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.

Collaboration is a key technique in modern supply chains, both for building trust with other companies, but also for reducing costs or maximizing profits. It is an approach which provides all involved parties with benefits that they could not possibly achieve on their own. Collaboration, however, requires abundant information, including proprietary information which the owners might not want to disclose publicly. This leads to the main privacy concern, namely ensuring the privacy of proprietary information, since access to this information can mean a competitive advantage in the market. Several techniques which enable collaboration while preserving privacy have been developed over time, including secure transformation, which is a non-cryptographic approach. The main focus of this paper is studying this technique and its recent developments, along with the feasibility of using it to preserve privacy in supply chains, through a literature review. Secure transformation is still somewhat in its infancy, with much theoretical research being conducted, yet the technique still not being employed in practice. Therefore, reviewing the research done is the most suitable approach to answering the question of how secure transformation applications can preserve privacy in collaborative supply chains. The main result of this research is that secure transformation is a double-edged sword which promises effective computation for certain collaborative problems, with the downside of having weaker security guarantees compared to cryptographic approaches.

Blockchain is an expanding technologythat offers benefits when applied to supplychain management. This distributed ledgertechnology is combined with supply chainsfor its intrinsic characteristics. For instance,traceability, immutability, and more arediscussed. In this paper, we first presentprivacy-related challenges encountered whenapplying blockchain technology to this work.These include dealing with immutable data,providing the anonymity of end-users, as wellas their accountability. The risks associatedwith the leakage of medical data show thenecessity of protecting patient privacy. Forthis sake, we delve into blood donation,clinical trial, PPE tracking supply chainsand highlight their privacy requirements.We introduce anonymous signatures, mixingservices, and other cryptographic techniques,which satisfy these requirements throughlocal anonymity, unlikability, but alsoaccountability in specific contexts.

Supply chains are vital to the global economy, and so, increasing efficiency in supply chain management is of utmost importance. Modernizing technology has allowed for various uses of machine learning to be possible in several aspects of supply chains, specifically in demand forecasting with prediction models, and customer relations with chat-bots. While this may be the case, many organizations are reluctant to implement such solutions due to potential threats to their privacy. In addition to this, some currently existing solutions do not take special care for privacy preservation. This brings the question of, "How can privacy be preserved in machine learning based applications in supply chains?" The results of this survey show that several approaches for privacy-preservation of machine learning applications exist, and can be applied to supply chains while maintaining increased efficiency in supply chain management.

The following paper aims to investigate what and how are the main privacy-preserving methods applied in the blockchain-based supply chain industry scenario, with a primary focus on the food sector. Recent developments, such as the exordium of cryptocurrencies to increment efficiency, are withal addressed. Overviews of key use cases in industry and research are provided. Numerous blockchain projects, such as VeChain, or multiple others established on well-known infrastructures such as Ethereum or Hyperledger Fabric, are being developed, introducing cryptocurrencies to contribute to a future food supply chain network. Therefore, the motivation of this investigation is to discover how privacy is protected in the field of the food supply chain by the latest developments, such as cryptocurrencies. Do these techniques avail towards a more secure future in the field of the food supply chain from a privacy perspective, or do they introduce additional bottlenecks that could lead to incipient challenges? The principal results and conclusions reveal that proper privacy-preserving techniques exist and have a vigorous connection with the existing applications in the aliment supply chain sector. However, as the magnitude of data grows and the adoption of blockchain becomes very relevant in the supply chain, more focus needs to be attributed to the direction of how privacy is preserved and better methods need to be implemented as the field evolves. In addition, a privacy-preserving model proposal is presented, along with some guidelines and views on the future of this field.

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. Collaborative optimization algorithms can be applied to problems in the supply chain, and secure multiparty computation is incorporated as part of the algorithm to preserve the privacy of the parties. This paper aims to create an overview of privacy-preserving applications in the collaborative supply chain by conducting a literary study that focuses on secure collaborative optimization research and its limitations.

Research findings showed that secure multiparty computation can be applied to the following supply chain collaboration problems: capacity sharing, price-masking, distributed scheduling, collaborative production and transport, vehicle routing, and resource allocation. These algorithms use multiparty computation that is secure under the semi-honest adversarial model, because a malicious model is generally too inefficient for practical use. This choice comes with a cost in privacy, as the semi-honest model assumes parties collaborating will not break the protocol. This is a weak assumption that results in an impractical protocol, as real life applications of semi-honest multiparty computation would not be protected against a party that benefits from cheating. Furthermore, secure multiparty computation has a limitation that it cannot prevent a party from lying in its private input.

This paper recommends for future secure collaborative optimization research to combine multiparty computation with game theory. Achieving incentive compatibility in a protocol proves that it is in the best interest for a party to not cheat, as it either leads to a loss in benefits or they are caught. This allows for the MPC protocol to keep its efficiency by being secure under just a semi-honest adversarial model, as well as offer greater protection for honest parties from a rational malicious party.

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 Bank, billions of Euros are lost due to credit card fraud every year. Since verifying all transactions by hand is infeasible, automated Fraud Detection Systems (FDSs) are needed. Currently, financial institutions create such systems by training machine learning algorithms on transaction data. However, the performance of these systems is obstructed due to a lack of positive (fraud) samples in the collected transaction data. To improve performance, an ideal solution would be to merge data of all institutions and to train an FDS on the resulting data set. However, privacy reasons concerning the sensitive customer information in this data, and security risks associated with transferring data, render this solution unrealistic. Therefore, the need rises for novel protocols that allow financial institutions to collaboratively train FDSs without sharing private data. Previous research in the field of collaborative learning attempts to solve such problems by requiring participants to train local models, which are aggregated into a global model by a trusted central entity. Unfortunately, the vulnerability of these settings to inference attacks restricts their applicability. Inference attacks aim to extract additional secret knowledge from a model. These are especially powerful when performed by participants in a sequential setting, where participants train the same model one after the other following a given order. This is because in this setting participants have white-box access to the model itself and to the data used to train it. Naturally, these attacks are considered a breach of privacy and hinder collaboration. In this work, we propose a novel protocol leveraging secure multi-party computation techniques to prevent inference attacks in a sequential setting. To achieve this, we require participants to jointly determine a training order. While doing so, we ensure participants only receive information on whom to send their data to. This means participants are unaware of whose data they are receiving. With this work, we contribute a practical protocol that is robust against inference and timing attacks to facilitate privacy-preserving sequential collaborative learning. To the best of our knowledge, our work is the first to prevent inference attacks using a secure multi-party selection protocol with overhead of only a few seconds.