has seen numerous implementations for relational databases, it still lacks research for non-relational schema-less databases. In this paper, we explore proposed techniques for non-relational database watermarking and introduce an improved technique for NoSQL database watermarking. The improved technique produces pseudo documents, embeds a watermark in them, and inserts them into the database. Then it modifies the relations between the pseudo and real documents to indicate which documents are covered by the watermark. Lastly, the pseudo documents act as a cover for the watermark embedding, proving the authenticity of the documents connected. We tested the technique against numerous modification and deletion attacks. The results show that the introduced technique does not improve the robustness of the watermark. Although the technique has the potential to produce a robust watermark, it still needs to be improved before being used in a commercial setting.

Digital watermarking has been used extensively in media in recent years. Yet, there are still relatively few techniques for watermarking 3D meshes. In this paper we implement a watermarking algorithm proposed by O. Benedens that encodes a bit string in the distribution of the normals of the faces of the mesh and investigate a method to improve the robustness of 3D mesh watermarking algorithms against connectivity attacks by introducing a mesh regularization step at the watermark insertion and extraction phases. Furthermore, O. Benedens' watermarking technique is augmented to embed bit strings of any length and we suggest a method to measure the similarity of two bit strings in terms of probabilities.

Money laundering is the process of hiding the origin of funds obtained through illicit activities. It is a major problem that has significant impacts on the global financial system and undermines the integrity of financial institutions. To combat this, the Dutch government planning to make it easier for banks to share data to improve the detection of money laundering. However, this approach raises concerns about privacy, as it would allow banks to share sensitive financial information with other banks and institutions. A way to allow banks to still detect money laundering using other banks' data, but without having to share the data would be through the use of multi-party computation. In this work we propose a privacy preserving distributed cycle detection protocol which is meant to find short cycles in financial transactions to help detect money laundering without compromising the privacy of the customers at the bank. Finally, we show that our protocol is significantly faster at detecting short cycles in large financial graphs than current state-of-the-art multi-party computation protocols.

Payment channel networks (PCNs) are a promising solution to the blockchain scalability problem. They move payments off-chain, i.e., not all payments have to be included in the blockchain. Thus, they do not require that every payment is broadcast to all participants and verified by them. Not requiring global consensus reduces latency, computation, and hence energy consumption.

In PCNs, a payer can route a multi-hop payment to a payee via intermediaries. Yet, Lightning, the only prominent payment channel network, has two major issues when it comes to multi-hop payments. First, the payer decides on the path without being able to take local capacity restrictions into account. Second, due to the atomicity of payments, any failure in the path causes a failure of the complete payment. While there are suggested solutions for each of these problems --- namely splitting payments, local routing, and adding redundancy ---, there is no solution that solves both of them.

In this work, we propose JustForward: The payer adds redundancy to the payment by initially committing to sending a higher amount than the actual payment value $v$. Intermediaries decide on how to forward a received payment, potentially splitting it between multiple paths. If they cannot (or do not want to) forward the complete payment value, they may reduce the amount they forward. If paths for at least $v$ coins are found, the receiver and source jointly select the amounts of sub-payments that make up the full payment. Payment commitments are updated accordingly and fulfilled. In order to guarantee atomicity and correctness of the payment value, we use a modified Hashed Time Lock Contract (HTLC) for paying that requires both the payer and the payee to provide a secret pre-image. JustForward furthermore is the first local routing protocol to include fees: The source adds a maximal amount of fees they are willing to pay to the payment. Intermediaries take fees as they see fit, taking into consideration that subsequent nodes on the path will not forward the payment if there are insufficient fees left.

We prove that the proposed protocol achieves balance security, atomicity, bounded loss for the sender, and optionally unlinkability of split sub-payments of the same overall payments. Furthermore, our evaluation on both synthetic and real-world Lightning topologies shows JustForward outperforms existing algorithms in terms of the fraction of successful payments as long as the degree of redundancy is not too high. Concretely, for a moderate amount of redundancy, JustForward increases the success ratio of payments by about 10%.

Healthcare recommender systems emerged to help patients make better decisions for their health, leveraging the vast amount of data and patient experience. One type of this system focuses on recommending the most appropriate physician based on previous patient feedback in the form of ratings. Such advice can be challenging to generate for new patients as their interaction with the new environment is too limited to inform meaningful recommendations. Their preferences from the previous system can be transferred to the new system to help generate recommendations in a cross-domain approach.
Cross-domain recommender systems aim to bridge the gap between domains where such interactions are plentiful and domains with relatively little interactions for the new users, using transfer learning techniques. However, while privacy concerns in single-domain recommender systems have been studied extensively, the same does not apply to cross-domain systems where multiple domains collaborate. Privacy is essential in settings such as healthcare, where rating information is sensitive and prone to leaking patient medical history to third parties. Existing works lack an efficient design that provides sufficient privacy while generating recommendations.
This thesis presents a privacy-preserving cross-domain recommender system with partial user overlap. We first propose a privacy-preserving matrix factorization approach with differential privacy and multiparty setting to serve as the base of our cross-domain design. Our matrix factorization design with privacy is easy to adopt by existing systems, incurring negligible overhead, and requires no modification to existing factorization implementations. Next, we show the privacy-accuracy trade-off in our base method with experiments over the MovieLens dataset. Finally, in the cross-domain setup, we demonstrate that adding privacy to the proposed scheme has a minimal predictive performance loss, essentially providing privacy for free.