Abstract. Multiparty computation can be used for privacy-friendly outsourcing of computations on private inputs of multiple parties. A computation is outsourced to several computation parties; if not too many are corrupted (e.g., no more than half), then they cannot determine the inputs or produce an incorrect output. However, in many cases, these guarantees are not enough: we need correctness even if all computation parties may be corrupted; and we need that correctness can be verified even by parties that did not participate in the computation. Protocols satisfying these additional properties are called "universally verifiable". In this paper, we propose a new security model for universally verifiable multiparty computation, and we present a practical construction, based on a threshold homomorphic cryptosystem. We also develop a multiparty protocol for jointly producing non-interactive zero-knowledge proofs, which may be of independent interest.

Abstract. In recent years, a number of infrastructures have been proposed for the collection and distribution of medical data for research purposes. The design of such infrastructures is challenging: on the one hand, they should link patient data collected from different hospitals; on the other hand, they can only use anonymised data because of privacy regulations. In addition, they should allow data depseudonymisation in case research results provide information relevant for patients' health. The privacy analysis of such infrastructures can be seen as a problem of data minimisation. In this work, we introduce coalition graphs, a graphical representation of knowledge of personal information to study data minimisation. We show how this representation allows identification of privacy issues in existing infrastructures. To validate our approach, we use coalition graphs to formally analyse data minimisation in two (de)-pseudonymisation infrastructures proposed by the Parelsnoer initiative.

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