Multiparty private set intersection enables multiple parties to determine the intersection of their private sets without disclosing the actual content. It is pivotal for collaboration in cyber threat intelligence as it allows organizations to share compromising or sensitive data in a privacy-preserving manner. This data includes infected IP addresses, malware hashes and other indicators of compromise. Then, the organizations identify elements that overlap across all datasets and take action to mitigate the threat with the broadest impact. Although, in many cases, the condition that an element be present in all sets is too stringent. Therefore, in this work, we focus on threshold multiparty private set intersection (T-MPSI), a protocol that identifies elements present in a subgroup of the total sets instead of in all sets. We highlight the differences between three different perspectives when computing the threshold intersection: individual—only the party leader learns the elements from their set that meet the threshold, all—all parties learn the elements from their set that meet the threshold, and collective—all parties jointly learn all elements that are present in the threshold, regardless of whether they possess those elements themselves. While many implementations for T-MPSI_individual and T-MPSIall have been proposed, to the best of our knowledge, no implementation for T-MPSI_collective exists. Therefore, we present a generic composition that extends any T-MPSI_individual protocol into a TMPSI_collective protocol. Our extension employs a multiparty private set union to aggregate outputs efficiently. We then provide a comprehensive analysis and runtime evaluation, demonstrating the feasibility of the extension.

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.